Smaws_Client_SageMaker.Typestype nonrec cognito_member_definition = {client_id : client_id;An identifier for an application client. You must create the app client ID using Amazon Cognito.
*)user_group : cognito_user_group;An identifier for a user group.
*)user_pool : cognito_user_pool;An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
*)}Identifies a Amazon Cognito user group. A user group can be used in on or more work teams.
type nonrec groups = group listtype nonrec oidc_member_definition = {groups : groups option;A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
*)}A list of user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
type nonrec member_definition = {oidc_member_definition : oidc_member_definition option;A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
cognito_member_definition : cognito_member_definition option;The Amazon Cognito user group that is part of the work team.
*)}Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team.
type nonrec member_definitions = member_definition listtype nonrec product_listings = string_ listtype nonrec notification_configuration = {notification_topic_arn : notification_topic_arn option;The ARN for the Amazon SNS topic to which notifications should be published.
*)}Configures Amazon SNS notifications of available or expiring work items for work teams.
type nonrec iam_policy_constraints = {vpc_source_ip : enabled_or_disabled option;When VpcSourceIp is Enabled the worker's IP address when a task is rendered in private worker portal inside the VPC is added to the IAM policy as a Condition used to generate the Amazon S3 presigned URL. To render the task successfully Amazon S3 checks that the presigned URL is being accessed over an Amazon S3 VPC Endpoint, and that the worker's IP address matches the IP address in the IAM policy. To learn more about configuring private worker portal, see Use Amazon VPC mode from a private worker portal.
source_ip : enabled_or_disabled option;When SourceIp is Enabled the worker's IP address when a task is rendered in the worker portal is added to the IAM policy as a Condition used to generate the Amazon S3 presigned URL. This IP address is checked by Amazon S3 and must match in order for the Amazon S3 resource to be rendered in the worker portal.
}Use this parameter to specify a supported global condition key that is added to the IAM policy.
type nonrec s3_presign = {iam_policy_constraints : iam_policy_constraints option;Use this parameter to specify the allowed request source. Possible sources are either SourceIp or VpcSourceIp.
}This object defines the access restrictions to Amazon S3 resources that are included in custom worker task templates using the Liquid filter, grant_read_access.
To learn more about how custom templates are created, see Create custom worker task templates.
type nonrec worker_access_configuration = {s3_presign : s3_presign option;Defines any Amazon S3 resource constraints.
*)}Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.
type nonrec workteam = {worker_access_configuration : worker_access_configuration option;Describes any access constraints that have been defined for Amazon S3 resources.
*)notification_configuration : notification_configuration option;Configures SNS notifications of available or expiring work items for work teams.
*)last_updated_date : timestamp option;The date and time that the work team was last updated (timestamp).
*)create_date : timestamp option;The date and time that the work team was created (timestamp).
*)sub_domain : string_ option;The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.
*)description : string200;A description of the work team.
*)product_listing_ids : product_listings option;The Amazon Marketplace identifier for a vendor's work team.
*)workforce_arn : workforce_arn option;The Amazon Resource Name (ARN) of the workforce.
*)workteam_arn : workteam_arn;The Amazon Resource Name (ARN) that identifies the work team.
*)member_definitions : member_definitions;A list of MemberDefinition objects that contains objects that identify the workers that make up the work team.
Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition. For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition.
workteam_name : workteam_name;The name of the work team.
*)}Provides details about a labeling work team.
type nonrec workteams = workteam listtype nonrec workspace_settings = {s3_kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
*)s3_artifact_path : s3_uri option;The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
*)}The workspace settings for the SageMaker Canvas application.
type nonrec workload_spec = | Inline of string_An inline YAML or JSON string that defines benchmark parameters.
*)The workload specification for benchmark tool configuration. Provide an inline YAML or JSON string.
type nonrec cidrs = cidr listtype nonrec source_ip_config = {cidrs : cidrs;A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
The following Length Constraints apply to individual CIDR values in the CIDR value list.
*)}A list of IP address ranges (CIDRs). Used to create an allow list of IP addresses for a private workforce. Workers will only be able to log in to their worker portal from an IP address within this range. By default, a workforce isn't restricted to specific IP addresses.
type nonrec cognito_config = {client_id : client_id;The client ID for your Amazon Cognito user pool.
*)user_pool : cognito_user_pool;A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
*)}Use this parameter to configure your Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.
type nonrec authentication_request_extra_params =
(authentication_request_extra_params_key
* authentication_request_extra_params_value)
listtype nonrec oidc_config_for_response = {authentication_request_extra_params : authentication_request_extra_params
option;A string to string map of identifiers specific to the custom identity provider (IdP) being used.
*)scope : scope option;An array of string identifiers used to refer to the specific pieces of user data or claims that the client application wants to access.
*)jwks_uri : oidc_endpoint option;The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
*)logout_endpoint : oidc_endpoint option;The OIDC IdP logout endpoint used to configure your private workforce.
*)user_info_endpoint : oidc_endpoint option;The OIDC IdP user information endpoint used to configure your private workforce.
*)token_endpoint : oidc_endpoint option;The OIDC IdP token endpoint used to configure your private workforce.
*)issuer : oidc_endpoint option;The OIDC IdP issuer used to configure your private workforce.
*)client_id : client_id option;The OIDC IdP client ID used to configure your private workforce.
*)}Your OIDC IdP workforce configuration.
type nonrec workforce_security_group_ids = workforce_security_group_id listtype nonrec workforce_subnets = workforce_subnet_id listtype nonrec workforce_vpc_config_response = {vpc_endpoint_id : workforce_vpc_endpoint_id option;The IDs for the VPC service endpoints of your VPC workforce when it is created and updated.
*)subnets : workforce_subnets;The ID of the subnets in the VPC that you want to connect.
*)security_group_ids : workforce_security_group_ids;The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
*)vpc_id : workforce_vpc_id;The ID of the VPC that the workforce uses for communication.
*)}A VpcConfig object that specifies the VPC that you want your workforce to connect to.
type nonrec workforce = {ip_address_type : workforce_ip_address_type option;The IP address type you specify - either IPv4 only or dualstack (IPv4 and IPv6) - to support your labeling workforce.
failure_reason : workforce_failure_reason option;The reason your workforce failed.
*)status : workforce_status option;The status of your workforce.
*)workforce_vpc_config : workforce_vpc_config_response option;The configuration of a VPC workforce.
*)create_date : timestamp option;The date that the workforce is created.
*)oidc_config : oidc_config_for_response option;The configuration of an OIDC Identity Provider (IdP) private workforce.
*)cognito_config : cognito_config option;The configuration of an Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.
*)sub_domain : string_ option;The subdomain for your OIDC Identity Provider.
*)source_ip_config : source_ip_config option;A list of one to ten IP address ranges (CIDRs) to be added to the workforce allow list. By default, a workforce isn't restricted to specific IP addresses.
*)last_updated_date : timestamp option;The most recent date that UpdateWorkforce was used to successfully add one or more IP address ranges (CIDRs) to a private workforce's allow list.
*)workforce_arn : workforce_arn;The Amazon Resource Name (ARN) of the private workforce.
*)workforce_name : workforce_name;The name of the private workforce.
*)}A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.
type nonrec workforces = workforce listtype nonrec workforce_vpc_config_request = {subnets : workforce_subnets option;The ID of the subnets in the VPC that you want to connect.
*)security_group_ids : workforce_security_group_ids option;The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
vpc_id : workforce_vpc_id option;The ID of the VPC that the workforce uses for communication.
*)}The VPC object you use to create or update a workforce.
type nonrec warm_pool_status = {reused_by_job : training_job_name option;The name of the matching training job that reused the warm pool.
*)resource_retained_billable_time_in_seconds : resource_retained_billable_time_in_seconds
option;The billable time in seconds used by the warm pool. Billable time refers to the absolute wall-clock time.
Multiply ResourceRetainedBillableTimeInSeconds by the number of instances (InstanceCount) in your training cluster to get the total compute time SageMaker bills you if you run warm pool training. The formula is as follows: ResourceRetainedBillableTimeInSeconds * InstanceCount.
status : warm_pool_resource_status;The status of the warm pool.
InUse: The warm pool is in use for the training job.Available: The warm pool is available to reuse for a matching training job.Reused: The warm pool moved to a matching training job for reuse.Terminated: The warm pool is no longer available. Warm pools are unavailable if they are terminated by a user, terminated for a patch update, or terminated for exceeding the specified KeepAlivePeriodInSeconds.}Status and billing information about the warm pool.
type nonrec vpc_security_group_ids = security_group_id listtype nonrec vpc_only_trusted_accounts = account_id listtype nonrec subnets = subnet_id listtype nonrec vpc_config = {subnets : subnets;The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
*)security_group_ids : vpc_security_group_ids;The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
}Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
type nonrec visibility_conditions = {value : visibility_conditions_value option;The value for the tag that you're using to filter the search results.
*)key : visibility_conditions_key option;The key that specifies the tag that you're using to filter the search results. It must be in the following format: Tags.<key>.
}The list of key-value pairs used to filter your search results. If a search result contains a key from your list, it is included in the final search response if the value associated with the key in the result matches the value you specified. If the value doesn't match, the result is excluded from the search response. Any resources that don't have a key from the list that you've provided will also be included in the search response.
type nonrec visibility_conditions_list = visibility_conditions listtype nonrec vertex = {lineage_type : lineage_type option;The type of resource of the lineage entity.
*)type_ : string40 option;The type of the lineage entity resource. For example: DataSet, Model, Endpoint, etc...
arn : association_entity_arn option;The Amazon Resource Name (ARN) of the lineage entity resource.
*)}A lineage entity connected to the starting entity(ies).
type nonrec vertices = vertex listtype nonrec version_aliases_list = image_version_alias_pattern listConfiguration for your vector collection type.
type nonrec variant_property = {variant_property_type : variant_property_type;The type of variant property. The supported values are:
DesiredInstanceCount: Overrides the existing variant instance counts using the InitialInstanceCount values in the ProductionVariants of CreateEndpointConfig.DesiredWeight: Overrides the existing variant weights using the InitialVariantWeight values in the ProductionVariants of CreateEndpointConfig.DataCaptureConfig: (Not currently supported.)}Specifies a production variant property type for an Endpoint.
If you are updating an endpoint with the RetainAllVariantProperties option of UpdateEndpointInput set to true, the VariantProperty objects listed in the ExcludeRetainedVariantProperties parameter of UpdateEndpointInput override the existing variant properties of the endpoint.
type nonrec variant_property_list = variant_property listtype nonrec security_group_ids = security_group_id listtype nonrec sharing_settings = {s3_kms_key_id : kms_key_id option;When NotebookOutputOption is Allowed, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
s3_output_path : s3_uri option;When NotebookOutputOption is Allowed, the Amazon S3 bucket used to store the shared notebook snapshots.
notebook_output_option : notebook_output_option option;Whether to include the notebook cell output when sharing the notebook. The default is Disabled.
}Specifies options for sharing Amazon SageMaker AI Studio notebooks. These settings are specified as part of DefaultUserSettings when the CreateDomain API is called, and as part of UserSettings when the CreateUserProfile API is called. When SharingSettings is not specified, notebook sharing isn't allowed.
type nonrec app_instance_type = | ML_G7E_48XLARGE| ML_G7E_24XLARGE| ML_G7E_12XLARGE| ML_G7E_8XLARGE| ML_G7E_4XLARGE| ML_G7E_2XLARGE| ML_P5_4XLARGE| ML_R6ID_32XLARGE| ML_R6ID_24XLARGE| ML_R6ID_16XLARGE| ML_R6ID_12XLARGE| ML_R6ID_8XLARGE| ML_R6ID_4XLARGE| ML_R6ID_2XLARGE| ML_R6ID_XLARGE| ML_R6ID_LARGE| ML_C6ID_32XLARGE| ML_C6ID_24XLARGE| ML_C6ID_16XLARGE| ML_C6ID_12XLARGE| ML_C6ID_8XLARGE| ML_C6ID_4XLARGE| ML_C6ID_2XLARGE| ML_C6ID_XLARGE| ML_C6ID_LARGE| ML_M6ID_32XLARGE| ML_M6ID_24XLARGE| ML_M6ID_16XLARGE| ML_M6ID_12XLARGE| ML_M6ID_8XLARGE| ML_M6ID_4XLARGE| ML_M6ID_2XLARGE| ML_M6ID_XLARGE| ML_M6ID_LARGE| ML_R7I_48XLARGE| ML_R7I_24XLARGE| ML_R7I_16XLARGE| ML_R7I_12XLARGE| ML_R7I_8XLARGE| ML_R7I_4XLARGE| ML_R7I_2XLARGE| ML_R7I_XLARGE| ML_R7I_LARGE| ML_R6I_32XLARGE| ML_R6I_24XLARGE| ML_R6I_16XLARGE| ML_R6I_12XLARGE| ML_R6I_8XLARGE| ML_R6I_4XLARGE| ML_R6I_2XLARGE| ML_R6I_XLARGE| ML_R6I_LARGE| ML_C7I_48XLARGE| ML_C7I_24XLARGE| ML_C7I_16XLARGE| ML_C7I_12XLARGE| ML_C7I_8XLARGE| ML_C7I_4XLARGE| ML_C7I_2XLARGE| ML_C7I_XLARGE| ML_C7I_LARGE| ML_C6I_32XLARGE| ML_C6I_24XLARGE| ML_C6I_16XLARGE| ML_C6I_12XLARGE| ML_C6I_8XLARGE| ML_C6I_4XLARGE| ML_C6I_2XLARGE| ML_C6I_XLARGE| ML_C6I_LARGE| ML_M7I_48XLARGE| ML_M7I_24XLARGE| ML_M7I_16XLARGE| ML_M7I_12XLARGE| ML_M7I_8XLARGE| ML_M7I_4XLARGE| ML_M7I_2XLARGE| ML_M7I_XLARGE| ML_M7I_LARGE| ML_M6I_32XLARGE| ML_M6I_24XLARGE| ML_M6I_16XLARGE| ML_M6I_12XLARGE| ML_M6I_8XLARGE| ML_M6I_4XLARGE| ML_M6I_2XLARGE| ML_M6I_XLARGE| ML_M6I_LARGE| ML_P6_B200_48XLARGE| ML_P5EN_48XLARGE| ML_P5_48XLARGE| ML_TRN1N_32XLARGE| ML_TRN1_32XLARGE| ML_TRN1_2XLARGE| ML_P4DE_24XLARGE| ML_P4D_24XLARGE| ML_GEOSPATIAL_INTERACTIVE| ML_G6E_48XLARGE| ML_G6E_24XLARGE| ML_G6E_16XLARGE| ML_G6E_12XLARGE| ML_G6E_8XLARGE| ML_G6E_4XLARGE| ML_G6E_2XLARGE| ML_G6E_XLARGE| ML_G6_48XLARGE| ML_G6_24XLARGE| ML_G6_16XLARGE| ML_G6_12XLARGE| ML_G6_8XLARGE| ML_G6_4XLARGE| ML_G6_2XLARGE| ML_G6_XLARGE| ML_G5_48XLARGE| ML_G5_24XLARGE| ML_G5_12XLARGE| ML_G5_16XLARGE| ML_G5_8XLARGE| ML_G5_4XLARGE| ML_G5_2XLARGE| ML_G5_XLARGE| ML_R5_24XLARGE| ML_R5_16XLARGE| ML_R5_12XLARGE| ML_R5_8XLARGE| ML_R5_4XLARGE| ML_R5_2XLARGE| ML_R5_XLARGE| ML_R5_LARGE| ML_G4DN_16XLARGE| ML_G4DN_12XLARGE| ML_G4DN_8XLARGE| ML_G4DN_4XLARGE| ML_G4DN_2XLARGE| ML_G4DN_XLARGE| ML_P3DN_24XLARGE| ML_P3_16XLARGE| ML_P3_8XLARGE| ML_P3_2XLARGE| ML_C5_24XLARGE| ML_C5_18XLARGE| ML_C5_12XLARGE| ML_C5_9XLARGE| ML_C5_4XLARGE| ML_C5_2XLARGE| ML_C5_XLARGE| ML_C5_LARGE| ML_M5D_24XLARGE| ML_M5D_16XLARGE| ML_M5D_12XLARGE| ML_M5D_8XLARGE| ML_M5D_4XLARGE| ML_M5D_2XLARGE| ML_M5D_XLARGE| ML_M5D_LARGE| ML_M5_24XLARGE| ML_M5_16XLARGE| ML_M5_12XLARGE| ML_M5_8XLARGE| ML_M5_4XLARGE| ML_M5_2XLARGE| ML_M5_XLARGE| ML_M5_LARGE| ML_T3_2XLARGE| ML_T3_XLARGE| ML_T3_LARGE| ML_T3_MEDIUM| ML_T3_SMALL| ML_T3_MICRO| SYSTEMtype nonrec resource_spec = {training_plan_arn : studio_resource_spec_training_plan_arn option;The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
*)lifecycle_config_arn : studio_lifecycle_config_arn option;The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
*)instance_type : app_instance_type option;The instance type that the image version runs on.
JupyterServer apps only support the system value.
For KernelGateway apps, the system value is translated to ml.t3.medium. KernelGateway apps also support all other values for available instance types.
sage_maker_image_version_alias : image_version_alias option;The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
*)sage_maker_image_version_arn : image_version_arn option;The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
sage_maker_image_arn : image_arn option;The ARN of the SageMaker AI image that the image version belongs to.
*)}Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
When both SageMakerImageVersionArn and SageMakerImageArn are passed, SageMakerImageVersionArn is used. Any updates to SageMakerImageArn will not take effect if SageMakerImageVersionArn already exists in the ResourceSpec because SageMakerImageVersionArn always takes precedence. To clear the value set for SageMakerImageVersionArn, pass None as the value.
type nonrec lifecycle_config_arns = studio_lifecycle_config_arn listA Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
type nonrec code_repositories = code_repository listtype nonrec jupyter_server_app_settings = {code_repositories : code_repositories option;A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
*)lifecycle_config_arns : lifecycle_config_arns option;The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
default_resource_spec : resource_spec option;The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
}The JupyterServer app settings.
type nonrec custom_image = {app_image_config_name : app_image_config_name;The name of the AppImageConfig.
*)image_version_number : image_version_number option;The version number of the CustomImage.
*)image_name : image_name;The name of the CustomImage. Must be unique to your account.
*)}A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
type nonrec custom_images = custom_image listtype nonrec kernel_gateway_app_settings = {lifecycle_config_arns : lifecycle_config_arns option;The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
custom_images : custom_images option;A list of custom SageMaker AI images that are configured to run as a KernelGateway app.
The maximum number of custom images are as follows.
default_resource_spec : resource_spec option;The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app.
The Amazon SageMaker AI Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
*)}The KernelGateway app settings.
type nonrec tensor_board_app_settings = {default_resource_spec : resource_spec option;The default instance type and the Amazon Resource Name (ARN) of the SageMaker AI image created on the instance.
*)}The TensorBoard app settings.
type nonrec r_studio_server_pro_app_settings = {user_group : r_studio_server_pro_user_group option;The level of permissions that the user has within the RStudioServerPro app. This value defaults to `User`. The `Admin` value allows the user access to the RStudio Administrative Dashboard.
access_status : r_studio_server_pro_access_status option;Indicates whether the current user has access to the RStudioServerPro app.
}A collection of settings that configure user interaction with the RStudioServerPro app.
type nonrec r_session_app_settings = {custom_images : custom_images option;A list of custom SageMaker AI images that are configured to run as a RSession app.
*)default_resource_spec : resource_spec option;}A collection of settings that apply to an RSessionGateway app.
type nonrec time_series_forecasting_settings = {amazon_forecast_role_arn : role_arn option;The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile, Canvas uses the execution role specified in the Domain that owns the UserProfile. To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.
status : feature_status option;Describes whether time series forecasting is enabled or disabled in the Canvas application.
*)}Time series forecast settings for the SageMaker Canvas application.
type nonrec model_register_settings = {cross_account_model_register_role_arn : role_arn option;The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
*)status : feature_status option;Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
*)}The model registry settings for the SageMaker Canvas application.
type nonrec identity_provider_o_auth_setting = {secret_arn : secret_arn option;The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
*)status : feature_status option;Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
*)data_source_name : data_source_name option;The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
*)}The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.
type nonrec identity_provider_o_auth_settings =
identity_provider_o_auth_setting listtype nonrec direct_deploy_settings = {status : feature_status option;Describes whether model deployment permissions are enabled or disabled in the Canvas application.
*)}The model deployment settings for the SageMaker Canvas application.
In order to enable model deployment for Canvas, the SageMaker Domain's or user profile's Amazon Web Services IAM execution role must have the AmazonSageMakerCanvasDirectDeployAccess policy attached. You can also turn on model deployment permissions through the SageMaker Domain's or user profile's settings in the SageMaker console.
type nonrec kendra_settings = {status : feature_status option;Describes whether the document querying feature is enabled or disabled in the Canvas application.
*)}The Amazon SageMaker Canvas application setting where you configure document querying.
type nonrec generative_ai_settings = {amazon_bedrock_role_arn : role_arn option;The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.
}The generative AI settings for the SageMaker Canvas application.
Configure these settings for Canvas users starting chats with generative AI foundation models. For more information, see Use generative AI with foundation models.
type nonrec emr_serverless_settings = {status : feature_status option;Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
*)execution_role_arn : role_arn option;The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
*)}The settings for running Amazon EMR Serverless jobs in SageMaker Canvas.
type nonrec canvas_app_settings = {emr_serverless_settings : emr_serverless_settings option;The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
*)generative_ai_settings : generative_ai_settings option;The generative AI settings for the SageMaker Canvas application.
*)kendra_settings : kendra_settings option;The settings for document querying.
*)direct_deploy_settings : direct_deploy_settings option;The model deployment settings for the SageMaker Canvas application.
*)identity_provider_o_auth_settings : identity_provider_o_auth_settings option;The settings for connecting to an external data source with OAuth.
*)workspace_settings : workspace_settings option;The workspace settings for the SageMaker Canvas application.
*)model_register_settings : model_register_settings option;The model registry settings for the SageMaker Canvas application.
*)time_series_forecasting_settings : time_series_forecasting_settings option;Time series forecast settings for the SageMaker Canvas application.
*)}The SageMaker Canvas application settings.
type nonrec idle_settings = {max_idle_timeout_in_minutes : idle_timeout_in_minutes option;The maximum value in minutes that custom idle shutdown can be set to by the user.
*)min_idle_timeout_in_minutes : idle_timeout_in_minutes option;The minimum value in minutes that custom idle shutdown can be set to by the user.
*)idle_timeout_in_minutes : idle_timeout_in_minutes option;The time that SageMaker waits after the application becomes idle before shutting it down.
*)lifecycle_management : lifecycle_management option;Indicates whether idle shutdown is activated for the application type.
*)}Settings related to idle shutdown of Studio applications.
type nonrec app_lifecycle_management = {idle_settings : idle_settings option;Settings related to idle shutdown of Studio applications.
*)}Settings that are used to configure and manage the lifecycle of Amazon SageMaker Studio applications.
type nonrec code_editor_app_settings = {built_in_lifecycle_config_arn : studio_lifecycle_config_arn option;The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
*)app_lifecycle_management : app_lifecycle_management option;Settings that are used to configure and manage the lifecycle of CodeEditor applications.
*)lifecycle_config_arns : lifecycle_config_arns option;The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
*)custom_images : custom_images option;A list of custom SageMaker images that are configured to run as a Code Editor app.
*)default_resource_spec : resource_spec option;}The Code Editor application settings.
For more information about Code Editor, see Get started with Code Editor in Amazon SageMaker.
type nonrec assumable_role_arns = role_arn listtype nonrec execution_role_arns = role_arn listtype nonrec emr_settings = {execution_role_arns : execution_role_arns option;An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
*)assumable_role_arns : assumable_role_arns option;An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
*)}The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
type nonrec jupyter_lab_app_settings = {built_in_lifecycle_config_arn : studio_lifecycle_config_arn option;The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
*)emr_settings : emr_settings option;The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
*)app_lifecycle_management : app_lifecycle_management option;Indicates whether idle shutdown is activated for JupyterLab applications.
*)code_repositories : code_repositories option;A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
*)lifecycle_config_arns : lifecycle_config_arns option;The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
custom_images : custom_images option;A list of custom SageMaker images that are configured to run as a JupyterLab app.
*)default_resource_spec : resource_spec option;}The settings for the JupyterLab application.
type nonrec default_ebs_storage_settings = {maximum_ebs_volume_size_in_gb : space_ebs_volume_size_in_gb;The maximum size of the EBS storage volume for a space.
*)default_ebs_volume_size_in_gb : space_ebs_volume_size_in_gb;The default size of the EBS storage volume for a space.
*)}A collection of default EBS storage settings that apply to spaces created within a domain or user profile.
type nonrec default_space_storage_settings = {default_ebs_storage_settings : default_ebs_storage_settings option;The default EBS storage settings for a space.
*)}The default storage settings for a space.
type nonrec uid = Smaws_Lib.CoreTypes.Int64.ttype nonrec gid = Smaws_Lib.CoreTypes.Int64.tDetails about the POSIX identity that is used for file system operations.
type nonrec efs_file_system_config = {file_system_path : file_system_path option;The path to the file system directory that is accessible in Amazon SageMaker AI Studio. Permitted users can access only this directory and below.
*)file_system_id : file_system_id;The ID of your Amazon EFS file system.
*)}The settings for assigning a custom Amazon EFS file system to a user profile or space for an Amazon SageMaker AI Domain.
type nonrec f_sx_lustre_file_system_config = {file_system_path : file_system_path option;The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
*)file_system_id : file_system_id;The globally unique, 17-digit, ID of the file system, assigned by Amazon FSx for Lustre.
*)}The settings for assigning a custom Amazon FSx for Lustre file system to a user profile or space for an Amazon SageMaker Domain.
type nonrec s3_file_system_config = {s3_uri : s3_schema_uri;The Amazon S3 URI of the S3 file system configuration.
*)mount_path : string1024 option;The file system path where the Amazon S3 storage location will be mounted within the Amazon SageMaker Studio environment.
*)}Configuration for the custom Amazon S3 file system.
type nonrec custom_file_system_config = | S3FileSystemConfig of s3_file_system_configConfiguration settings for a custom Amazon S3 file system.
*)| FSxLustreFileSystemConfig of f_sx_lustre_file_system_configThe settings for a custom Amazon FSx for Lustre file system.
*)| EFSFileSystemConfig of efs_file_system_configThe settings for a custom Amazon EFS file system.
*)The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
type nonrec custom_file_system_configs = custom_file_system_config listtype nonrec ml_tools = | EVALUATORS| DATASETS| RUNNING_INSTANCES| HYPER_POD_CLUSTERS| FIDDLER| DEEPCHECKS_LLM_EVALUATION| COMET| LAKERA_GUARD| PERFORMANCE_EVALUATION| INFERENCE_OPTIMIZATION| PROJECTS| ENDPOINTS| INFERENCE_RECOMMENDER| JUMP_START| MODELS| PIPELINES| MODEL_EVALUATION| TRAINING| EXPERIMENTS| AUTO_ML| EMR_CLUSTERS| FEATURE_STORE| DATA_WRANGLERThe SageMaker images that are hidden from the Studio user interface. You must specify the SageMaker image name and version aliases.
type nonrec studio_web_portal_settings = {execution_role_session_name_mode : execution_role_session_name_mode option;The execution role session name mode. If this value is set to USER_IDENTITY, the session name of the execution role corresponds to the user's identity. For IAM domains, the session name is the IAM session name used to generate the presigned URL. For IAM Identity Center domains, the session name is the username of the associated IAM Identity Center user. If this value is set to STATIC or is not set, the session name defaults to SageMaker.
}Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
type nonrec user_settings = {auto_mount_home_ef_s : auto_mount_home_ef_s option;Indicates whether auto-mounting of an EFS volume is supported for the user profile. The DefaultAsDomain value is only supported for user profiles. Do not use the DefaultAsDomain value when setting this parameter for a domain.
SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces.
*)studio_web_portal_settings : studio_web_portal_settings option;Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
*)custom_file_system_configs : custom_file_system_configs option;The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker AI Studio.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
*)custom_posix_user_config : custom_posix_user_config option;Details about the POSIX identity that is used for file system operations.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
*)studio_web_portal : studio_web_portal option;Whether the user can access Studio. If this value is set to DISABLED, the user cannot access Studio, even if that is the default experience for the domain.
default_landing_uri : landing_uri option;The default experience that the user is directed to when accessing the domain. The supported values are:
studio::: Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED.app:JupyterServer:: Indicates that Studio Classic is the default experience.space_storage_settings : default_space_storage_settings option;The storage settings for a space.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
*)jupyter_lab_app_settings : jupyter_lab_app_settings option;The settings for the JupyterLab application.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
*)code_editor_app_settings : code_editor_app_settings option;The Code Editor application settings.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
*)canvas_app_settings : canvas_app_settings option;The Canvas app settings.
SageMaker applies these settings only to private spaces that SageMaker creates for the Canvas app.
*)r_session_app_settings : r_session_app_settings option;A collection of settings that configure the RSessionGateway app.
r_studio_server_pro_app_settings : r_studio_server_pro_app_settings option;A collection of settings that configure user interaction with the RStudioServerPro app.
tensor_board_app_settings : tensor_board_app_settings option;The TensorBoard app settings.
*)kernel_gateway_app_settings : kernel_gateway_app_settings option;The kernel gateway app settings.
*)jupyter_server_app_settings : jupyter_server_app_settings option;The Jupyter server's app settings.
*)sharing_settings : sharing_settings option;Specifies options for sharing Amazon SageMaker AI Studio notebooks.
*)security_groups : security_group_ids option;The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly.
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly, unless specified as part of the DefaultUserSettings for the domain.
Amazon SageMaker AI adds a security group to allow NFS traffic from Amazon SageMaker AI Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
*)execution_role : role_arn option;The execution role for the user.
SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces.
*)}A collection of settings that apply to users in a domain. These settings are specified when the CreateUserProfile API is called, and as DefaultUserSettings when the CreateDomain API is called.
SecurityGroups is aggregated when specified in both calls. For all other settings in UserSettings, the values specified in CreateUserProfile take precedence over those specified in CreateDomain.
type nonrec user_profile_details = {last_modified_time : last_modified_time option;The last modified time.
*)creation_time : creation_time option;The creation time.
*)status : user_profile_status option;The status.
*)user_profile_name : user_profile_name option;The user profile name.
*)domain_id : domain_id option;The domain ID.
*)}The user profile details.
type nonrec user_profile_list = user_profile_details listtype nonrec iam_identity = {source_identity : string_ option;The person or application which assumes the IAM identity.
*)principal_id : string_ option;The ID of the principal that assumes the IAM identity.
*)arn : string_ option;The Amazon Resource Name (ARN) of the IAM identity.
*)}The IAM Identity details associated with the user. These details are associated with model package groups, model packages and project entities only.
type nonrec user_context = {iam_identity : iam_identity option;The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
*)domain_id : string_ option;The domain associated with the user.
*)user_profile_name : string_ option;The name of the user's profile.
*)user_profile_arn : string_ option;The Amazon Resource Name (ARN) of the user's profile.
*)}Information about the user who created or modified a SageMaker resource.
type nonrec update_workteam_response = {workteam : workteam;A Workteam object that describes the updated work team.
}type nonrec update_workteam_request = {worker_access_configuration : worker_access_configuration option;Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.
*)notification_configuration : notification_configuration option;Configures SNS topic notifications for available or expiring work items
*)description : string200 option;An updated description for the work team.
*)member_definitions : member_definitions option;A list of MemberDefinition objects that contains objects that identify the workers that make up the work team.
Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition. For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition. You should not provide input for both of these parameters in a single request.
For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups within the user pool used to create a workforce. All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more information about user pools, see Amazon Cognito User Pools.
For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in OidcMemberDefinition by listing those groups in Groups. Be aware that user groups that are already in the work team must also be listed in Groups when you make this request to remain on the work team. If you do not include these user groups, they will no longer be associated with the work team you update.
workteam_name : workteam_name;The name of the work team to update.
*)}You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
type nonrec update_workforce_response = {workforce : workforce;A single private workforce. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.
*)}type nonrec oidc_config = {authentication_request_extra_params : authentication_request_extra_params
option;A string to string map of identifiers specific to the custom identity provider (IdP) being used.
*)scope : scope option;An array of string identifiers used to refer to the specific pieces of user data or claims that the client application wants to access.
*)jwks_uri : oidc_endpoint;The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
*)logout_endpoint : oidc_endpoint;The OIDC IdP logout endpoint used to configure your private workforce.
*)user_info_endpoint : oidc_endpoint;The OIDC IdP user information endpoint used to configure your private workforce.
*)token_endpoint : oidc_endpoint;The OIDC IdP token endpoint used to configure your private workforce.
*)issuer : oidc_endpoint;The OIDC IdP issuer used to configure your private workforce.
*)client_secret : client_secret;The OIDC IdP client secret used to configure your private workforce.
*)client_id : client_id;The OIDC IdP client ID used to configure your private workforce.
*)}Use this parameter to configure your OIDC Identity Provider (IdP).
type nonrec update_workforce_request = {ip_address_type : workforce_ip_address_type option;Use this parameter to specify whether you want IPv4 only or dualstack (IPv4 and IPv6) to support your labeling workforce.
workforce_vpc_config : workforce_vpc_config_request option;Use this parameter to update your VPC configuration for a workforce.
*)oidc_config : oidc_config option;Use this parameter to update your OIDC Identity Provider (IdP) configuration for a workforce made using your own IdP.
*)source_ip_config : source_ip_config option;A list of one to ten worker IP address ranges (CIDRs) that can be used to access tasks assigned to this workforce.
Maximum: Ten CIDR values
*)workforce_name : workforce_name;The name of the private workforce that you want to update. You can find your workforce name by using the ListWorkforces operation.
*)}There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
type nonrec update_user_profile_response = {user_profile_arn : user_profile_arn option;The user profile Amazon Resource Name (ARN).
*)}type nonrec update_user_profile_request = {user_settings : user_settings option;A collection of settings.
*)user_profile_name : user_profile_name;The user profile name.
*)domain_id : domain_id;The domain ID.
*)}Resource being access is not found.
Resource being accessed is in use.
type nonrec update_trial_response = {trial_arn : trial_arn option;The Amazon Resource Name (ARN) of the trial.
*)}type nonrec update_trial_request = {display_name : experiment_entity_name option;The name of the trial as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialName is displayed.
trial_name : experiment_entity_name;The name of the trial to update.
*)}type nonrec update_trial_component_response = {trial_component_arn : trial_component_arn option;The Amazon Resource Name (ARN) of the trial component.
*)}type nonrec trial_component_status = {message : trial_component_status_message option;If the component failed, a message describing why.
*)primary_status : trial_component_primary_status option;The status of the trial component.
*)}The status of the trial component.
type nonrec trial_component_parameter_value = | NumberValue of double_parameter_valueThe numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the StringValue parameter.
| StringValue of string_parameter_valueThe string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the NumberValue parameter.
The value of a hyperparameter. Only one of NumberValue or StringValue can be specified.
This object is specified in the CreateTrialComponent request.
type nonrec trial_component_parameters =
(trial_component_key320 * trial_component_parameter_value) listtype nonrec list_trial_component_key256 = trial_component_key256 listtype nonrec trial_component_artifact = {value : trial_component_artifact_value;The location of the artifact.
*)media_type : media_type option;The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
*)}Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
type nonrec trial_component_artifacts =
(trial_component_key128 * trial_component_artifact) listtype nonrec update_trial_component_request = {output_artifacts_to_remove : list_trial_component_key256 option;The output artifacts to remove from the component.
*)output_artifacts : trial_component_artifacts option;Replaces all of the component's output artifacts with the specified artifacts or adds new output artifacts. Existing output artifacts are replaced if the trial component is updated with an identical output artifact key.
*)input_artifacts_to_remove : list_trial_component_key256 option;The input artifacts to remove from the component.
*)input_artifacts : trial_component_artifacts option;Replaces all of the component's input artifacts with the specified artifacts or adds new input artifacts. Existing input artifacts are replaced if the trial component is updated with an identical input artifact key.
*)parameters_to_remove : list_trial_component_key256 option;The hyperparameters to remove from the component.
*)parameters : trial_component_parameters option;Replaces all of the component's hyperparameters with the specified hyperparameters or add new hyperparameters. Existing hyperparameters are replaced if the trial component is updated with an identical hyperparameter key.
*)end_time : timestamp option;When the component ended.
*)start_time : timestamp option;When the component started.
*)status : trial_component_status option;The new status of the component.
*)display_name : experiment_entity_name option;The name of the component as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialComponentName is displayed.
trial_component_name : experiment_entity_name;The name of the component to update.
*)}type nonrec update_training_job_response = {training_job_arn : training_job_arn;The Amazon Resource Name (ARN) of the training job.
*)}type nonrec profiling_interval_in_milliseconds = Smaws_Lib.CoreTypes.Int64.ttype nonrec profiling_parameters = (config_key * config_value) listtype nonrec profiler_config_for_update = {disable_profiler : disable_profiler option;To turn off Amazon SageMaker Debugger monitoring and profiling while a training job is in progress, set to True.
profiling_parameters : profiling_parameters option;Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig, PythonProfilingConfig, and DataLoaderProfilingConfig. The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
profiling_interval_in_milliseconds : profiling_interval_in_milliseconds option;A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
*)s3_output_path : s3_uri option;Path to Amazon S3 storage location for system and framework metrics.
*)}Configuration information for updating the Amazon SageMaker Debugger profile parameters, system and framework metrics configurations, and storage paths.
type nonrec processing_instance_type = | ML_G7E_48XLARGE| ML_G7E_24XLARGE| ML_G7E_12XLARGE| ML_G7E_8XLARGE| ML_G7E_4XLARGE| ML_G7E_2XLARGE| ML_P5_4XLARGE| ML_R7I_48XLARGE| ML_R7I_24XLARGE| ML_R7I_16XLARGE| ML_R7I_12XLARGE| ML_R7I_8XLARGE| ML_R7I_4XLARGE| ML_R7I_2XLARGE| ML_R7I_XLARGE| ML_R7I_LARGE| ML_C7I_48XLARGE| ML_C7I_24XLARGE| ML_C7I_16XLARGE| ML_C7I_12XLARGE| ML_C7I_8XLARGE| ML_C7I_4XLARGE| ML_C7I_2XLARGE| ML_C7I_XLARGE| ML_C7I_LARGE| ML_M7I_48XLARGE| ML_M7I_24XLARGE| ML_M7I_16XLARGE| ML_M7I_12XLARGE| ML_M7I_8XLARGE| ML_M7I_4XLARGE| ML_M7I_2XLARGE| ML_M7I_XLARGE| ML_M7I_LARGE| ML_C6I_32XLARGE| ML_C6I_24XLARGE| ML_C6I_16XLARGE| ML_C6I_12XLARGE| ML_C6I_8XLARGE| ML_C6I_4XLARGE| ML_C6I_2XLARGE| ML_C6I_XLARGE| ML_M6I_32XLARGE| ML_M6I_24XLARGE| ML_M6I_16XLARGE| ML_M6I_12XLARGE| ML_M6I_8XLARGE| ML_M6I_4XLARGE| ML_M6I_2XLARGE| ML_M6I_XLARGE| ML_M6I_LARGE| ML_G6E_48XLARGE| ML_G6E_24XLARGE| ML_G6E_16XLARGE| ML_G6E_12XLARGE| ML_G6E_8XLARGE| ML_G6E_4XLARGE| ML_G6E_2XLARGE| ML_G6E_XLARGE| ML_G6_48XLARGE| ML_G6_24XLARGE| ML_G6_16XLARGE| ML_G6_12XLARGE| ML_G6_8XLARGE| ML_G6_4XLARGE| ML_G6_2XLARGE| ML_G6_XLARGE| ML_R5D_24XLARGE| ML_R5D_16XLARGE| ML_R5D_12XLARGE| ML_R5D_8XLARGE| ML_R5D_4XLARGE| ML_R5D_2XLARGE| ML_R5D_XLARGE| ML_R5D_LARGE| ML_G5_48XLARGE| ML_G5_24XLARGE| ML_G5_12XLARGE| ML_G5_16XLARGE| ML_G5_8XLARGE| ML_G5_4XLARGE| ML_G5_2XLARGE| ML_G5_XLARGE| ML_G4DN_16XLARGE| ML_G4DN_12XLARGE| ML_G4DN_8XLARGE| ML_G4DN_4XLARGE| ML_G4DN_2XLARGE| ML_G4DN_XLARGE| ML_R5_24XLARGE| ML_R5_16XLARGE| ML_R5_12XLARGE| ML_R5_8XLARGE| ML_R5_4XLARGE| ML_R5_2XLARGE| ML_R5_XLARGE| ML_R5_LARGE| ML_M5_24XLARGE| ML_M5_12XLARGE| ML_M5_4XLARGE| ML_M5_2XLARGE| ML_M5_XLARGE| ML_M5_LARGE| ML_C5_18XLARGE| ML_C5_9XLARGE| ML_C5_4XLARGE| ML_C5_2XLARGE| ML_C5_XLARGE| ML_P3_16XLARGE| ML_P3_8XLARGE| ML_P3_2XLARGE| ML_P2_16XLARGE| ML_P2_8XLARGE| ML_P2_XLARGE| ML_C4_8XLARGE| ML_C4_4XLARGE| ML_C4_2XLARGE| ML_C4_XLARGE| ML_M4_16XLARGE| ML_M4_10XLARGE| ML_M4_4XLARGE| ML_M4_2XLARGE| ML_M4_XLARGE| ML_T3_2XLARGE| ML_T3_XLARGE| ML_T3_LARGE| ML_T3_MEDIUMtype nonrec rule_parameters = (config_key * config_value) listtype nonrec profiler_rule_configuration = {rule_parameters : rule_parameters option;Runtime configuration for rule container.
*)volume_size_in_g_b : optional_volume_size_in_g_b option;The size, in GB, of the ML storage volume attached to the processing instance.
*)instance_type : processing_instance_type option;The instance type to deploy a custom rule for profiling a training job.
*)rule_evaluator_image : algorithm_image;The Amazon Elastic Container Registry Image for the managed rule evaluation.
*)s3_output_path : s3_uri option;Path to Amazon S3 storage location for rules.
*)local_path : directory_path option;Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.
rule_configuration_name : rule_configuration_name;The name of the rule configuration. It must be unique relative to other rule configuration names.
*)}Configuration information for profiling rules.
type nonrec profiler_rule_configurations = profiler_rule_configuration listtype nonrec resource_config_for_update = {keep_alive_period_in_seconds : keep_alive_period_in_seconds;The KeepAlivePeriodInSeconds value specified in the ResourceConfig to update.
}The ResourceConfig to update KeepAlivePeriodInSeconds. Other fields in the ResourceConfig cannot be updated.
type nonrec remote_debug_config_for_update = {enable_remote_debug : enable_remote_debug option;If set to True, enables remote debugging.
*)}Configuration for remote debugging for the UpdateTrainingJob API. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
type nonrec update_training_job_request = {remote_debug_config : remote_debug_config_for_update option;Configuration for remote debugging while the training job is running. You can update the remote debugging configuration when the SecondaryStatus of the job is Downloading or Training.To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
resource_config : resource_config_for_update option;The training job ResourceConfig to update warm pool retention length.
profiler_rule_configurations : profiler_rule_configurations option;Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
*)profiler_config : profiler_config_for_update option;Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
*)training_job_name : training_job_name;The name of a training job to update the Debugger profiling configuration.
*)}type nonrec cfn_stack_update_parameter = {value : cfn_stack_parameter_value option;The value of the CloudFormation parameter.
*)key : cfn_stack_parameter_key;The name of the CloudFormation parameter.
*)}A key-value pair representing a parameter used in the CloudFormation stack.
type nonrec cfn_stack_update_parameters = cfn_stack_update_parameter listtype nonrec cfn_update_template_provider = {parameters : cfn_stack_update_parameters option;An array of CloudFormation stack parameters.
*)template_ur_l : cfn_template_ur_l;The Amazon S3 URL of the CloudFormation template.
*)template_name : cfn_template_name;The unique identifier of the template to update within the project.
*)}Contains configuration details for updating an existing CloudFormation template provider in the project.
type nonrec update_template_provider = {cfn_template_provider : cfn_update_template_provider option;The CloudFormation template provider configuration to update.
*)}Contains configuration details for updating an existing template provider in the project.
type nonrec update_template_provider_list = update_template_provider listtype nonrec update_space_response = {space_arn : space_arn option;The space's Amazon Resource Name (ARN).
*)}type nonrec space_idle_settings = {idle_timeout_in_minutes : idle_timeout_in_minutes option;The time that SageMaker waits after the application becomes idle before shutting it down.
*)}Settings related to idle shutdown of Studio applications in a space.
type nonrec space_app_lifecycle_management = {idle_settings : space_idle_settings option;Settings related to idle shutdown of Studio applications.
*)}Settings that are used to configure and manage the lifecycle of Amazon SageMaker Studio applications in a space.
type nonrec space_code_editor_app_settings = {app_lifecycle_management : space_app_lifecycle_management option;Settings that are used to configure and manage the lifecycle of CodeEditor applications in a space.
*)default_resource_spec : resource_spec option;}The application settings for a Code Editor space.
type nonrec space_jupyter_lab_app_settings = {app_lifecycle_management : space_app_lifecycle_management option;Settings that are used to configure and manage the lifecycle of JupyterLab applications in a space.
*)code_repositories : code_repositories option;A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
*)default_resource_spec : resource_spec option;}The settings for the JupyterLab application within a space.
type nonrec ebs_storage_settings = {ebs_volume_size_in_gb : space_ebs_volume_size_in_gb;The size of an EBS storage volume for a space.
*)}A collection of EBS storage settings that apply to both private and shared spaces.
type nonrec space_storage_settings = {ebs_storage_settings : ebs_storage_settings option;A collection of EBS storage settings for a space.
*)}The storage settings for a space.
type nonrec efs_file_system = {file_system_id : file_system_id;The ID of your Amazon EFS file system.
*)}A file system, created by you in Amazon EFS, that you assign to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
type nonrec f_sx_lustre_file_system = {file_system_id : file_system_id;Amazon FSx for Lustre file system ID.
*)}A custom file system in Amazon FSx for Lustre.
type nonrec s3_file_system = {s3_uri : s3_schema_uri;The Amazon S3 URI that specifies the location in S3 where files are stored, which is mounted within the Studio environment. For example: s3://<bucket-name>/<prefix>/.
}A custom file system in Amazon S3. This is only supported in Amazon SageMaker Unified Studio.
type nonrec custom_file_system = | S3FileSystem of s3_file_systemA custom file system in Amazon S3. This is only supported in Amazon SageMaker Unified Studio.
*)| FSxLustreFileSystem of f_sx_lustre_file_systemA custom file system in Amazon FSx for Lustre.
*)| EFSFileSystem of efs_file_systemA custom file system in Amazon EFS.
*)A file system, created by you, that you assign to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
type nonrec custom_file_systems = custom_file_system listtype nonrec space_settings = {remote_access : feature_status option;A setting that enables or disables remote access for a SageMaker space. When enabled, this allows you to connect to the remote space from your local IDE.
*)custom_file_systems : custom_file_systems option;A file system, created by you, that you assign to a space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
*)space_managed_resources : feature_status option;If you enable this option, SageMaker AI creates the following resources on your behalf when you create the space:
space_storage_settings : space_storage_settings option;The storage settings for a space.
*)app_type : app_type option;The type of app created within the space.
If using the UpdateSpace API, you can't change the app type of your space by specifying a different value for this field.
*)jupyter_lab_app_settings : space_jupyter_lab_app_settings option;The settings for the JupyterLab application.
*)code_editor_app_settings : space_code_editor_app_settings option;The Code Editor application settings.
*)kernel_gateway_app_settings : kernel_gateway_app_settings option;jupyter_server_app_settings : jupyter_server_app_settings option;}A collection of space settings.
type nonrec update_space_request = {space_display_name : non_empty_string64 option;The name of the space that appears in the Amazon SageMaker Studio UI.
*)space_settings : space_settings option;A collection of space settings.
*)space_name : space_name;The name of the space.
*)domain_id : domain_id;The ID of the associated domain.
*)}type nonrec update_project_output = {project_arn : project_arn;The Amazon Resource Name (ARN) of the project.
*)}type nonrec provisioning_parameter = {value : provisioning_parameter_value option;The value of the provisioning parameter.
*)key : provisioning_parameter_key option;The key that identifies a provisioning parameter.
*)}A key value pair used when you provision a project as a service catalog product. For information, see What is Amazon Web Services Service Catalog.
type nonrec provisioning_parameters = provisioning_parameter listtype nonrec service_catalog_provisioning_update_details = {provisioning_parameters : provisioning_parameters option;A list of key value pairs that you specify when you provision a product.
*)provisioning_artifact_id : service_catalog_entity_id option;The ID of the provisioning artifact.
*)}Details that you specify to provision a service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog.
type nonrec tag = {value : tag_value;The tag value.
*)key : tag_key;The tag key. Tag keys must be unique per resource.
*)}A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.
type nonrec tag_list = tag listtype nonrec update_project_input = {template_providers_to_update : update_template_provider_list option;The template providers to update in the project.
*)service_catalog_provisioning_update_details : service_catalog_provisioning_update_details
option;The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don't provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.
*)project_description : entity_description option;The description for the project.
*)project_name : project_entity_name;The name of the project.
*)}type nonrec pipeline_version_id = Smaws_Lib.CoreTypes.Int64.ttype nonrec update_pipeline_version_response = {pipeline_version_id : pipeline_version_id option;The ID of the pipeline version.
*)pipeline_arn : pipeline_arn option;The Amazon Resource Name (ARN) of the pipeline.
*)}type nonrec update_pipeline_version_request = {pipeline_version_description : pipeline_version_description option;The description of the pipeline version.
*)pipeline_version_display_name : pipeline_version_name option;The display name of the pipeline version.
*)pipeline_version_id : pipeline_version_id;The pipeline version ID to update.
*)pipeline_arn : pipeline_arn;The Amazon Resource Name (ARN) of the pipeline.
*)}type nonrec update_pipeline_response = {pipeline_version_id : pipeline_version_id option;The ID of the pipeline version.
*)pipeline_arn : pipeline_arn option;The Amazon Resource Name (ARN) of the updated pipeline.
*)}type nonrec pipeline_definition_s3_location = {version_id : version_id option;Version Id of the pipeline definition file. If not specified, Amazon SageMaker will retrieve the latest version.
*)object_key : key;The object key (or key name) uniquely identifies the object in an S3 bucket.
*)bucket : bucket_name;Name of the S3 bucket.
*)}The location of the pipeline definition stored in Amazon S3.
type nonrec parallelism_configuration = {max_parallel_execution_steps : max_parallel_execution_steps;The max number of steps that can be executed in parallel.
*)}Configuration that controls the parallelism of the pipeline. By default, the parallelism configuration specified applies to all executions of the pipeline unless overridden.
type nonrec update_pipeline_request = {parallelism_configuration : parallelism_configuration option;If specified, it applies to all executions of this pipeline by default.
*)role_arn : role_arn option;The Amazon Resource Name (ARN) that the pipeline uses to execute.
*)pipeline_description : pipeline_description option;The description of the pipeline.
*)pipeline_definition_s3_location : pipeline_definition_s3_location option;The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.
*)pipeline_definition : pipeline_definition option;The JSON pipeline definition.
*)pipeline_display_name : pipeline_name option;The display name of the pipeline.
*)pipeline_name : pipeline_name;The name of the pipeline to update.
*)}type nonrec update_pipeline_execution_response = {pipeline_execution_arn : pipeline_execution_arn option;The Amazon Resource Name (ARN) of the updated pipeline execution.
*)}type nonrec update_pipeline_execution_request = {parallelism_configuration : parallelism_configuration option;This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.
*)pipeline_execution_display_name : pipeline_execution_name option;The display name of the pipeline execution.
*)pipeline_execution_description : pipeline_execution_description option;The description of the pipeline execution.
*)pipeline_execution_arn : pipeline_execution_arn;The Amazon Resource Name (ARN) of the pipeline execution.
*)}type nonrec update_partner_app_response = {arn : partner_app_arn option;The ARN of the SageMaker Partner AI App that was updated.
*)}type nonrec partner_app_maintenance_config = {maintenance_window_start : weekly_schedule_time_format option;The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. This value must take the following format: 3-letter-day:24-h-hour:minute. For example: TUE:03:30.
}Maintenance configuration settings for the SageMaker Partner AI App.
type nonrec partner_app_admin_user_list = non_empty_string256 listtype nonrec partner_app_arguments = (non_empty_string256 * string1024) listtype nonrec assigned_group_patterns_list = group_name_pattern listtype nonrec group_patterns_list = group_name_pattern listtype nonrec role_group_assignment = {group_patterns : group_patterns_list;A list of Amazon Web Services IAM Identity Center group patterns that should be assigned to the specified role. Group patterns support wildcard matching using *.
role_name : non_empty_string256;The name of the in-app role within the SageMaker Partner AI App. The specific roles available depend on the app type and version.
*)}Defines the mapping between an in-app role and the Amazon Web Services IAM Identity Center group patterns that should be assigned to that role within the SageMaker Partner AI App.
type nonrec role_group_assignments_list = role_group_assignment listtype nonrec partner_app_config = {role_group_assignments : role_group_assignments_list option;A map of in-app roles to Amazon Web Services IAM Identity Center group patterns. Groups assigned to specific roles receive those permissions, while groups in AssignedGroupPatterns but not in this map receive default in-app role depending on app type. Group patterns support wildcard matching using *. Currently supported by Fiddler version 1.3 and later with roles: ORG_MEMBER (default) and ORG_ADMIN.
assigned_group_patterns : assigned_group_patterns_list option;A list of Amazon Web Services IAM Identity Center group patterns that can access the SageMaker Partner AI App. Group names support wildcard matching using *. An empty list indicates the app will not use Identity Center group features. All groups specified in RoleGroupAssignments must match patterns in this list.
arguments : partner_app_arguments option;This is a map of required inputs for a SageMaker Partner AI App. Based on the application type, the map is populated with a key and value pair that is specific to the user and application.
*)admin_users : partner_app_admin_user_list option;The list of users that are given admin access to the SageMaker Partner AI App.
*)}Configuration settings for the SageMaker Partner AI App.
type nonrec update_partner_app_request = {client_token : client_token option;A unique token that guarantees that the call to this API is idempotent.
*)app_version : major_minor_version option;The semantic version to upgrade the SageMaker Partner AI App to. Must be the same semantic version returned in the AvailableUpgrade field from DescribePartnerApp. Version skipping and downgrades are not supported.
enable_auto_minor_version_upgrade : boolean_ option;When set to TRUE, the SageMaker Partner AI App is automatically upgraded to the latest minor version during the next scheduled maintenance window, if one is available.
enable_iam_session_based_identity : boolean_ option;When set to TRUE, the SageMaker Partner AI App sets the Amazon Web Services IAM session name or the authenticated IAM user as the identity of the SageMaker Partner AI App user.
application_config : partner_app_config option;Configuration settings for the SageMaker Partner AI App.
*)tier : non_empty_string64 option;Indicates the instance type and size of the cluster attached to the SageMaker Partner AI App.
*)maintenance_config : partner_app_maintenance_config option;Maintenance configuration settings for the SageMaker Partner AI App.
*)arn : partner_app_arn;The ARN of the SageMaker Partner AI App to update.
*)}type nonrec notebook_instance_lifecycle_hook = {content : notebook_instance_lifecycle_config_content option;A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
*)}Contains the notebook instance lifecycle configuration script.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.
View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
type nonrec notebook_instance_lifecycle_config_list =
notebook_instance_lifecycle_hook listtype nonrec update_notebook_instance_lifecycle_config_input = {on_start : notebook_instance_lifecycle_config_list option;The shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
*)on_create : notebook_instance_lifecycle_config_list option;The shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
*)notebook_instance_lifecycle_config_name : notebook_instance_lifecycle_config_name;The name of the lifecycle configuration.
*)}type nonrec instance_type = | ML_G6E_48XLARGE| ML_G6E_24XLARGE| ML_G6E_16XLARGE| ML_G6E_12XLARGE| ML_G6E_8XLARGE| ML_G6E_4XLARGE| ML_G6E_2XLARGE| ML_G6E_XLARGE| ML_P5EN_48XLARGE| ML_P5_4XLARGE| ML_G6_48XLARGE| ML_G6_24XLARGE| ML_G6_16XLARGE| ML_G6_12XLARGE| ML_G6_8XLARGE| ML_G6_4XLARGE| ML_G6_2XLARGE| ML_G6_XLARGE| ML_R6ID_32XLARGE| ML_R6ID_24XLARGE| ML_R6ID_16XLARGE| ML_R6ID_12XLARGE| ML_R6ID_8XLARGE| ML_R6ID_4XLARGE| ML_R6ID_2XLARGE| ML_R6ID_XLARGE| ML_R6ID_LARGE| ML_C6ID_32XLARGE| ML_C6ID_24XLARGE| ML_C6ID_16XLARGE| ML_C6ID_12XLARGE| ML_C6ID_8XLARGE| ML_C6ID_4XLARGE| ML_C6ID_2XLARGE| ML_C6ID_XLARGE| ML_C6ID_LARGE| ML_M6ID_32XLARGE| ML_M6ID_24XLARGE| ML_M6ID_16XLARGE| ML_M6ID_12XLARGE| ML_M6ID_8XLARGE| ML_M6ID_4XLARGE| ML_M6ID_2XLARGE| ML_M6ID_XLARGE| ML_M6ID_LARGE| ML_R7I_48XLARGE| ML_R7I_24XLARGE| ML_R7I_16XLARGE| ML_R7I_12XLARGE| ML_R7I_8XLARGE| ML_R7I_4XLARGE| ML_R7I_2XLARGE| ML_R7I_XLARGE| ML_R7I_LARGE| ML_R6I_32XLARGE| ML_R6I_24XLARGE| ML_R6I_16XLARGE| ML_R6I_12XLARGE| ML_R6I_8XLARGE| ML_R6I_4XLARGE| ML_R6I_2XLARGE| ML_R6I_XLARGE| ML_R6I_LARGE| ML_C7I_48XLARGE| ML_C7I_24XLARGE| ML_C7I_16XLARGE| ML_C7I_12XLARGE| ML_C7I_8XLARGE| ML_C7I_4XLARGE| ML_C7I_2XLARGE| ML_C7I_XLARGE| ML_C7I_LARGE| ML_C6I_32XLARGE| ML_C6I_24XLARGE| ML_C6I_16XLARGE| ML_C6I_12XLARGE| ML_C6I_8XLARGE| ML_C6I_4XLARGE| ML_C6I_2XLARGE| ML_C6I_XLARGE| ML_C6I_LARGE| ML_M7I_48XLARGE| ML_M7I_24XLARGE| ML_M7I_16XLARGE| ML_M7I_12XLARGE| ML_M7I_8XLARGE| ML_M7I_4XLARGE| ML_M7I_2XLARGE| ML_M7I_XLARGE| ML_M7I_LARGE| ML_M6I_32XLARGE| ML_M6I_24XLARGE| ML_M6I_16XLARGE| ML_M6I_12XLARGE| ML_M6I_8XLARGE| ML_M6I_4XLARGE| ML_M6I_2XLARGE| ML_M6I_XLARGE| ML_M6I_LARGE| ML_P6_B200_48XLARGE| ML_P5_48XLARGE| ML_P4DE_24XLARGE| ML_P4D_24XLARGE| ML_INF2_48XLARGE| ML_INF2_24XLARGE| ML_INF2_8XLARGE| ML_INF2_XLARGE| ML_TRN1N_32XLARGE| ML_TRN1_32XLARGE| ML_TRN1_2XLARGE| ML_INF1_24XLARGE| ML_INF1_6XLARGE| ML_INF1_2XLARGE| ML_INF1_XLARGE| ML_G5_48XLARGE| ML_G5_24XLARGE| ML_G5_12XLARGE| ML_G5_16XLARGE| ML_G5_8XLARGE| ML_G5_4XLARGE| ML_G5_2XLARGE| ML_G5_XLARGE| ML_R5_24XLARGE| ML_R5_16XLARGE| ML_R5_12XLARGE| ML_R5_8XLARGE| ML_R5_4XLARGE| ML_R5_2XLARGE| ML_R5_XLARGE| ML_R5_LARGE| ML_G4DN_16XLARGE| ML_G4DN_12XLARGE| ML_G4DN_8XLARGE| ML_G4DN_4XLARGE| ML_G4DN_2XLARGE| ML_G4DN_XLARGE| ML_P3DN_24XLARGE| ML_P3_16XLARGE| ML_P3_8XLARGE| ML_P3_2XLARGE| ML_P2_16XLARGE| ML_P2_8XLARGE| ML_P2_XLARGE| ML_C5D_18XLARGE| ML_C5D_9XLARGE| ML_C5D_4XLARGE| ML_C5D_2XLARGE| ML_C5D_XLARGE| ML_C5_18XLARGE| ML_C5_9XLARGE| ML_C5_4XLARGE| ML_C5_2XLARGE| ML_C5_XLARGE| ML_C4_8XLARGE| ML_C4_4XLARGE| ML_C4_2XLARGE| ML_C4_XLARGE| ML_M5D_24XLARGE| ML_M5D_16XLARGE| ML_M5D_12XLARGE| ML_M5D_8XLARGE| ML_M5D_4XLARGE| ML_M5D_2XLARGE| ML_M5D_XLARGE| ML_M5D_LARGE| ML_M5_24XLARGE| ML_M5_12XLARGE| ML_M5_4XLARGE| ML_M5_2XLARGE| ML_M5_XLARGE| ML_M4_16XLARGE| ML_M4_10XLARGE| ML_M4_4XLARGE| ML_M4_2XLARGE| ML_M4_XLARGE| ML_T3_2XLARGE| ML_T3_XLARGE| ML_T3_LARGE| ML_T3_MEDIUM| ML_T2_2XLARGE| ML_T2_XLARGE| ML_T2_LARGE| ML_T2_MEDIUMtype nonrec additional_code_repository_names_or_urls =
code_repository_name_or_url listtype nonrec notebook_instance_accelerator_types =
notebook_instance_accelerator_type listtype nonrec instance_metadata_service_configuration = {minimum_instance_metadata_service_version : minimum_instance_metadata_service_version;Indicates the minimum IMDS version that the notebook instance supports. When passed as part of CreateNotebookInstance, if no value is selected, then it defaults to IMDSv1. This means that both IMDSv1 and IMDSv2 are supported. If passed as part of UpdateNotebookInstance, there is no default.
}Information on the IMDS configuration of the notebook instance
type nonrec update_notebook_instance_input = {instance_metadata_service_configuration : instance_metadata_service_configuration
option;Information on the IMDS configuration of the notebook instance
*)root_access : root_access option;Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.
If you set this to Disabled, users don't have root access on the notebook instance, but lifecycle configuration scripts still run with root permissions.
disassociate_additional_code_repositories : disassociate_additional_code_repositories
option;A list of names or URLs of the default Git repositories to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.
*)disassociate_default_code_repository : disassociate_default_code_repository
option;The name or URL of the default Git repository to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.
*)disassociate_accelerator_types : disassociate_notebook_instance_accelerator_types
option;This parameter is no longer supported. Elastic Inference (EI) is no longer available.
This parameter was used to specify a list of the EI instance types to remove from this notebook instance.
*)accelerator_types : notebook_instance_accelerator_types option;This parameter is no longer supported. Elastic Inference (EI) is no longer available.
This parameter was used to specify a list of the EI instance types to associate with this notebook instance.
*)additional_code_repositories : additional_code_repository_names_or_urls option;An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.
*)default_code_repository : code_repository_name_or_url option;The Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.
*)volume_size_in_g_b : notebook_instance_volume_size_in_g_b option;The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB. ML storage volumes are encrypted, so SageMaker AI can't determine the amount of available free space on the volume. Because of this, you can increase the volume size when you update a notebook instance, but you can't decrease the volume size. If you want to decrease the size of the ML storage volume in use, create a new notebook instance with the desired size.
*)disassociate_lifecycle_config : disassociate_notebook_instance_lifecycle_config
option;Set to true to remove the notebook instance lifecycle configuration currently associated with the notebook instance. This operation is idempotent. If you specify a lifecycle configuration that is not associated with the notebook instance when you call this method, it does not throw an error.
lifecycle_config_name : notebook_instance_lifecycle_config_name option;The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
*)role_arn : role_arn option;The Amazon Resource Name (ARN) of the IAM role that SageMaker AI can assume to access the notebook instance. For more information, see SageMaker AI Roles.
To be able to pass this role to SageMaker AI, the caller of this API must have the iam:PassRole permission.
platform_identifier : platform_identifier option;The platform identifier of the notebook instance runtime environment.
*)ip_address_type : ip_address_type option;The IP address type for the notebook instance. Specify ipv4 for IPv4-only connectivity or dualstack for both IPv4 and IPv6 connectivity. The notebook instance must be stopped before updating this setting. When you specify dualstack, the subnet must support IPv6 addressing.
instance_type : instance_type option;The Amazon ML compute instance type.
*)notebook_instance_name : notebook_instance_name;The name of the notebook instance to update.
*)}type nonrec update_monitoring_schedule_response = {monitoring_schedule_arn : monitoring_schedule_arn;The Amazon Resource Name (ARN) of the monitoring schedule.
*)}type nonrec schedule_config = {data_analysis_end_time : string_ option;Sets the end time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify: "-PT1H".
The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the DataAnalysisStartTime parameter.
If you set ScheduleExpression to NOW, this parameter is required.
data_analysis_start_time : string_ option;Sets the start time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify: "-PT5H".
The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the DataAnalysisEndTime parameter.
If you set ScheduleExpression to NOW, this parameter is required.
schedule_expression : schedule_expression;A cron expression that describes details about the monitoring schedule.
The supported cron expressions are:
If you want to set the job to start every hour, use the following:
Hourly: cron(0 * ? * * *)
If you want to start the job daily:
cron(0 [00-23] ? * * *)
If you want to run the job one time, immediately, use the following keyword:
NOW
For example, the following are valid cron expressions:
cron(0 12 ? * * *)cron(0 0 ? * * *)To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
cron(0 17/12 ? * * *)cron(0 0/2 ? * * *)You can also specify the keyword NOW to run the monitoring job immediately, one time, without recurring.
}Configuration details about the monitoring schedule.
type nonrec monitoring_constraints_resource = {s3_uri : s3_uri option;The Amazon S3 URI for the constraints resource.
*)}The constraints resource for a monitoring job.
type nonrec monitoring_statistics_resource = {s3_uri : s3_uri option;The Amazon S3 URI for the statistics resource.
*)}The statistics resource for a monitoring job.
type nonrec monitoring_baseline_config = {statistics_resource : monitoring_statistics_resource option;The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
*)constraints_resource : monitoring_constraints_resource option;The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
*)baselining_job_name : processing_job_name option;The name of the job that performs baselining for the monitoring job.
*)}Configuration for monitoring constraints and monitoring statistics. These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.
type nonrec endpoint_input = {exclude_features_attribute : exclude_features_attribute option;The attributes of the input data to exclude from the analysis.
*)end_time_offset : monitoring_time_offset_string option;If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
*)start_time_offset : monitoring_time_offset_string option;If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
*)probability_threshold_attribute : probability_threshold_attribute option;The threshold for the class probability to be evaluated as a positive result.
*)probability_attribute : string_ option;In a classification problem, the attribute that represents the class probability.
*)inference_attribute : string_ option;The attribute of the input data that represents the ground truth label.
*)features_attribute : string_ option;The attributes of the input data that are the input features.
*)s3_data_distribution_type : processing_s3_data_distribution_type option;Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to FullyReplicated
s3_input_mode : processing_s3_input_mode option;Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File.
local_path : processing_local_path;Path to the filesystem where the endpoint data is available to the container.
*)endpoint_name : endpoint_name;An endpoint in customer's account which has enabled DataCaptureConfig enabled.
}Input object for the endpoint
type nonrec monitoring_csv_dataset_format = {header : boolean_ option;Indicates if the CSV data has a header.
*)}Represents the CSV dataset format used when running a monitoring job.
type nonrec monitoring_json_dataset_format = {line : boolean_ option;Indicates if the file should be read as a JSON object per line.
*)}Represents the JSON dataset format used when running a monitoring job.
type nonrec monitoring_dataset_format = {parquet : monitoring_parquet_dataset_format option;The Parquet dataset used in the monitoring job
*)json : monitoring_json_dataset_format option;The JSON dataset used in the monitoring job
*)csv : monitoring_csv_dataset_format option;The CSV dataset used in the monitoring job.
*)}Represents the dataset format used when running a monitoring job.
type nonrec batch_transform_input = {exclude_features_attribute : exclude_features_attribute option;The attributes of the input data to exclude from the analysis.
*)end_time_offset : monitoring_time_offset_string option;If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
*)start_time_offset : monitoring_time_offset_string option;If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
*)probability_threshold_attribute : probability_threshold_attribute option;The threshold for the class probability to be evaluated as a positive result.
*)probability_attribute : string_ option;In a classification problem, the attribute that represents the class probability.
*)inference_attribute : string_ option;The attribute of the input data that represents the ground truth label.
*)features_attribute : string_ option;The attributes of the input data that are the input features.
*)s3_data_distribution_type : processing_s3_data_distribution_type option;Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
s3_input_mode : processing_s3_input_mode option;Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File.
local_path : processing_local_path;Path to the filesystem where the batch transform data is available to the container.
*)dataset_format : monitoring_dataset_format;The dataset format for your batch transform job.
*)data_captured_destination_s3_uri : destination_s3_uri;The Amazon S3 location being used to capture the data.
*)}Input object for the batch transform job.
type nonrec monitoring_input = {batch_transform_input : batch_transform_input option;Input object for the batch transform job.
*)endpoint_input : endpoint_input option;The endpoint for a monitoring job.
*)}The inputs for a monitoring job.
type nonrec monitoring_inputs = monitoring_input listtype nonrec monitoring_s3_output = {s3_upload_mode : processing_s3_upload_mode option;Whether to upload the results of the monitoring job continuously or after the job completes.
*)local_path : processing_local_path;The local path to the Amazon S3 storage location where Amazon SageMaker AI saves the results of a monitoring job. LocalPath is an absolute path for the output data.
*)s3_uri : monitoring_s3_uri;A URI that identifies the Amazon S3 storage location where Amazon SageMaker AI saves the results of a monitoring job.
*)}Information about where and how you want to store the results of a monitoring job.
type nonrec monitoring_output = {s3_output : monitoring_s3_output;The Amazon S3 storage location where the results of a monitoring job are saved.
*)}The output object for a monitoring job.
type nonrec monitoring_outputs = monitoring_output listtype nonrec monitoring_output_config = {kms_key_id : kms_key_id option;The Key Management Service (KMS) key that Amazon SageMaker AI uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
*)monitoring_outputs : monitoring_outputs;Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
*)}The output configuration for monitoring jobs.
type nonrec monitoring_cluster_config = {volume_kms_key_id : kms_key_id option;The Key Management Service (KMS) key that Amazon SageMaker AI uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
*)volume_size_in_g_b : processing_volume_size_in_g_b;The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
*)instance_type : processing_instance_type;The ML compute instance type for the processing job.
*)instance_count : processing_instance_count;The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
*)}Configuration for the cluster used to run model monitoring jobs.
type nonrec monitoring_resources = {cluster_config : monitoring_cluster_config;The configuration for the cluster resources used to run the processing job.
*)}Identifies the resources to deploy for a monitoring job.
type nonrec container_entrypoint = container_entrypoint_string listtype nonrec monitoring_container_arguments = container_argument listtype nonrec monitoring_app_specification = {post_analytics_processor_source_uri : s3_uri option;An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
*)record_preprocessor_source_uri : s3_uri option;An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
*)container_arguments : monitoring_container_arguments option;An array of arguments for the container used to run the monitoring job.
*)container_entrypoint : container_entrypoint option;Specifies the entrypoint for a container used to run the monitoring job.
*)image_uri : image_uri;The container image to be run by the monitoring job.
*)}Container image configuration object for the monitoring job.
type nonrec monitoring_stopping_condition = {max_runtime_in_seconds : monitoring_max_runtime_in_seconds;The maximum runtime allowed in seconds.
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
}A time limit for how long the monitoring job is allowed to run before stopping.
type nonrec monitoring_environment_map =
(processing_environment_key * processing_environment_value) listtype nonrec network_config = {vpc_config : vpc_config option;enable_network_isolation : boolean_ option;Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
*)enable_inter_container_traffic_encryption : boolean_ option;Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
}Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
type nonrec monitoring_job_definition = {role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.
*)network_config : network_config option;Specifies networking options for an monitoring job.
*)environment : monitoring_environment_map option;Sets the environment variables in the Docker container.
*)stopping_condition : monitoring_stopping_condition option;Specifies a time limit for how long the monitoring job is allowed to run.
*)monitoring_app_specification : monitoring_app_specification;Configures the monitoring job to run a specified Docker container image.
*)monitoring_resources : monitoring_resources;Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
*)monitoring_output_config : monitoring_output_config;The array of outputs from the monitoring job to be uploaded to Amazon S3.
*)monitoring_inputs : monitoring_inputs;The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker AI Endpoint.
*)baseline_config : monitoring_baseline_config option;Baseline configuration used to validate that the data conforms to the specified constraints and statistics
*)}Defines the monitoring job.
type nonrec monitoring_schedule_config = {monitoring_type : monitoring_type option;The type of the monitoring job definition to schedule.
*)monitoring_job_definition_name : monitoring_job_definition_name option;The name of the monitoring job definition to schedule.
*)monitoring_job_definition : monitoring_job_definition option;Defines the monitoring job.
*)schedule_config : schedule_config option;Configures the monitoring schedule.
*)}Configures the monitoring schedule and defines the monitoring job.
type nonrec update_monitoring_schedule_request = {monitoring_schedule_config : monitoring_schedule_config;The configuration object that specifies the monitoring schedule and defines the monitoring job.
*)monitoring_schedule_name : monitoring_schedule_name;The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
*)}type nonrec update_monitoring_alert_response = {monitoring_alert_name : monitoring_alert_name option;The name of a monitoring alert.
*)monitoring_schedule_arn : monitoring_schedule_arn;The Amazon Resource Name (ARN) of the monitoring schedule.
*)}type nonrec update_monitoring_alert_request = {evaluation_period : monitoring_evaluation_period;The number of most recent monitoring executions to consider when evaluating alert status.
*)datapoints_to_alert : monitoring_datapoints_to_alert;Within EvaluationPeriod, how many execution failures will raise an alert.
monitoring_alert_name : monitoring_alert_name;The name of a monitoring alert.
*)monitoring_schedule_name : monitoring_schedule_name;The name of a monitoring schedule.
*)}type nonrec update_model_package_output = {model_package_arn : model_package_arn;The Amazon Resource Name (ARN) of the model.
*)}type nonrec customer_metadata_map =
(customer_metadata_key * customer_metadata_value) listtype nonrec customer_metadata_key_list = customer_metadata_key listtype nonrec model_access_config = {accept_eula : accept_eula;Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
}The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig.
type nonrec inference_hub_access_config = {hub_content_arn : hub_content_arn;The ARN of the hub content for which deployment access is allowed.
*)}Configuration information specifying which hub contents have accessible deployment options.
type nonrec s3_model_data_source = {manifest_etag : string_ option;The ETag associated with Manifest S3 URI.
*)e_tag : string_ option;The ETag associated with S3 URI.
*)manifest_s3_uri : s3_model_uri option;The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations.
*)hub_access_config : inference_hub_access_config option;Configuration information for hub access.
*)model_access_config : model_access_config option;Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
compression_type : model_compression_type;Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.Do not use any of the following as file names or directory names:
.)..)s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).s3_data_type : s3_model_data_type;Specifies the type of ML model data to deploy.
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.
s3_uri : s3_model_uri;Specifies the S3 path of ML model data to deploy.
*)}Specifies the S3 location of ML model data to deploy.
type nonrec model_data_source = {s3_data_source : s3_model_data_source option;Specifies the S3 location of ML model data to deploy.
*)}Specifies the location of ML model data to deploy. If specified, you must specify one and only one of the available data sources.
type nonrec environment_map = (environment_key * environment_value) listtype nonrec model_input = {data_input_config : data_input_config;The input configuration object for the model.
*)}Input object for the model.
type nonrec additional_model_data_source = {s3_data_source : s3_model_data_source;channel_name : additional_model_channel_name;A custom name for this AdditionalModelDataSource object.
}Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.
type nonrec additional_model_data_sources = additional_model_data_source listtype nonrec additional_s3_data_source = {e_tag : string_ option;The ETag associated with S3 URI.
*)compression_type : compression_type option;The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
s3_uri : s3_uri;The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
*)s3_data_type : additional_s3_data_source_data_type;The data type of the additional data source that you specify for use in inference or training.
*)}A data source used for training or inference that is in addition to the input dataset or model data.
type nonrec base_model = {recipe_name : recipe_name option;The recipe name of the base model.
*)hub_content_version : hub_content_version option;The hub content version of the base model.
*)hub_content_name : hub_content_name option;The hub content name of the base model.
*)}Identifies the foundation model that was used as the starting point for model customization.
type nonrec model_package_container_definition = {base_model : base_model option;Identifies the foundation model that was used as the starting point for model customization.
*)is_checkpoint : boolean_ option;Specifies whether the model data is a training checkpoint.
*)model_data_e_tag : string_ option;The ETag associated with Model Data URL.
*)additional_s3_data_source : additional_s3_data_source option;The additional data source that is used during inference in the Docker container for your model package.
*)additional_model_data_sources : additional_model_data_sources option;Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModelPackage action.
nearest_model_name : string_ option;The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.
framework_version : model_package_framework_version option;The framework version of the Model Package Container Image.
*)framework : string_ option;The machine learning framework of the model package container image.
*)model_input : model_input option;A structure with Model Input details.
*)environment : environment_map option;The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
product_id : product_id option;The Amazon Web Services Marketplace product ID of the model package.
*)model_data_source : model_data_source option;Specifies the location of ML model data to deploy during endpoint creation.
*)model_data_url : url option;The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
The model artifacts must be in an S3 bucket that is in the same region as the model package.
*)image_digest : image_digest option;An MD5 hash of the training algorithm that identifies the Docker image used for training.
*)image : container_image option;The Amazon Elastic Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
container_hostname : container_hostname option;The DNS host name for the Docker container.
*)}Describes the Docker container for the model package.
type nonrec model_package_container_definition_list =
model_package_container_definition listtype nonrec transform_instance_type = | ML_G6_48XLARGE| ML_G6_24XLARGE| ML_G6_16XLARGE| ML_G6_12XLARGE| ML_G6_8XLARGE| ML_G6_4XLARGE| ML_G6_2XLARGE| ML_G6_XLARGE| ML_INF2_48XLARGE| ML_INF2_24XLARGE| ML_INF2_8XLARGE| ML_INF2_XLARGE| ML_TRN1_32XLARGE| ML_TRN1_2XLARGE| ML_G5_48XLARGE| ML_G5_24XLARGE| ML_G5_16XLARGE| ML_G5_12XLARGE| ML_G5_8XLARGE| ML_G5_4XLARGE| ML_G5_2XLARGE| ML_G5_XLARGE| ML_G4DN_16XLARGE| ML_G4DN_12XLARGE| ML_G4DN_8XLARGE| ML_G4DN_4XLARGE| ML_G4DN_2XLARGE| ML_G4DN_XLARGE| ML_R7I_48XLARGE| ML_R7I_24XLARGE| ML_R7I_16XLARGE| ML_R7I_12XLARGE| ML_R7I_8XLARGE| ML_R7I_4XLARGE| ML_R7I_2XLARGE| ML_R7I_XLARGE| ML_R7I_LARGE| ML_C7I_48XLARGE| ML_C7I_24XLARGE| ML_C7I_16XLARGE| ML_C7I_12XLARGE| ML_C7I_8XLARGE| ML_C7I_4XLARGE| ML_C7I_2XLARGE| ML_C7I_XLARGE| ML_C7I_LARGE| ML_M7I_48XLARGE| ML_M7I_24XLARGE| ML_M7I_16XLARGE| ML_M7I_12XLARGE| ML_M7I_8XLARGE| ML_M7I_4XLARGE| ML_M7I_2XLARGE| ML_M7I_XLARGE| ML_M7I_LARGE| ML_R6I_32XLARGE| ML_R6I_24XLARGE| ML_R6I_16XLARGE| ML_R6I_12XLARGE| ML_R6I_8XLARGE| ML_R6I_4XLARGE| ML_R6I_2XLARGE| ML_R6I_XLARGE| ML_R6I_LARGE| ML_C6I_32XLARGE| ML_C6I_24XLARGE| ML_C6I_16XLARGE| ML_C6I_12XLARGE| ML_C6I_8XLARGE| ML_C6I_4XLARGE| ML_C6I_2XLARGE| ML_C6I_XLARGE| ML_C6I_LARGE| ML_M6I_32XLARGE| ML_M6I_24XLARGE| ML_M6I_16XLARGE| ML_M6I_12XLARGE| ML_M6I_8XLARGE| ML_M6I_4XLARGE| ML_M6I_2XLARGE| ML_M6I_XLARGE| ML_M6I_LARGE| ML_M5_24XLARGE| ML_M5_12XLARGE| ML_M5_4XLARGE| ML_M5_2XLARGE| ML_M5_XLARGE| ML_M5_LARGE| ML_C5_18XLARGE| ML_C5_9XLARGE| ML_C5_4XLARGE| ML_C5_2XLARGE| ML_C5_XLARGE| ML_P3_16XLARGE| ML_P3_8XLARGE| ML_P3_2XLARGE| ML_P2_16XLARGE| ML_P2_8XLARGE| ML_P2_XLARGE| ML_C4_8XLARGE| ML_C4_4XLARGE| ML_C4_2XLARGE| ML_C4_XLARGE| ML_M4_16XLARGE| ML_M4_10XLARGE| ML_M4_4XLARGE| ML_M4_2XLARGE| ML_M4_XLARGEtype nonrec transform_instance_types = transform_instance_type listtype nonrec production_variant_instance_type = | ML_P5_4XLARGE| ML_P6E_GB200_36XLARGE| ML_P6_B300_48XLARGE| ML_P6_B200_48XLARGE| ML_C6IN_32XLARGE| ML_C6IN_24XLARGE| ML_C6IN_16XLARGE| ML_C6IN_12XLARGE| ML_C6IN_8XLARGE| ML_C6IN_4XLARGE| ML_C6IN_2XLARGE| ML_C6IN_XLARGE| ML_C6IN_LARGE| ML_M8G_48XLARGE| ML_M8G_24XLARGE| ML_M8G_16XLARGE| ML_M8G_12XLARGE| ML_M8G_8XLARGE| ML_M8G_4XLARGE| ML_M8G_2XLARGE| ML_M8G_XLARGE| ML_M8G_LARGE| ML_M8G_MEDIUM| ML_R7GD_16XLARGE| ML_R7GD_12XLARGE| ML_R7GD_8XLARGE| ML_R7GD_4XLARGE| ML_R7GD_2XLARGE| ML_R7GD_XLARGE| ML_R7GD_LARGE| ML_R7GD_MEDIUM| ML_C8G_48XLARGE| ML_C8G_24XLARGE| ML_C8G_16XLARGE| ML_C8G_12XLARGE| ML_C8G_8XLARGE| ML_C8G_4XLARGE| ML_C8G_2XLARGE| ML_C8G_XLARGE| ML_C8G_LARGE| ML_C8G_MEDIUM| ML_R7I_48XLARGE| ML_R7I_24XLARGE| ML_R7I_16XLARGE| ML_R7I_12XLARGE| ML_R7I_8XLARGE| ML_R7I_4XLARGE| ML_R7I_2XLARGE| ML_R7I_XLARGE| ML_R7I_LARGE| ML_C7I_48XLARGE| ML_C7I_24XLARGE| ML_C7I_16XLARGE| ML_C7I_12XLARGE| ML_C7I_8XLARGE| ML_C7I_4XLARGE| ML_C7I_2XLARGE| ML_C7I_XLARGE| ML_C7I_LARGE| ML_M7I_48XLARGE| ML_M7I_24XLARGE| ML_M7I_16XLARGE| ML_M7I_12XLARGE| ML_M7I_8XLARGE| ML_M7I_4XLARGE| ML_M7I_2XLARGE| ML_M7I_XLARGE| ML_M7I_LARGE| ML_P5EN_48XLARGE| ML_P5E_48XLARGE| ML_P5_48XLARGE| ML_INF2_48XLARGE| ML_INF2_24XLARGE| ML_INF2_8XLARGE| ML_INF2_XLARGE| ML_TRN2_48XLARGE| ML_TRN1N_32XLARGE| ML_TRN1_32XLARGE| ML_TRN1_2XLARGE| ML_P4DE_24XLARGE| ML_R6GD_16XLARGE| ML_R6GD_12XLARGE| ML_R6GD_8XLARGE| ML_R6GD_4XLARGE| ML_R6GD_2XLARGE| ML_R6GD_XLARGE| ML_R6GD_LARGE| ML_R6G_16XLARGE| ML_R6G_12XLARGE| ML_R6G_8XLARGE| ML_R6G_4XLARGE| ML_R6G_2XLARGE| ML_R6G_XLARGE| ML_R6G_LARGE| ML_C6GN_16XLARGE| ML_C6GN_12XLARGE| ML_C6GN_8XLARGE| ML_C6GN_4XLARGE| ML_C6GN_2XLARGE| ML_C6GN_XLARGE| ML_C6GN_LARGE| ML_C6GD_16XLARGE| ML_C6GD_12XLARGE| ML_C6GD_8XLARGE| ML_C6GD_4XLARGE| ML_C6GD_2XLARGE| ML_C6GD_XLARGE| ML_C6GD_LARGE| ML_C6G_16XLARGE| ML_C6G_12XLARGE| ML_C6G_8XLARGE| ML_C6G_4XLARGE| ML_C6G_2XLARGE| ML_C6G_XLARGE| ML_C6G_LARGE| ML_M6GD_16XLARGE| ML_M6GD_12XLARGE| ML_M6GD_8XLARGE| ML_M6GD_4XLARGE| ML_M6GD_2XLARGE| ML_M6GD_XLARGE| ML_M6GD_LARGE| ML_M6G_16XLARGE| ML_M6G_12XLARGE| ML_M6G_8XLARGE| ML_M6G_4XLARGE| ML_M6G_2XLARGE| ML_M6G_XLARGE| ML_M6G_LARGE| ML_C7G_16XLARGE| ML_C7G_12XLARGE| ML_C7G_8XLARGE| ML_C7G_4XLARGE| ML_C7G_2XLARGE| ML_C7G_XLARGE| ML_C7G_LARGE| ML_P4D_24XLARGE| ML_G7E_48XLARGE| ML_G7E_24XLARGE| ML_G7E_12XLARGE| ML_G7E_8XLARGE| ML_G7E_4XLARGE| ML_G7E_2XLARGE| ML_G6E_48XLARGE| ML_G6E_24XLARGE| ML_G6E_16XLARGE| ML_G6E_12XLARGE| ML_G6E_8XLARGE| ML_G6E_4XLARGE| ML_G6E_2XLARGE| ML_G6E_XLARGE| ML_R8G_48XLARGE| ML_R8G_24XLARGE| ML_R8G_16XLARGE| ML_R8G_12XLARGE| ML_R8G_8XLARGE| ML_R8G_4XLARGE| ML_R8G_2XLARGE| ML_R8G_XLARGE| ML_R8G_LARGE| ML_R8G_MEDIUM| ML_G6_48XLARGE| ML_G6_24XLARGE| ML_G6_16XLARGE| ML_G6_12XLARGE| ML_G6_8XLARGE| ML_G6_4XLARGE| ML_G6_2XLARGE| ML_G6_XLARGE| ML_G5_48XLARGE| ML_G5_24XLARGE| ML_G5_16XLARGE| ML_G5_12XLARGE| ML_G5_8XLARGE| ML_G5_4XLARGE| ML_G5_2XLARGE| ML_G5_XLARGE| ML_R6I_32XLARGE| ML_R6I_24XLARGE| ML_R6I_16XLARGE| ML_R6I_12XLARGE| ML_R6I_8XLARGE| ML_R6I_4XLARGE| ML_R6I_2XLARGE| ML_R6I_XLARGE| ML_R6I_LARGE| ML_M6I_32XLARGE| ML_M6I_24XLARGE| ML_M6I_16XLARGE| ML_M6I_12XLARGE| ML_M6I_8XLARGE| ML_M6I_4XLARGE| ML_M6I_2XLARGE| ML_M6I_XLARGE| ML_M6I_LARGE| ML_C6I_32XLARGE| ML_C6I_24XLARGE| ML_C6I_16XLARGE| ML_C6I_12XLARGE| ML_C6I_8XLARGE| ML_C6I_4XLARGE| ML_C6I_2XLARGE| ML_C6I_XLARGE| ML_C6I_LARGE| ML_DL1_24XLARGE| ML_INF1_24XLARGE| ML_INF1_6XLARGE| ML_INF1_2XLARGE| ML_INF1_XLARGE| ML_R5D_24XLARGE| ML_R5D_12XLARGE| ML_R5D_4XLARGE| ML_R5D_2XLARGE| ML_R5D_XLARGE| ML_R5D_LARGE| ML_R5_24XLARGE| ML_R5_12XLARGE| ML_R5_4XLARGE| ML_R5_2XLARGE| ML_R5_XLARGE| ML_R5_LARGE| ML_G4DN_16XLARGE| ML_G4DN_12XLARGE| ML_G4DN_8XLARGE| ML_G4DN_4XLARGE| ML_G4DN_2XLARGE| ML_G4DN_XLARGE| ML_C5D_18XLARGE| ML_C5D_9XLARGE| ML_C5D_4XLARGE| ML_C5D_2XLARGE| ML_C5D_XLARGE| ML_C5D_LARGE| ML_C5_18XLARGE| ML_C5_9XLARGE| ML_C5_4XLARGE| ML_C5_2XLARGE| ML_C5_XLARGE| ML_C5_LARGE| ML_P3_16XLARGE| ML_P3_8XLARGE| ML_P3_2XLARGE| ML_P2_16XLARGE| ML_P2_8XLARGE| ML_P2_XLARGE| ML_C4_8XLARGE| ML_C4_4XLARGE| ML_C4_2XLARGE| ML_C4_XLARGE| ML_C4_LARGE| ML_M5D_24XLARGE| ML_M5D_12XLARGE| ML_M5D_4XLARGE| ML_M5D_2XLARGE| ML_M5D_XLARGE| ML_M5D_LARGE| ML_M5_24XLARGE| ML_M5_12XLARGE| ML_M5_4XLARGE| ML_M5_2XLARGE| ML_M5_XLARGE| ML_M5_LARGE| ML_M4_16XLARGE| ML_M4_10XLARGE| ML_M4_4XLARGE| ML_M4_2XLARGE| ML_M4_XLARGE| ML_T2_2XLARGE| ML_T2_XLARGE| ML_T2_LARGE| ML_T2_MEDIUMtype nonrec realtime_inference_instance_types =
production_variant_instance_type listtype nonrec content_types = content_type listtype nonrec response_mime_types = response_mime_type listtype nonrec additional_inference_specification_definition = {supported_response_mime_types : response_mime_types option;The supported MIME types for the output data.
*)supported_content_types : content_types option;The supported MIME types for the input data.
*)supported_realtime_inference_instance_types : realtime_inference_instance_types
option;A list of the instance types that are used to generate inferences in real-time.
*)supported_transform_instance_types : transform_instance_types option;A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
*)containers : model_package_container_definition_list;The Amazon ECR registry path of the Docker image that contains the inference code.
*)description : entity_description option;A description of the additional Inference specification
*)name : entity_name;A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.
*)}A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package
type nonrec additional_inference_specifications =
additional_inference_specification_definition listtype nonrec inference_specification = {supported_response_mime_types : response_mime_types option;The supported MIME types for the output data.
*)supported_content_types : content_types option;The supported MIME types for the input data.
*)supported_realtime_inference_instance_types : realtime_inference_instance_types
option;A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
*)supported_transform_instance_types : transform_instance_types option;A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
*)containers : model_package_container_definition_list;The Amazon ECR registry path of the Docker image that contains the inference code.
*)}Defines how to perform inference generation after a training job is run.
type nonrec model_package_model_card = {model_card_status : model_card_status option;The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
Draft: The model card is a work in progress.PendingReview: The model card is pending review.Approved: The model card is approved.Archived: The model card is archived. No more updates can be made to the model card content. If you try to update the model card content, you will receive the message Model Card is in Archived state.model_card_content : model_card_content option;The content of the model card. The content must follow the schema described in Model Package Model Card Schema.
*)}The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.
type nonrec model_life_cycle = {stage_description : stage_description option;Describes the stage related details.
*)stage_status : entity_name;The current status of a stage in model life cycle.
*)stage : entity_name;The current stage in the model life cycle.
*)}A structure describing the current state of the model in its life cycle.
type nonrec update_model_package_input = {client_token : client_token option;A unique token that guarantees that the call to this API is idempotent.
*)model_life_cycle : model_life_cycle option;A structure describing the current state of the model in its life cycle.
*)model_card : model_package_model_card option;The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.
source_uri : model_package_source_uri option;The URI of the source for the model package.
*)inference_specification : inference_specification option;Specifies details about inference jobs that you can run with models based on this model package, including the following information:
additional_inference_specifications_to_add : additional_inference_specifications
option;An array of additional Inference Specification objects to be added to the existing array additional Inference Specification. Total number of additional Inference Specifications can not exceed 15. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
*)customer_metadata_properties_to_remove : customer_metadata_key_list option;The metadata properties associated with the model package versions to remove.
*)customer_metadata_properties : customer_metadata_map option;The metadata properties associated with the model package versions.
*)approval_description : approval_description option;A description for the approval status of the model.
*)model_package_registration_type : model_package_registration_type option;The package registration type of the model package input.
*)model_approval_status : model_approval_status option;The approval status of the model.
*)model_package_arn : model_package_arn;The Amazon Resource Name (ARN) of the model package.
*)}type nonrec update_model_card_response = {model_card_arn : model_card_arn;The Amazon Resource Name (ARN) of the updated model card.
*)}type nonrec update_model_card_request = {model_card_status : model_card_status option;The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
Draft: The model card is a work in progress.PendingReview: The model card is pending review.Approved: The model card is approved.Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.content : model_card_content option;The updated model card content. Content must be in model card JSON schema and provided as a string.
When updating model card content, be sure to include the full content and not just updated content.
*)model_card_name : model_card_name_or_arn;The name or Amazon Resource Name (ARN) of the model card to update.
*)}type nonrec update_mlflow_tracking_server_response = {tracking_server_arn : tracking_server_arn option;The ARN of the updated MLflow Tracking Server.
*)}type nonrec update_mlflow_tracking_server_request = {s3_bucket_owner_verification : boolean_ option;Whether to enable or disable Amazon S3 Bucket Owenrship Verifaction whenever the MLflow Tracking Server interacts with Amazon Amazon S3.
*)s3_bucket_owner_account_id : account_id option;The new expected Amazon Web Services account ID that owns the Amazon S3 bucket for artifact storage.
*)weekly_maintenance_window_start : weekly_maintenance_window_start option;The new weekly maintenance window start day and time to update. The maintenance window day and time should be in Coordinated Universal Time (UTC) 24-hour standard time. For example: TUE:03:30.
*)automatic_model_registration : boolean_ option;Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to True. To disable automatic model registration, set this value to False. If not specified, AutomaticModelRegistration defaults to False
tracking_server_size : tracking_server_size option;The new size for the MLflow Tracking Server.
*)artifact_store_uri : s3_uri option;The new S3 URI for the general purpose bucket to use as the artifact store for the MLflow Tracking Server.
*)tracking_server_name : tracking_server_name;The name of the MLflow Tracking Server to update.
*)}type nonrec update_mlflow_app_response = {arn : mlflow_app_arn option;The ARN of the updated MLflow App.
*)}type nonrec default_domain_id_list = domain_id listtype nonrec update_mlflow_app_request = {account_default_status : account_default_status option;Indicates whether this this MLflow App is the default for the account.
*)default_domain_id_list : default_domain_id_list option;List of SageMaker Domain IDs for which this MLflow App is the default.
*)weekly_maintenance_window_start : weekly_maintenance_window_start option;The new weekly maintenance window start day and time to update. The maintenance window day and time should be in Coordinated Universal Time (UTC) 24-hour standard time. For example: TUE:03:30.
*)model_registration_mode : model_registration_mode option;Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to AutoModelRegistrationEnabled. To disable automatic model registration, set this value to AutoModelRegistrationDisabled. If not specified, AutomaticModelRegistration defaults to AutoModelRegistrationEnabled
artifact_store_uri : s3_uri option;The new S3 URI for the general purpose bucket to use as the artifact store for the MLflow App.
*)name : mlflow_app_name option;The name of the MLflow App to update.
*)arn : mlflow_app_arn;The ARN of the MLflow App to update.
*)}type nonrec update_inference_experiment_response = {inference_experiment_arn : inference_experiment_arn;The ARN of the updated inference experiment.
*)}type nonrec inference_experiment_schedule = {end_time : timestamp option;The timestamp at which the inference experiment ended or will end.
*)start_time : timestamp option;The timestamp at which the inference experiment started or will start.
*)}The start and end times of an inference experiment.
The maximum duration that you can set for an inference experiment is 30 days.
type nonrec real_time_inference_config = {instance_count : task_count;The number of instances of the type specified by InstanceType.
instance_type : production_variant_instance_type;The instance type the model is deployed to.
*)}The infrastructure configuration for deploying the model to a real-time inference endpoint.
type nonrec model_infrastructure_config = {real_time_inference_config : real_time_inference_config;The infrastructure configuration for deploying the model to real-time inference.
*)infrastructure_type : model_infrastructure_type;The inference option to which to deploy your model. Possible values are the following:
RealTime: Deploy to real-time inference.}The configuration for the infrastructure that the model will be deployed to.
type nonrec model_variant_config = {infrastructure_config : model_infrastructure_config;The configuration for the infrastructure that the model will be deployed to.
*)variant_name : model_variant_name;The name of the variant.
*)model_name : model_name;The name of the Amazon SageMaker Model entity.
*)}Contains information about the deployment options of a model.
type nonrec model_variant_config_list = model_variant_config listtype nonrec csv_content_types = csv_content_type listtype nonrec json_content_types = json_content_type listtype nonrec capture_content_type_header = {json_content_types : json_content_types option;The list of all content type headers that SageMaker AI will treat as JSON and capture accordingly.
*)csv_content_types : csv_content_types option;The list of all content type headers that Amazon SageMaker AI will treat as CSV and capture accordingly.
*)}Configuration specifying how to treat different headers. If no headers are specified Amazon SageMaker AI will by default base64 encode when capturing the data.
type nonrec inference_experiment_data_storage_config = {content_type : capture_content_type_header option;kms_key : kms_key_id option;The Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3 server-side encryption.
*)destination : destination_s3_uri;The Amazon S3 bucket where the inference request and response data is stored.
*)}The Amazon S3 location and configuration for storing inference request and response data.
type nonrec shadow_model_variant_config = {sampling_percentage : percentage;The percentage of inference requests that Amazon SageMaker replicates from the production variant to the shadow variant.
*)shadow_model_variant_name : model_variant_name;The name of the shadow variant.
*)}The name and sampling percentage of a shadow variant.
type nonrec shadow_model_variant_config_list = shadow_model_variant_config listtype nonrec shadow_mode_config = {shadow_model_variants : shadow_model_variant_config_list;List of shadow variant configurations.
*)source_model_variant_name : model_variant_name;The name of the production variant, which takes all the inference requests.
*)}The configuration of ShadowMode inference experiment type, which specifies a production variant to take all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant it also specifies the percentage of requests that Amazon SageMaker replicates.
type nonrec update_inference_experiment_request = {shadow_mode_config : shadow_mode_config option;The configuration of ShadowMode inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.
data_storage_config : inference_experiment_data_storage_config option;The Amazon S3 location and configuration for storing inference request and response data.
*)model_variants : model_variant_config_list option;An array of ModelVariantConfig objects. There is one for each variant, whose infrastructure configuration you want to update.
description : inference_experiment_description option;The description of the inference experiment.
*)schedule : inference_experiment_schedule option;The duration for which the inference experiment will run. If the status of the inference experiment is Created, then you can update both the start and end dates. If the status of the inference experiment is Running, then you can update only the end date.
name : inference_experiment_name;The name of the inference experiment to be updated.
*)}type nonrec update_inference_component_runtime_config_output = {inference_component_arn : inference_component_arn;The Amazon Resource Name (ARN) of the inference component.
*)}type nonrec inference_component_runtime_config = {copy_count : inference_component_copy_count;The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.
*)}Runtime settings for a model that is deployed with an inference component.
type nonrec update_inference_component_runtime_config_input = {desired_runtime_config : inference_component_runtime_config;Runtime settings for a model that is deployed with an inference component.
*)inference_component_name : inference_component_name;The name of the inference component to update.
*)}type nonrec update_inference_component_output = {inference_component_arn : inference_component_arn;The Amazon Resource Name (ARN) of the inference component.
*)}type nonrec metric_publish_frequency_in_seconds = | FREQ_300_SMetrics publishing at 300 seconds frequency
*)| FREQ_240_SMetrics publishing at 240 seconds frequency
*)| FREQ_180_SMetrics publishing at 180 seconds frequency
*)| FREQ_120_SMetrics publishing at 120 seconds frequency
*)| FREQ_60_SMetrics publishing at 60 seconds frequency
*)| FREQ_30_SMetrics publishing at 30 seconds frequency
*)| FREQ_10_SMetrics publishing at 10 seconds frequency
*)type nonrec metrics_endpoint = {metric_publish_frequency_in_seconds : metric_publish_frequency_in_seconds
option;The interval, in seconds, at which container metrics scraped from the endpoint are published to Amazon CloudWatch. Valid values: 10, 30, 60, 120, 180, 240, 300. Defaults to 60.
metrics_endpoint_path : metrics_endpoint_path;The path to the metrics endpoint exposed by the container. For example, /metrics or /server/metrics. The path must start with / and can contain alphanumeric characters, forward slashes, underscores, hyphens, and periods. Maximum length is 256 characters. If not specified, defaults to /metrics.
}Specifies a metrics endpoint for a container, including the path where the container exposes Prometheus-formatted metrics and the frequency at which to publish them to Amazon CloudWatch.
type nonrec metrics_endpoint_list = metrics_endpoint listtype nonrec container_metrics_config = {metrics_endpoints : metrics_endpoint_list option;A list of metrics endpoints to scrape from the container. Each endpoint specifies the path where the container exposes Prometheus-formatted metrics and the frequency at which to publish them. You can specify a maximum of 1 endpoint.
*)}The configuration for container-level metrics scraping. Use this configuration to specify a custom metrics endpoint path and publishing frequency for container metrics. When EnableDetailedObservability is set to True in MetricsConfig, metrics are scraped from the container's Prometheus endpoint. If this configuration is not provided, the default path /metrics on port 8080 is used with a default publishing frequency of 60 seconds. For first-party and Deep Learning Containers (DLC), the endpoint path is determined automatically and this configuration is optional.
type nonrec inference_component_container_specification = {container_metrics_config : container_metrics_config option;The configuration for container metrics scraping. Specifies the metrics endpoint path and publishing frequency for the inference component's container. If not specified when EnableDetailedObservability is True, the default path /metrics on port 8080 is used. For first-party and Deep Learning Containers (DLC), the endpoint path is determined automatically and this configuration is optional.
environment : environment_map option;The environment variables to set in the Docker container. Each key and value in the Environment string-to-string map can have length of up to 1024. We support up to 16 entries in the map.
*)artifact_url : url option;The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
*)image : container_image option;The Amazon Elastic Container Registry (Amazon ECR) path where the Docker image for the model is stored.
*)}Defines a container that provides the runtime environment for a model that you deploy with an inference component.
type nonrec inference_component_startup_parameters = {container_startup_health_check_timeout_in_seconds : production_variant_container_startup_health_check_timeout_in_seconds
option;The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.
*)model_data_download_timeout_in_seconds : production_variant_model_data_download_timeout_in_seconds
option;The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.
*)}Settings that take effect while the model container starts up.
type nonrec inference_component_compute_resource_requirements = {max_memory_required_in_mb : memory_in_mb option;The maximum MB of memory to allocate to run a model that you assign to an inference component.
*)min_memory_required_in_mb : memory_in_mb;The minimum MB of memory to allocate to run a model that you assign to an inference component.
*)number_of_accelerator_devices_required : number_of_accelerator_devices option;The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and Amazon Web Services Inferentia.
*)number_of_cpu_cores_required : number_of_cpu_cores option;The number of CPU cores to allocate to run a model that you assign to an inference component.
*)}Defines the compute resources to allocate to run a model, plus any adapter models, that you assign to an inference component. These resources include CPU cores, accelerators, and memory.
type nonrec inference_component_data_cache_config = {enable_caching : enable_caching;Sets whether the endpoint that hosts the inference component caches the model artifacts and container image.
With caching enabled, the endpoint caches this data in each instance that it provisions for the inference component. That way, the inference component deploys faster during the auto scaling process. If caching isn't enabled, the inference component takes longer to deploy because of the time it spends downloading the data.
*)}Settings that affect how the inference component caches data.
type nonrec inference_component_availability_zone_balance = {max_imbalance : availability_zone_balance_max_imbalance option;The maximum allowed difference in the number of inference component copies between any two Availability Zones. This parameter applies only when the endpoint has instances across two or more Availability Zones. A copy placement is allowed if it reduces imbalance or the resulting imbalance is within this value.
Default value: 0.
enforcement_mode : availability_zone_balance_enforcement_mode;Determines how strictly the Availability Zone balance constraint is enforced.
PERMISSIVE The endpoint attempts to balance copies across Availability Zones but proceeds with scheduling even if balance can't be achieved due to available capacity or instance distribution across Availability Zones.
*)}Configuration for balancing inference component copies across Availability Zones.
type nonrec inference_component_scheduling_config = {availability_zone_balance : inference_component_availability_zone_balance
option;Configuration for balancing inference component copies across Availability Zones.
*)placement_strategy : inference_component_placement_strategy;The strategy for placing inference component copies across available instances. If you also set AvailabilityZoneBalance, this strategy applies to placement within each Availability Zone.
SPREAD Distributes copies evenly across available instances for better resilience.
BINPACK Packs copies onto fewer instances to optimize resource utilization.
*)}The scheduling configuration that determines how inference component copies are placed across available instances when copies are added or removed.
type nonrec inference_component_specification = {scheduling_config : inference_component_scheduling_config option;The scheduling configuration that determines how inference component copies are placed across available instances when copies are added or removed.
*)data_cache_config : inference_component_data_cache_config option;Settings that affect how the inference component caches data.
*)base_inference_component_name : inference_component_name option;The name of an existing inference component that is to contain the inference component that you're creating with your request.
Specify this parameter only if your request is meant to create an adapter inference component. An adapter inference component contains the path to an adapter model. The purpose of the adapter model is to tailor the inference output of a base foundation model, which is hosted by the base inference component. The adapter inference component uses the compute resources that you assigned to the base inference component.
When you create an adapter inference component, use the Container parameter to specify the location of the adapter artifacts. In the parameter value, use the ArtifactUrl parameter of the InferenceComponentContainerSpecification data type.
Before you can create an adapter inference component, you must have an existing inference component that contains the foundation model that you want to adapt.
*)compute_resource_requirements : inference_component_compute_resource_requirements
option;The compute resources allocated to run the model, plus any adapter models, that you assign to the inference component.
Omit this parameter if your request is meant to create an adapter inference component. An adapter inference component is loaded by a base inference component, and it uses the compute resources of the base inference component.
*)startup_parameters : inference_component_startup_parameters option;Settings that take effect while the model container starts up.
*)container : inference_component_container_specification option;Defines a container that provides the runtime environment for a model that you deploy with an inference component.
*)model_name : model_name option;The name of an existing SageMaker AI model object in your account that you want to deploy with the inference component.
*)instance_type : production_variant_instance_type option;The ML compute instance type for the inference component specification. Specifies which instance type this specification applies to. Required when using the Specifications parameter with multiple entries.
}Details about the resources to deploy with this inference component, including the model, container, and compute resources.
type nonrec inference_component_specification_list =
inference_component_specification listtype nonrec inference_component_capacity_size = {value : capacity_size_value;Defines the capacity size, either as a number of inference component copies or a capacity percentage.
*)type_ : inference_component_capacity_size_type;Specifies the endpoint capacity type.
COPY_COUNT The endpoint activates based on the number of inference component copies.
CAPACITY_PERCENT The endpoint activates based on the specified percentage of capacity.
*)}Specifies the type and size of the endpoint capacity to activate for a rolling deployment or a rollback strategy. You can specify your batches as either of the following:
For a rollback strategy, if you don't specify the fields in this object, or if you set the Value parameter to 100%, then SageMaker AI uses a blue/green rollback strategy and rolls all traffic back to the blue fleet.
type nonrec inference_component_rolling_update_policy = {rollback_maximum_batch_size : inference_component_capacity_size option;The batch size for a rollback to the old endpoint fleet. If this field is absent, the value is set to the default, which is 100% of the total capacity. When the default is used, SageMaker AI provisions the entire capacity of the old fleet at once during rollback.
*)maximum_execution_timeout_in_seconds : maximum_execution_timeout_in_seconds
option;The time limit for the total deployment. Exceeding this limit causes a timeout.
*)wait_interval_in_seconds : wait_interval_in_seconds;The length of the baking period, during which SageMaker AI monitors alarms for each batch on the new fleet.
*)maximum_batch_size : inference_component_capacity_size;The batch size for each rolling step in the deployment process. For each step, SageMaker AI provisions capacity on the new endpoint fleet, routes traffic to that fleet, and terminates capacity on the old endpoint fleet. The value must be between 5% to 50% of the copy count of the inference component.
*)}Specifies a rolling deployment strategy for updating a SageMaker AI inference component.
type nonrec alarm = {alarm_name : alarm_name option;The name of a CloudWatch alarm in your account.
*)}An Amazon CloudWatch alarm configured to monitor metrics on an endpoint.
type nonrec alarm_list = alarm listtype nonrec auto_rollback_config = {alarms : alarm_list option;List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.
*)}Automatic rollback configuration for handling endpoint deployment failures and recovery.
type nonrec inference_component_deployment_config = {auto_rollback_configuration : auto_rollback_config option;rolling_update_policy : inference_component_rolling_update_policy;Specifies a rolling deployment strategy for updating a SageMaker AI endpoint.
*)}The deployment configuration for an endpoint that hosts inference components. The configuration includes the desired deployment strategy and rollback settings.
type nonrec update_inference_component_input = {deployment_config : inference_component_deployment_config option;The deployment configuration for the inference component. The configuration contains the desired deployment strategy and rollback settings.
*)runtime_config : inference_component_runtime_config option;Runtime settings for a model that is deployed with an inference component.
*)specifications : inference_component_specification_list option;A list of specification objects for the inference component, one per instance type. Use this parameter when you want to specify different model or resource configurations for the inference component on each instance type. You can use either this parameter or the singular Specification parameter, but not both.
specification : inference_component_specification option;Details about the resources to deploy with this inference component, including the model, container, and compute resources.
*)inference_component_name : inference_component_name;The name of the inference component.
*)}type nonrec update_image_version_response = {image_version_arn : image_version_arn option;The ARN of the image version.
*)}type nonrec sage_maker_image_version_aliases =
sage_maker_image_version_alias listtype nonrec update_image_version_request = {release_notes : release_notes option;The maintainer description of the image version.
*)horovod : horovod option;Indicates Horovod compatibility.
*)processor : processor option;Indicates CPU or GPU compatibility.
CPU: The image version is compatible with CPU.GPU: The image version is compatible with GPU.programming_lang : programming_lang option;The supported programming language and its version.
*)ml_framework : ml_framework option;The machine learning framework vended in the image version.
*)job_type : job_type option;Indicates SageMaker AI job type compatibility.
TRAINING: The image version is compatible with SageMaker AI training jobs.INFERENCE: The image version is compatible with SageMaker AI inference jobs.NOTEBOOK_KERNEL: The image version is compatible with SageMaker AI notebook kernels.vendor_guidance : vendor_guidance option;The availability of the image version specified by the maintainer.
NOT_PROVIDED: The maintainers did not provide a status for image version stability.STABLE: The image version is stable.TO_BE_ARCHIVED: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months.ARCHIVED: The image version is archived. Archived image versions are not searchable and are no longer actively supported.aliases_to_delete : sage_maker_image_version_aliases option;A list of aliases to delete.
*)aliases_to_add : sage_maker_image_version_aliases option;A list of aliases to add.
*)version : image_version_number option;The version of the image.
*)alias : sage_maker_image_version_alias option;The alias of the image version.
*)image_name : image_name;The name of the image.
*)}type nonrec image_delete_property_list = image_delete_property listtype nonrec update_image_request = {role_arn : role_arn option;The new ARN for the IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.
*)image_name : image_name;The name of the image to update.
*)display_name : image_display_name option;The new display name for the image.
*)description : image_description option;The new description for the image.
*)delete_properties : image_delete_property_list option;A list of properties to delete. Only the Description and DisplayName properties can be deleted.
}type nonrec update_hub_response = {hub_arn : hub_arn;The Amazon Resource Name (ARN) of the updated hub.
*)}type nonrec hub_search_keyword_list = hub_search_keyword listtype nonrec update_hub_request = {hub_search_keywords : hub_search_keyword_list option;The searchable keywords for the hub.
*)hub_display_name : hub_display_name option;The display name of the hub.
*)hub_description : hub_description option;A description of the updated hub.
*)hub_name : hub_name_or_arn;The name of the hub to update.
*)}type nonrec update_hub_content_response = {hub_content_arn : hub_content_arn;The ARN of the hub content resource that was updated.
*)hub_arn : hub_arn;The ARN of the private model hub that contains the updated hub content.
*)}type nonrec hub_content_search_keyword_list = hub_content_search_keyword listtype nonrec update_hub_content_request = {support_status : hub_content_support_status option;Indicates the current status of the hub content resource.
*)hub_content_search_keywords : hub_content_search_keyword_list option;The searchable keywords of the hub content.
*)hub_content_markdown : hub_content_markdown option;A string that provides a description of the hub content. This string can include links, tables, and standard markdown formatting.
*)hub_content_description : hub_content_description option;The description of the hub content.
*)hub_content_display_name : hub_content_display_name option;The display name of the hub content.
*)hub_content_version : hub_content_version;The hub content version that you want to update. For example, if you have two versions of a resource in your hub, you can update the second version.
*)hub_content_type : hub_content_type;The content type of the resource that you want to update. Only specify a Model or Notebook resource for this API. To update a ModelReference, use the UpdateHubContentReference API instead.
hub_content_name : hub_content_name;The name of the hub content resource that you want to update.
*)hub_name : hub_name_or_arn;The name of the SageMaker hub that contains the hub content you want to update. You can optionally use the hub ARN instead.
*)}type nonrec update_hub_content_reference_response = {hub_content_arn : hub_content_arn;The ARN of the hub content resource that was updated.
*)hub_arn : hub_arn;The ARN of the private model hub that contains the updated hub content.
*)}type nonrec update_hub_content_reference_request = {min_version : hub_content_version option;The minimum hub content version of the referenced model that you want to use. The minimum version must be older than the latest available version of the referenced model. To support all versions of a model, set the value to 1.0.0.
hub_content_type : hub_content_type;The content type of the resource that you want to update. Only specify a ModelReference resource for this API. To update a Model or Notebook resource, use the UpdateHubContent API instead.
hub_content_name : hub_content_name;The name of the hub content resource that you want to update.
*)hub_name : hub_name_or_arn;The name of the SageMaker hub that contains the hub content you want to update. You can optionally use the hub ARN instead.
*)}type nonrec feature_parameter = {value : feature_parameter_value option;The value that belongs to a key.
*)key : feature_parameter_key option;A key that must contain a value to describe the feature.
*)}A key-value pair that you specify to describe the feature.
type nonrec feature_parameter_additions = feature_parameter listtype nonrec feature_parameter_removals = feature_parameter_key listtype nonrec update_feature_metadata_request = {parameter_removals : feature_parameter_removals option;A list of parameter keys that you can specify to remove parameters that describe your feature.
*)parameter_additions : feature_parameter_additions option;A list of key-value pairs that you can add to better describe the feature.
*)description : feature_description option;A description that you can write to better describe the feature.
*)feature_name : feature_name;The name of the feature that you're updating.
*)feature_group_name : feature_group_name_or_arn;The name or Amazon Resource Name (ARN) of the feature group containing the feature that you're updating.
*)}type nonrec update_feature_group_response = {feature_group_arn : feature_group_arn;The Amazon Resource Number (ARN) of the feature group that you're updating.
*)}type nonrec collection_config = | VectorConfig of vector_configConfiguration for your vector collection type.
Dimension: The number of elements in your vector.Configuration for your collection.
type nonrec feature_definition = {collection_config : collection_config option;Configuration for your collection.
*)collection_type : collection_type option;A grouping of elements where each element within the collection must have the same feature type (String, Integral, or Fractional).
List: An ordered collection of elements.Set: An unordered collection of unique elements.Vector: A specialized list that represents a fixed-size array of elements. The vector dimension is determined by you. Must have elements with fractional feature types.feature_type : feature_type;The value type of a feature. Valid values are Integral, Fractional, or String.
*)feature_name : feature_name;The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted, write_time, api_invocation_time.
The name:
}A list of features. You must include FeatureName and FeatureType. Valid feature FeatureTypes are Integral, Fractional and String.
type nonrec feature_additions = feature_definition listtype nonrec ttl_duration = {value : ttl_duration_value option;TtlDuration time value.
unit_ : ttl_duration_unit option;TtlDuration time unit.
}Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration. For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.
type nonrec online_store_config_update = {ttl_duration : ttl_duration option;Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration. For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.
}Updates the feature group online store configuration.
type nonrec throughput_config_update = {provisioned_write_capacity_units : capacity_unit option;For provisioned feature groups, this indicates the write throughput you are billed for and can consume without throttling.
*)provisioned_read_capacity_units : capacity_unit option;For provisioned feature groups with online store enabled, this indicates the read throughput you are billed for and can consume without throttling.
*)throughput_mode : throughput_mode option;Target throughput mode of the feature group. Throughput update is an asynchronous operation, and the outcome should be monitored by polling LastUpdateStatus field in DescribeFeatureGroup response. You cannot update a feature group's throughput while another update is in progress.
}The new throughput configuration for the feature group. You can switch between on-demand and provisioned modes or update the read / write capacity of provisioned feature groups. You can switch a feature group to on-demand only once in a 24 hour period.
type nonrec update_feature_group_request = {throughput_config : throughput_config_update option;online_store_config : online_store_config_update option;Updates the feature group online store configuration.
*)feature_additions : feature_additions option;Updates the feature group. Updating a feature group is an asynchronous operation. When you get an HTTP 200 response, you've made a valid request. It takes some time after you've made a valid request for Feature Store to update the feature group.
*)feature_group_name : feature_group_name_or_arn;The name or Amazon Resource Name (ARN) of the feature group that you're updating.
*)}type nonrec update_experiment_response = {experiment_arn : experiment_arn option;The Amazon Resource Name (ARN) of the experiment.
*)}type nonrec update_experiment_request = {description : experiment_description option;The description of the experiment.
*)display_name : experiment_entity_name option;The name of the experiment as displayed. The name doesn't need to be unique. If DisplayName isn't specified, ExperimentName is displayed.
experiment_name : experiment_entity_name;The name of the experiment to update.
*)}type nonrec update_endpoint_weights_and_capacities_output = {endpoint_arn : endpoint_arn;The Amazon Resource Name (ARN) of the updated endpoint.
*)}type nonrec production_variant_serverless_update_config = {provisioned_concurrency : serverless_provisioned_concurrency option;The updated amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency.
max_concurrency : serverless_max_concurrency option;The updated maximum number of concurrent invocations your serverless endpoint can process.
*)}Specifies the serverless update concurrency configuration for an endpoint variant.
type nonrec desired_weight_and_capacity = {serverless_update_config : production_variant_serverless_update_config option;Specifies the serverless update concurrency configuration for an endpoint variant.
*)desired_instance_count : task_count option;The variant's capacity.
*)desired_weight : variant_weight option;The variant's weight.
*)variant_name : variant_name;The name of the variant to update.
*)}Specifies weight and capacity values for a production variant.
type nonrec desired_weight_and_capacity_list = desired_weight_and_capacity listtype nonrec update_endpoint_weights_and_capacities_input = {desired_weights_and_capacities : desired_weight_and_capacity_list;An object that provides new capacity and weight values for a variant.
*)endpoint_name : endpoint_name;The name of an existing SageMaker endpoint.
*)}type nonrec update_endpoint_output = {endpoint_arn : endpoint_arn;The Amazon Resource Name (ARN) of the endpoint.
*)}type nonrec capacity_size = {value : capacity_size_value;Defines the capacity size, either as a number of instances or a capacity percentage.
*)type_ : capacity_size_type;Specifies the endpoint capacity type.
INSTANCE_COUNT: The endpoint activates based on the number of instances.CAPACITY_PERCENT: The endpoint activates based on the specified percentage of capacity.}Specifies the type and size of the endpoint capacity to activate for a blue/green deployment, a rolling deployment, or a rollback strategy. You can specify your batches as either instance count or the overall percentage or your fleet.
For a rollback strategy, if you don't specify the fields in this object, or if you set the Value to 100%, then SageMaker uses a blue/green rollback strategy and rolls all traffic back to the blue fleet.
type nonrec traffic_routing_config = {linear_step_size : capacity_size option;Batch size for each step to turn on traffic on the new endpoint fleet. Value must be 10-50% of the variant's total instance count.
canary_size : capacity_size option;Batch size for the first step to turn on traffic on the new endpoint fleet. Value must be less than or equal to 50% of the variant's total instance count.
wait_interval_in_seconds : wait_interval_in_seconds;The waiting time (in seconds) between incremental steps to turn on traffic on the new endpoint fleet.
*)type_ : traffic_routing_config_type;Traffic routing strategy type.
ALL_AT_ONCE: Endpoint traffic shifts to the new fleet in a single step.CANARY: Endpoint traffic shifts to the new fleet in two steps. The first step is the canary, which is a small portion of the traffic. The second step is the remainder of the traffic.LINEAR: Endpoint traffic shifts to the new fleet in n steps of a configurable size.}Defines the traffic routing strategy during an endpoint deployment to shift traffic from the old fleet to the new fleet.
type nonrec blue_green_update_policy = {maximum_execution_timeout_in_seconds : maximum_execution_timeout_in_seconds
option;Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting time specified in TerminationWaitInSeconds and WaitIntervalInSeconds.
termination_wait_in_seconds : termination_wait_in_seconds option;Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.
*)traffic_routing_configuration : traffic_routing_config;Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.
*)}Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.
type nonrec rolling_update_policy = {rollback_maximum_batch_size : capacity_size option;Batch size for rollback to the old endpoint fleet. Each rolling step to provision capacity and turn on traffic on the old endpoint fleet, and terminate capacity on the new endpoint fleet. If this field is absent, the default value will be set to 100% of total capacity which means to bring up the whole capacity of the old fleet at once during rollback.
*)maximum_execution_timeout_in_seconds : maximum_execution_timeout_in_seconds
option;The time limit for the total deployment. Exceeding this limit causes a timeout.
*)wait_interval_in_seconds : wait_interval_in_seconds;The length of the baking period, during which SageMaker monitors alarms for each batch on the new fleet.
*)maximum_batch_size : capacity_size;Batch size for each rolling step to provision capacity and turn on traffic on the new endpoint fleet, and terminate capacity on the old endpoint fleet. Value must be between 5% to 50% of the variant's total instance count.
*)}Specifies a rolling deployment strategy for updating a SageMaker endpoint.
type nonrec deployment_config = {auto_rollback_configuration : auto_rollback_config option;Automatic rollback configuration for handling endpoint deployment failures and recovery.
*)rolling_update_policy : rolling_update_policy option;Specifies a rolling deployment strategy for updating a SageMaker endpoint.
*)blue_green_update_policy : blue_green_update_policy option;Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.
*)}The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.
type nonrec update_endpoint_input = {retain_deployment_config : boolean_ option;Specifies whether to reuse the last deployment configuration. The default value is false (the configuration is not reused).
*)deployment_config : deployment_config option;The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.
*)exclude_retained_variant_properties : variant_property_list option;When you are updating endpoint resources with RetainAllVariantProperties, whose value is set to true, ExcludeRetainedVariantProperties specifies the list of type VariantProperty to override with the values provided by EndpointConfig. If you don't specify a value for ExcludeRetainedVariantProperties, no variant properties are overridden.
retain_all_variant_properties : boolean_ option;When updating endpoint resources, enables or disables the retention of variant properties, such as the instance count or the variant weight. To retain the variant properties of an endpoint when updating it, set RetainAllVariantProperties to true. To use the variant properties specified in a new EndpointConfig call when updating an endpoint, set RetainAllVariantProperties to false. The default is false.
endpoint_config_name : endpoint_config_name;The name of the new endpoint configuration.
*)endpoint_name : endpoint_name;The name of the endpoint whose configuration you want to update.
*)}type nonrec update_domain_response = {domain_arn : domain_arn option;The Amazon Resource Name (ARN) of the domain.
*)}type nonrec r_studio_server_pro_domain_settings_for_update = {r_studio_package_manager_url : string_ option;A URL pointing to an RStudio Package Manager server.
*)r_studio_connect_url : string_ option;A URL pointing to an RStudio Connect server.
*)default_resource_spec : resource_spec option;domain_execution_role_arn : role_arn;The execution role for the RStudioServerPro Domain-level app.
}A collection of settings that update the current configuration for the RStudioServerPro Domain-level app.
type nonrec domain_security_group_ids = security_group_id listtype nonrec trusted_identity_propagation_settings = {status : feature_status;The status of Trusted Identity Propagation (TIP) at the SageMaker domain level.
When disabled, standard IAM role-based access is used.
When enabled:
}The Trusted Identity Propagation (TIP) settings for the SageMaker domain. These settings determine how user identities from IAM Identity Center are propagated through the domain to TIP enabled Amazon Web Services services.
type nonrec docker_settings = {rootless_docker : feature_status option;Indicates whether to use rootless Docker.
*)vpc_only_trusted_accounts : vpc_only_trusted_accounts option;The list of Amazon Web Services accounts that are trusted when the domain is created in VPC-only mode.
*)enable_docker_access : feature_status option;Indicates whether the domain can access Docker.
*)}A collection of settings that configure the domain's Docker interaction.
type nonrec amazon_q_settings = {q_profile_arn : q_profile_arn option;The ARN of the Amazon Q profile used within the domain.
*)status : feature_status option;Whether Amazon Q has been enabled within the domain.
*)}A collection of settings that configure the Amazon Q experience within the domain.
type nonrec unified_studio_settings = {single_sign_on_application_arn : single_sign_on_application_arn option;The ARN of the Amazon DataZone application managed by Amazon SageMaker Unified Studio in the Amazon Web Services IAM Identity Center.
*)project_s3_path : s3_uri option;The location where Amazon S3 stores temporary execution data and other artifacts for the project that corresponds to the domain.
*)environment_id : unified_studio_environment_id option;The ID of the environment that Amazon SageMaker Unified Studio associates with the domain.
*)project_id : unified_studio_project_id option;The ID of the Amazon SageMaker Unified Studio project that corresponds to the domain.
*)domain_id : unified_studio_domain_id option;The ID of the Amazon SageMaker Unified Studio domain associated with this domain.
*)domain_region : region_name option;The Amazon Web Services Region where the domain is located in Amazon SageMaker Unified Studio. The default value, if you don't specify a Region, is the Region where the Amazon SageMaker AI domain is located.
*)domain_account_id : account_id option;The ID of the Amazon Web Services account that has the Amazon SageMaker Unified Studio domain. The default value, if you don't specify an ID, is the ID of the account that has the Amazon SageMaker AI domain.
*)studio_web_portal_access : feature_status option;Sets whether you can access the domain in Amazon SageMaker Studio:
ENABLED You can access the domain in Amazon SageMaker Studio. If you migrate the domain to Amazon SageMaker Unified Studio, you can access it in both studio interfaces.
DISABLED You can't access the domain in Amazon SageMaker Studio. If you migrate the domain to Amazon SageMaker Unified Studio, you can access it only in that studio interface.
To migrate a domain to Amazon SageMaker Unified Studio, you specify the UnifiedStudioSettings data type when you use the UpdateDomain action.
*)}The settings that apply to an Amazon SageMaker AI domain when you use it in Amazon SageMaker Unified Studio.
type nonrec domain_settings_for_update = {ip_address_type : ip_address_type option;The IP address type for the domain. Specify ipv4 for IPv4-only connectivity or dualstack for both IPv4 and IPv6 connectivity. When you specify dualstack, the subnet must support IPv6 CIDR blocks.
unified_studio_settings : unified_studio_settings option;The settings that apply to an SageMaker AI domain when you use it in Amazon SageMaker Unified Studio.
*)amazon_q_settings : amazon_q_settings option;A collection of settings that configure the Amazon Q experience within the domain.
*)docker_settings : docker_settings option;A collection of settings that configure the domain's Docker interaction.
*)trusted_identity_propagation_settings : trusted_identity_propagation_settings
option;The Trusted Identity Propagation (TIP) settings for the SageMaker domain. These settings determine how user identities from IAM Identity Center are propagated through the domain to TIP enabled Amazon Web Services services.
*)security_group_ids : domain_security_group_ids option;The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.
execution_role_identity_config : execution_role_identity_config option;The configuration for attaching a SageMaker AI user profile name to the execution role as a sts:SourceIdentity key. This configuration can only be modified if there are no apps in the InService or Pending state.
r_studio_server_pro_domain_settings_for_update : r_studio_server_pro_domain_settings_for_update
option;A collection of RStudioServerPro Domain-level app settings to update. A single RStudioServerPro application is created for a domain.
}A collection of Domain configuration settings to update.
type nonrec default_space_settings = {custom_file_system_configs : custom_file_system_configs option;The settings for assigning a custom file system to a domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
*)custom_posix_user_config : custom_posix_user_config option;space_storage_settings : default_space_storage_settings option;jupyter_lab_app_settings : jupyter_lab_app_settings option;kernel_gateway_app_settings : kernel_gateway_app_settings option;jupyter_server_app_settings : jupyter_server_app_settings option;security_groups : security_group_ids option;The security group IDs for the Amazon VPC that the space uses for communication.
*)execution_role : role_arn option;The ARN of the execution role for the space.
*)}The default settings for shared spaces that users create in the domain.
SageMaker applies these settings only to shared spaces. It doesn't apply them to private spaces.
Indicates whether a home EFS file system is created for the domain.
type nonrec update_domain_request = {vpc_id : vpc_id option;The identifier for the VPC used by the domain for network communication. Use this field only when adding VPC configuration to a SageMaker AI domain used in Amazon SageMaker Unified Studio that was created without VPC settings. SageMaker AI doesn't automatically apply VPC updates to existing applications. Stop and restart your applications to apply the changes.
*)home_efs_file_system_creation : home_efs_file_system_creation option;Indicates whether to create a home EFS file system for the domain. You can change from Disabled to Enabled to provision EFS on demand, but you cannot change from Enabled to Disabled.
tag_propagation : tag_propagation option;Indicates whether custom tag propagation is supported for the domain. Defaults to DISABLED.
app_network_access_type : app_network_access_type option;Specifies the VPC used for non-EFS traffic.
PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker AI, which allows direct internet access.VpcOnly - All Studio traffic is through the specified VPC and subnets.This configuration can only be modified if there are no apps in the InService, Pending, or Deleting state. The configuration cannot be updated if DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is already set or DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided as part of the same request.
subnet_ids : subnets option;The VPC subnets that Studio uses for communication.
If removing subnets, ensure there are no apps in the InService, Pending, or Deleting state.
default_space_settings : default_space_settings option;The default settings for shared spaces that users create in the domain.
*)app_security_group_management : app_security_group_management option;The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided. If setting up the domain for use with RStudio, this value must be set to Service.
domain_settings_for_update : domain_settings_for_update option;A collection of DomainSettings configuration values to update.
default_user_settings : user_settings option;A collection of settings.
*)domain_id : domain_id;The ID of the domain to be updated.
*)}type nonrec device = {iot_thing_name : thing_name option;Amazon Web Services Internet of Things (IoT) object name.
*)description : device_description option;Description of the device.
*)device_name : device_name;The name of the device.
*)}Information of a particular device.
type nonrec devices = device listtype nonrec update_devices_request = {devices : devices;List of devices to register with Edge Manager agent.
*)device_fleet_name : entity_name;The name of the fleet the devices belong to.
*)}type nonrec edge_output_config = {preset_deployment_config : string_ option;The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
ComponentName (optional) - Name of the GreenGrass V2 component. If not specified, the default name generated consists of "SagemakerEdgeManager" and the name of your SageMaker Edge Manager packaging job.ComponentDescription (optional) - Description of the component.ComponentVersion (optional) - The version of the component.
Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow amajor.minor.patch number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification.
PlatformOS (optional) - The name of the operating system for the platform. Supported platforms include Windows and Linux.PlatformArchitecture (optional) - The processor architecture for the platform.
Supported architectures Windows include: Windows32_x86, Windows64_x64.
Supported architectures for Linux include: Linux x86_64, Linux ARMV8.
preset_deployment_type : edge_preset_deployment_type option;The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
*)kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
*)s3_output_location : s3_uri;The Amazon Simple Storage (S3) bucker URI.
*)}The output configuration.
type nonrec update_device_fleet_request = {enable_iot_role_alias : enable_iot_role_alias option;Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-{DeviceFleetName}".
For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".
*)output_config : edge_output_config;Output configuration for storing sample data collected by the fleet.
*)description : device_fleet_description option;Description of the fleet.
*)role_arn : role_arn option;The Amazon Resource Name (ARN) of the device.
*)device_fleet_name : entity_name;The name of the fleet.
*)}type nonrec update_context_response = {context_arn : context_arn option;The Amazon Resource Name (ARN) of the context.
*)}type nonrec lineage_entity_parameters =
(string_parameter_value * string_parameter_value) listtype nonrec list_lineage_entity_parameter_key = string_parameter_value listtype nonrec update_context_request = {properties_to_remove : list_lineage_entity_parameter_key option;A list of properties to remove.
*)properties : lineage_entity_parameters option;The new list of properties. Overwrites the current property list.
*)description : experiment_description option;The new description for the context.
*)context_name : context_name;The name of the context to update.
*)}type nonrec update_compute_quota_response = {compute_quota_version : integer;Version of the compute allocation definition.
*)compute_quota_arn : compute_quota_arn;ARN of the compute allocation definition.
*)}type nonrec cluster_instance_type = | ML_P6_B300_48XLARGE| ML_G7E_48XLARGE| ML_G7E_24XLARGE| ML_G7E_12XLARGE| ML_G7E_8XLARGE| ML_G7E_4XLARGE| ML_G7E_2XLARGE| ML_R5D_16XLARGE| ML_R7I_48XLARGE| ML_R7I_24XLARGE| ML_R7I_16XLARGE| ML_R7I_12XLARGE| ML_R7I_8XLARGE| ML_R7I_4XLARGE| ML_R7I_2XLARGE| ML_R7I_XLARGE| ML_R7I_LARGE| ML_M7I_48XLARGE| ML_M7I_24XLARGE| ML_M7I_16XLARGE| ML_M7I_12XLARGE| ML_M7I_8XLARGE| ML_M7I_4XLARGE| ML_M7I_2XLARGE| ML_M7I_XLARGE| ML_M7I_LARGE| ML_I3EN_24XLARGE| ML_I3EN_12XLARGE| ML_I3EN_6XLARGE| ML_I3EN_3XLARGE| ML_I3EN_2XLARGE| ML_I3EN_XLARGE| ML_I3EN_LARGE| ML_R6I_32XLARGE| ML_R6I_24XLARGE| ML_R6I_16XLARGE| ML_R6I_12XLARGE| ML_R6I_8XLARGE| ML_R6I_4XLARGE| ML_R6I_2XLARGE| ML_R6I_XLARGE| ML_R6I_LARGE| ML_M6I_32XLARGE| ML_M6I_24XLARGE| ML_M6I_16XLARGE| ML_M6I_12XLARGE| ML_M6I_8XLARGE| ML_M6I_4XLARGE| ML_M6I_2XLARGE| ML_M6I_XLARGE| ML_M6I_LARGE| ML_C6I_32XLARGE| ML_C6I_24XLARGE| ML_C6I_16XLARGE| ML_C6I_12XLARGE| ML_C6I_8XLARGE| ML_C6I_4XLARGE| ML_C6I_2XLARGE| ML_C6I_XLARGE| ML_C6I_LARGE| ML_TRN2_48XLARGE| ML_TRN2_3XLARGE| ML_P6_B200_48XLARGE| ML_P5EN_48XLARGE| ML_P5E_48XLARGE| ML_G6E_48XLARGE| ML_G6E_24XLARGE| ML_G6E_12XLARGE| ML_G6E_16XLARGE| ML_G6E_8XLARGE| ML_G6E_4XLARGE| ML_G6E_2XLARGE| ML_G6E_XLARGE| ML_GR6_8XLARGE| ML_GR6_4XLARGE| ML_G6_48XLARGE| ML_G6_24XLARGE| ML_G6_12XLARGE| ML_G6_16XLARGE| ML_G6_8XLARGE| ML_G6_4XLARGE| ML_G6_2XLARGE| ML_G6_XLARGE| ML_T3_2XLARGE| ML_T3_XLARGE| ML_T3_LARGE| ML_T3_MEDIUM| ML_M5_24XLARGE| ML_M5_16XLARGE| ML_M5_12XLARGE| ML_M5_8XLARGE| ML_M5_4XLARGE| ML_M5_2XLARGE| ML_M5_XLARGE| ML_M5_LARGE| ML_C5N_18XLARGE| ML_C5N_9XLARGE| ML_C5N_4XLARGE| ML_C5N_2XLARGE| ML_C5N_LARGE| ML_C5_24XLARGE| ML_C5_18XLARGE| ML_C5_12XLARGE| ML_C5_9XLARGE| ML_C5_4XLARGE| ML_C5_2XLARGE| ML_C5_XLARGE| ML_C5_LARGE| ML_G5_48XLARGE| ML_G5_24XLARGE| ML_G5_16XLARGE| ML_G5_12XLARGE| ML_G5_8XLARGE| ML_G5_4XLARGE| ML_G5_2XLARGE| ML_G5_XLARGE| ML_TRN1N_32XLARGE| ML_TRN1_32XLARGE| ML_P6E_GB200_36XLARGE| ML_P5_4XLARGE| ML_P5_48XLARGE| ML_P4DE_24XLARGE| ML_P4D_24XLARGEtype nonrec mig_profile_type = | MIG_7G_186GB| MIG_7G_180GB| MIG_7G_141GB| MIG_7G_80GB| MIG_7G_40GB| MIG_4G_93GB| MIG_4G_90GB| MIG_4G_71GB| MIG_4G_40GB| MIG_4G_20GB| MIG_3G_93GB| MIG_3G_90GB| MIG_3G_71GB| MIG_3G_40GB| MIG_3G_20GB| MIG_2G_47GB| MIG_2G_45GB| MIG_2G_35GB| MIG_2G_20GB| MIG_2G_10GB| MIG_1G_47GB| MIG_1G_45GB| MIG_1G_35GB| MIG_1G_23GB| MIG_1G_20GB| MIG_1G_18GB| MIG_1G_10GB| MIG_1G_5GBtype nonrec accelerator_partition_config = {count : integer;The number of accelerator partitions to allocate with the specified partition type. If you don't specify a value for vCPU and MemoryInGiB, SageMaker AI automatically allocates ratio-based values for those parameters based on the accelerator partition count you provide.
*)type_ : mig_profile_type;The Multi-Instance GPU (MIG) profile type that defines the partition configuration. The profile specifies the compute and memory allocation for each partition instance. The available profile types depend on the instance type specified in the compute quota configuration.
*)}Configuration for allocating accelerator partitions.
type nonrec compute_quota_resource_config = {accelerator_partition : accelerator_partition_config option;The accelerator partition configuration for fractional GPU allocation.
*)memory_in_gi_b : memory_in_gi_b_amount option;The amount of memory in GiB to allocate. If you specify a value only for this parameter, SageMaker AI automatically allocates a ratio-based value for vCPU based on this memory that you provide. For example, if you allocate 200 out of 400 total memory in GiB, SageMaker AI uses the ratio of 0.5 and allocates values to vCPU. Accelerators are set to 0.
*)v_cpu : v_cpu_amount option;The number of vCPU to allocate. If you specify a value only for vCPU, SageMaker AI automatically allocates ratio-based values for MemoryInGiB based on this vCPU parameter. For example, if you allocate 20 out of 40 total vCPU, SageMaker AI uses the ratio of 0.5 and allocates values to MemoryInGiB. Accelerators are set to 0.
*)accelerators : accelerators_amount option;The number of accelerators to allocate. If you don't specify a value for vCPU and MemoryInGiB, SageMaker AI automatically allocates ratio-based values for those parameters based on the number of accelerators you provide. For example, if you allocate 16 out of 32 total accelerators, SageMaker AI uses the ratio of 0.5 and allocates values to vCPU and MemoryInGiB.
*)count : instance_count option;The number of instances to add to the instance group of a SageMaker HyperPod cluster.
*)instance_type : cluster_instance_type;The instance type of the instance group for the cluster.
*)}Configuration of the resources used for the compute allocation definition.
type nonrec compute_quota_resource_config_list =
compute_quota_resource_config listtype nonrec absolute_borrow_limit_resource_list =
compute_quota_resource_config listtype nonrec resource_sharing_config = {absolute_borrow_limits : absolute_borrow_limit_resource_list option;The absolute limits on compute resources that can be borrowed from idle compute. When specified, these limits define the maximum amount of specific resource types (such as accelerators, vCPU, or memory) that an entity can borrow, regardless of the percentage-based BorrowLimit.
borrow_limit : borrow_limit option;The limit on how much idle compute can be borrowed.The values can be 1 - 500 percent of idle compute that the team is allowed to borrow.
Default is 50.
strategy : resource_sharing_strategy;The strategy of how idle compute is shared within the cluster. The following are the options of strategies.
DontLend: entities do not lend idle compute.Lend: entities can lend idle compute to entities that can borrow.LendandBorrow: entities can lend idle compute and borrow idle compute from other entities.Default is LendandBorrow.
}Resource sharing configuration.
type nonrec compute_quota_config = {preempt_team_tasks : preempt_team_tasks option;Allows workloads from within an entity to preempt same-team workloads. When set to LowerPriority, the entity's lower priority tasks are preempted by their own higher priority tasks.
Default is LowerPriority.
resource_sharing_config : resource_sharing_config option;Resource sharing configuration. This defines how an entity can lend and borrow idle compute with other entities within the cluster.
*)compute_quota_resources : compute_quota_resource_config_list option;Allocate compute resources by instance types.
*)}Configuration of the compute allocation definition for an entity. This includes the resource sharing option and the setting to preempt low priority tasks.
type nonrec compute_quota_target = {team_name : compute_quota_target_team_name;Name of the team to allocate compute resources to.
*)}The target entity to allocate compute resources to.
type nonrec update_compute_quota_request = {description : entity_description option;Description of the compute allocation definition.
*)activation_state : activation_state option;The state of the compute allocation being described. Use to enable or disable compute allocation.
Default is Enabled.
compute_quota_target : compute_quota_target option;The target entity to allocate compute resources to.
*)compute_quota_config : compute_quota_config option;Configuration of the compute allocation definition. This includes the resource sharing option, and the setting to preempt low priority tasks.
*)target_version : integer;Target version.
*)compute_quota_id : compute_quota_id;ID of the compute allocation definition.
*)}type nonrec update_code_repository_output = {code_repository_arn : code_repository_arn;The ARN of the Git repository.
*)}type nonrec git_config_for_update = {secret_arn : secret_arn option;The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:
\{"username": {i UserName}, "password": {i Password}\}}Specifies configuration details for a Git repository when the repository is updated.
type nonrec update_code_repository_input = {git_config : git_config_for_update option;The configuration of the git repository, including the URL and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository. The secret must have a staging label of AWSCURRENT and must be in the following format:
\{"username": {i UserName}, "password": {i Password}\}code_repository_name : entity_name;The name of the Git repository to update.
*)}type nonrec update_cluster_software_response = {cluster_arn : cluster_arn;The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster being updated for security patching.
*)}type nonrec update_cluster_software_instance_group_specification = {image_release_version : image_release_version option;The version of the HyperPod-managed AMI to update to for the instance group. Uses semantic versioning in the format MAJOR.MINOR.PATCH.
instance_group_name : cluster_instance_group_name;The name of the instance group to update.
*)}The configuration that describes specifications of the instance groups to update.
type nonrec update_cluster_software_instance_groups =
update_cluster_software_instance_group_specification listtype nonrec capacity_size_config = {value : node_unavailability_value;Specifies the amount or percentage of instances SageMaker updates at a time.
*)type_ : node_unavailability_type;Specifies whether SageMaker should process the update by amount or percentage of instances.
*)}The configuration of the size measurements of the AMI update. Using this configuration, you can specify whether SageMaker should update your instance group by an amount or percentage of instances.
type nonrec rolling_deployment_policy = {rollback_maximum_batch_size : capacity_size_config option;The maximum amount of instances in the cluster that SageMaker can roll back at a time.
*)maximum_batch_size : capacity_size_config;The maximum amount of instances in the cluster that SageMaker can update at a time.
*)}The configurations that SageMaker uses when updating the AMI versions.
The details of the alarm to monitor during the AMI update.
type nonrec auto_rollback_alarms = alarm_details listtype nonrec deployment_configuration = {auto_rollback_configuration : auto_rollback_alarms option;An array that contains the alarms that SageMaker monitors to know whether to roll back the AMI update.
*)wait_interval_in_seconds : wait_time_interval_in_seconds option;The duration in seconds that SageMaker waits before updating more instances in the cluster.
*)rolling_update_policy : rolling_deployment_policy option;The policy that SageMaker uses when updating the AMI versions of the cluster.
*)}The configuration to use when updating the AMI versions.
type nonrec update_cluster_software_request = {image_id : image_id option;When configuring your HyperPod cluster, you can specify an image ID using one of the following options:
HyperPodPublicAmiId: Use a HyperPod public AMICustomAmiId: Use your custom AMIdefault: Use the default latest system imageIf you choose to use a custom AMI (CustomAmiId), ensure it meets the following requirements:
When updating the instance group's AMI through the UpdateClusterSoftware operation, if an instance group uses a custom AMI, you must provide an ImageId or use the default as input. Note that if you don't specify an instance group in your UpdateClusterSoftware request, then all of the instance groups are patched with the specified image.
deployment_config : deployment_configuration option;The configuration to use when updating the AMI versions.
*)instance_groups : update_cluster_software_instance_groups option;The array of instance groups for which to update AMI versions.
*)cluster_name : cluster_name_or_arn;Specify the name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster you want to update for security patching.
*)}type nonrec update_cluster_scheduler_config_response = {cluster_scheduler_config_version : integer;Version of the cluster policy.
*)cluster_scheduler_config_arn : cluster_scheduler_config_arn;ARN of the cluster policy.
*)}type nonrec priority_class = {weight : priority_weight;Weight of the priority class. The value is within a range from 0 to 100, where 0 is the default.
A weight of 0 is the lowest priority and 100 is the highest. Weight 0 is the default.
*)name : cluster_scheduler_priority_class_name;Name of the priority class.
*)}Priority class configuration. When included in PriorityClasses, these class configurations define how tasks are queued.
type nonrec priority_class_list = priority_class listtype nonrec scheduler_config = {idle_resource_sharing : idle_resource_sharing option;Configuration for sharing idle compute resources across entities in the cluster. When enabled, unallocated resources are automatically calculated and made available for entities to borrow.
*)priority_classes : priority_class_list option;List of the priority classes, PriorityClass, of the cluster policy. When specified, these class configurations define how tasks are queued.
}Cluster policy configuration. This policy is used for task prioritization and fair-share allocation. This helps prioritize critical workloads and distributes idle compute across entities.
type nonrec update_cluster_scheduler_config_request = {description : entity_description option;Description of the cluster policy.
*)scheduler_config : scheduler_config option;Cluster policy configuration.
*)target_version : integer;Target version.
*)cluster_scheduler_config_id : cluster_scheduler_config_id;ID of the cluster policy.
*)}type nonrec update_cluster_response = {cluster_arn : cluster_arn;The Amazon Resource Name (ARN) of the updated SageMaker HyperPod cluster.
*)}type nonrec cluster_instance_types = cluster_instance_type listtype nonrec cluster_instance_requirements = {instance_types : cluster_instance_types;The list of instance types that the instance group can use. The order of instance types determines the priority—HyperPod attempts to provision instances using the first instance type in the list and falls back to subsequent types if capacity is unavailable.
*)}The instance requirements for a flexible instance group. Use this to specify multiple instance types that the instance group can use. The order of instance types in the list determines the priority for instance provisioning.
type nonrec cluster_life_cycle_config = {on_init_complete : cluster_life_cycle_config_file_name option;The file name of the entrypoint script of lifecycle scripts under SourceS3Uri. This script runs on the node after the AMI-based initialization is complete.
on_create : cluster_life_cycle_config_file_name option;The file name of the entrypoint script of lifecycle scripts under SourceS3Uri. This entrypoint script runs during cluster creation.
source_s3_uri : s3_uri option;An Amazon S3 bucket path where your lifecycle scripts are stored.
Make sure that the S3 bucket path starts with s3://sagemaker-. The IAM role for SageMaker HyperPod has the managed AmazonSageMakerClusterInstanceRolePolicy attached, which allows access to S3 buckets with the specific prefix sagemaker-.
}The lifecycle configuration for a SageMaker HyperPod cluster.
type nonrec cluster_ebs_volume_config = {root_volume : boolean_ option;Specifies whether the configuration is for the cluster's root or secondary Amazon EBS volume. You can specify two ClusterEbsVolumeConfig fields to configure both the root and secondary volumes. Set the value to True if you'd like to provide your own customer managed Amazon Web Services KMS key to encrypt the root volume. When True:
VolumeSizeInGB field. The size of the root volume is determined for you.VolumeKmsKeyId to encrypt the root volume with your own KMS key instead of an Amazon Web Services owned KMS key.Otherwise, by default, the value is False, and the following applies:
VolumeSizeInGB field.VolumeKmsKeyId to encrypt the secondary volume with your own KMS key instead of an Amazon Web Services owned KMS key.volume_kms_key_id : kms_key_id option;The ID of a KMS key to encrypt the Amazon EBS volume.
*)volume_size_in_g_b : cluster_ebs_volume_size_in_g_b option;The size in gigabytes (GB) of the additional EBS volume to be attached to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to /opt/sagemaker.
}Defines the configuration for attaching an additional Amazon Elastic Block Store (EBS) volume to each instance of the SageMaker HyperPod cluster instance group. To learn more, see SageMaker HyperPod release notes: June 20, 2024.
type nonrec cluster_fsx_lustre_config = {mount_path : cluster_fsx_mount_path option;The local path where the Amazon FSx for Lustre file system is mounted on instances.
*)mount_name : cluster_mount_name;The mount name of the Amazon FSx for Lustre file system.
*)dns_name : cluster_dns_name;The DNS name of the Amazon FSx for Lustre file system.
*)}Defines the configuration for attaching an Amazon FSx for Lustre file system to instances in a SageMaker HyperPod cluster instance group.
type nonrec cluster_fsx_open_zfs_config = {mount_path : cluster_fsx_mount_path option;The local path where the Amazon FSx for OpenZFS file system is mounted on instances.
*)dns_name : cluster_dns_name;The DNS name of the Amazon FSx for OpenZFS file system.
*)}Defines the configuration for attaching an Amazon FSx for OpenZFS file system to instances in a SageMaker HyperPod cluster instance group.
type nonrec cluster_instance_storage_config = | FsxOpenZfsConfig of cluster_fsx_open_zfs_configDefines the configuration for attaching an Amazon FSx for OpenZFS file system to the instances in the SageMaker HyperPod cluster instance group.
*)| FsxLustreConfig of cluster_fsx_lustre_configDefines the configuration for attaching an Amazon FSx for Lustre file system to the instances in the SageMaker HyperPod cluster instance group.
*)| EbsVolumeConfig of cluster_ebs_volume_configDefines the configuration for attaching additional Amazon Elastic Block Store (EBS) volumes to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to /opt/sagemaker.
Defines the configuration for attaching additional storage to the instances in the SageMaker HyperPod cluster instance group. To learn more, see SageMaker HyperPod release notes: June 20, 2024.
type nonrec cluster_instance_storage_configs =
cluster_instance_storage_config listtype nonrec on_start_deep_health_checks = deep_health_check_type listtype nonrec scheduled_update_config = {deployment_config : deployment_configuration option;The configuration to use when updating the AMI versions.
*)schedule_expression : cron_schedule_expression;A cron expression that specifies the schedule that SageMaker follows when updating the AMI.
*)}The configuration object of the schedule that SageMaker follows when updating the AMI.
The strategy for applying automatic patches to instances.
WhenIdle: Cordons all instances and patches each instance as it becomes idle (no running jobs). Each instance is uncordoned immediately after patching and becomes available for new jobs. If instances do not become idle, they remain on the previous AMI version. You can then use UpdateClusterSoftware with the desired ImageReleaseVersion to manually update the remaining instances.WhenAllIdle: Cordons all instances and waits for all to become idle before patching. All instances are uncordoned after patching completes. If not all instances become idle, no patching occurs and all instances remain on the previous AMI version.type nonrec cluster_patch_schedule = {next_patch_date : timestamp option;The date and time of the next scheduled automatic patch. The system sets this automatically when a patch is detected. Use this field to reschedule the patch to a different date.
*)}The schedule configuration for automatic patching.
type nonrec cluster_auto_patch_config = {deployment_config : deployment_configuration option;The deployment configuration for rolling patch updates, including rollback settings and batch sizes. Only applicable when using a rolling patching strategy.
*)patch_schedule : cluster_patch_schedule option;The schedule for automatic patching, including the next patch date.
*)patching_strategy : cluster_patching_strategy;The strategy for applying patches to instances in the group.
*)}The configuration for automatic patching of the instance group. When configured, the system automatically applies security patch AMI updates to the instance group.
type nonrec cluster_kubernetes_labels =
(cluster_kubernetes_label_key * cluster_kubernetes_label_value) listtype nonrec cluster_kubernetes_taint = {effect_ : cluster_kubernetes_taint_effect;The effect of the taint. Valid values are NoSchedule, PreferNoSchedule, and NoExecute.
value : cluster_kubernetes_taint_value option;The value of the taint.
*)key : cluster_kubernetes_taint_key;The key of the taint.
*)}A Kubernetes taint that can be applied to cluster nodes.
type nonrec cluster_kubernetes_taints = cluster_kubernetes_taint listtype nonrec cluster_kubernetes_config = {taints : cluster_kubernetes_taints option;List of taints to be applied to cluster nodes.
*)labels : cluster_kubernetes_labels option;Key-value pairs of labels to be applied to cluster nodes.
*)}Kubernetes configuration that specifies labels and taints to be applied to cluster nodes in an instance group.
type nonrec cluster_partition_names = cluster_partition_name listtype nonrec cluster_slurm_config = {partition_names : cluster_partition_names option;The list of Slurm partition names that the instance group belongs to.
*)node_type : cluster_slurm_node_type;The type of Slurm node for the instance group. Valid values are Controller, Worker, and Login.
}The Slurm configuration for an instance group in a SageMaker HyperPod cluster.
type nonrec cluster_capacity_requirements = {on_demand : cluster_on_demand_options option;Configuration options specific to On-Demand instances.
*)spot : cluster_spot_options option;Configuration options specific to Spot instances.
*)}Defines the instance capacity requirements for an instance group, including configurations for both Spot and On-Demand capacity types.
type nonrec cluster_network_interface = {interface_type : cluster_interface_type option;The type of network interface for the instance group. Valid values:
efa – An EFA with ENA interface, which provides both the EFA device for low-latency, high-throughput communication and the ENA device for IP networking.efa-only – An EFA-only interface, which provides only the EFA device capabilities without the ENA device for traditional IP networking.For more information, see Elastic Fabric Adapter.
*)}The network interface configuration for a Amazon SageMaker HyperPod cluster instance group.
type nonrec cluster_instance_group_specification = {network_interface : cluster_network_interface option;The network interface configuration for the instance group.
*)capacity_requirements : cluster_capacity_requirements option;Specifies the capacity requirements for the instance group.
*)slurm_config : cluster_slurm_config option;Specifies the Slurm configuration for the instance group.
*)kubernetes_config : cluster_kubernetes_config option;Specifies the Kubernetes configuration for the instance group. You describe what you want the labels and taints to look like, and the cluster works to reconcile the actual state with the declared state for nodes in this instance group.
*)image_release_version : image_release_version option;The version of the HyperPod-managed AMI to use for the instance group. Uses semantic versioning in the format MAJOR.MINOR.PATCH (for example, 1.2.3). If omitted, the latest available version is used.
auto_patch_config : cluster_auto_patch_config option;The configuration for automatic OS security patching. If present, the system automatically applies PATCH AMI updates to this instance group.
*)image_id : image_id option;When configuring your HyperPod cluster, you can specify an image ID using one of the following options:
HyperPodPublicAmiId: Use a HyperPod public AMICustomAmiId: Use your custom AMIdefault: Use the default latest system imageIf you choose to use a custom AMI (CustomAmiId), ensure it meets the following requirements:
When updating the instance group's AMI through the UpdateClusterSoftware operation, if an instance group uses a custom AMI, you must provide an ImageId or use the default as input. Note that if you don't specify an instance group in your UpdateClusterSoftware request, then all of the instance groups are patched with the specified image.
scheduled_update_config : scheduled_update_config option;The configuration object of the schedule that SageMaker uses to update the AMI.
*)override_vpc_config : vpc_config option;To configure multi-AZ deployments, customize the Amazon VPC configuration at the instance group level. You can specify different subnets and security groups across different AZs in the instance group specification to override a SageMaker HyperPod cluster's default Amazon VPC configuration. For more information about deploying a cluster in multiple AZs, see Setting up SageMaker HyperPod clusters across multiple AZs.
When your Amazon VPC and subnets support IPv6, network communications differ based on the cluster orchestration platform:
Additional resources for IPv6 configuration:
training_plan_arn : training_plan_arn option;The Amazon Resource Name (ARN); of the training plan to use for this cluster instance group.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingPlan.html}CreateTrainingPlan} .
*)on_start_deep_health_checks : on_start_deep_health_checks option;A flag indicating whether deep health checks should be performed when the cluster instance group is created or updated.
*)instance_storage_configs : cluster_instance_storage_configs option;Specifies the additional storage configurations for the instances in the SageMaker HyperPod cluster instance group.
*)threads_per_core : cluster_threads_per_core option;Specifies the value for Threads per core. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For instance types that doesn't support multithreading, specify 1. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.
execution_role : role_arn;Specifies an IAM execution role to be assumed by the instance group.
*)life_cycle_config : cluster_life_cycle_config option;Specifies the LifeCycle configuration for the instance group.
*)instance_requirements : cluster_instance_requirements option;The instance requirements for the instance group, including the instance types to use. Use this to create a flexible instance group that supports multiple instance types. The InstanceType and InstanceRequirements properties are mutually exclusive.
instance_type : cluster_instance_type option;Specifies the instance type of the instance group.
*)instance_group_name : cluster_instance_group_name;Specifies the name of the instance group.
*)min_instance_count : cluster_instance_count option;Defines the minimum number of instances required for an instance group to become InService. If this threshold isn't met within 3 hours, the instance group rolls back to its previous state - zero instances for new instance groups, or previous settings for existing instance groups. MinInstanceCount only affects the initial transition to InService and does not guarantee maintaining this minimum afterward.
instance_count : cluster_instance_count;Specifies the number of instances to add to the instance group of a SageMaker HyperPod cluster.
*)}The specifications of an instance group that you need to define.
type nonrec cluster_instance_group_specifications =
cluster_instance_group_specification listtype nonrec f_sx_lustre_config = {per_unit_storage_throughput : f_sx_lustre_per_unit_storage_throughput;The throughput capacity of the Amazon FSx for Lustre file system, measured in MB/s per TiB of storage.
*)size_in_gi_b : f_sx_lustre_size_in_gi_b;The storage capacity of the Amazon FSx for Lustre file system, specified in gibibytes (GiB).
*)}Configuration settings for an Amazon FSx for Lustre file system to be used with the cluster.
type nonrec environment_config = {f_sx_lustre_config : f_sx_lustre_config option;Configuration settings for an Amazon FSx for Lustre file system to be used with the cluster.
*)}The configuration for the restricted instance groups (RIG) environment.
type nonrec cluster_restricted_instance_group_specification = {environment_config : environment_config option;The configuration for the restricted instance groups (RIG) environment.
*)scheduled_update_config : scheduled_update_config option;override_vpc_config : vpc_config option;training_plan_arn : training_plan_arn option;The Amazon Resource Name (ARN) of the training plan to filter clusters by. For more information about reserving GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingPlan.html}CreateTrainingPlan} .
*)on_start_deep_health_checks : on_start_deep_health_checks option;A flag indicating whether deep health checks should be performed when the cluster restricted instance group is created or updated.
*)instance_storage_configs : cluster_instance_storage_configs option;Specifies the additional storage configurations for the instances in the SageMaker HyperPod cluster restricted instance group.
*)threads_per_core : cluster_threads_per_core option;The number you specified to TreadsPerCore in CreateCluster for enabling or disabling multithreading. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.
execution_role : role_arn;Specifies an IAM execution role to be assumed by the restricted instance group.
*)instance_type : cluster_instance_type;Specifies the instance type of the restricted instance group.
*)instance_group_name : cluster_instance_group_name;Specifies the name of the restricted instance group.
*)instance_count : cluster_instance_count;Specifies the number of instances to add to the restricted instance group of a SageMaker HyperPod cluster.
*)}The specifications of a restricted instance group that you need to define.
type nonrec cluster_restricted_instance_group_specifications =
cluster_restricted_instance_group_specification listThe deletion policy for the Amazon FSx for Lustre file system used in the shared environment of restricted instance groups (RIG).
The shared environment configuration for the restricted instance groups (RIG).
The configuration for the restricted instance groups (RIG) in the SageMaker HyperPod cluster.
type nonrec cluster_tiered_storage_config = {instance_memory_allocation_percentage : cluster_instance_memory_allocation_percentage
option;The percentage (int) of cluster memory to allocate for checkpointing.
*)mode : cluster_config_mode;Specifies whether managed tier checkpointing is enabled or disabled for the HyperPod cluster. When set to Enable, the system installs a memory management daemon that provides disaggregated memory as a service for checkpoint storage. When set to Disable, the feature is turned off and the memory management daemon is removed from the cluster.
}Defines the configuration for managed tier checkpointing in a HyperPod cluster. Managed tier checkpointing uses multiple storage tiers, including cluster CPU memory, to provide faster checkpoint operations and improved fault tolerance for large-scale model training. The system automatically saves checkpoints at high frequency to memory and periodically persists them to durable storage, like Amazon S3.
type nonrec cluster_instance_groups_to_delete =
cluster_instance_group_name listtype nonrec cluster_auto_scaling_config = {auto_scaler_type : cluster_auto_scaler_type option;The type of autoscaler to use. Currently supported value is Karpenter.
mode : cluster_auto_scaling_mode;Describes whether autoscaling is enabled or disabled for the cluster. Valid values are Enable and Disable.
}Specifies the autoscaling configuration for a HyperPod cluster.
type nonrec cluster_orchestrator_eks_config = {cluster_arn : eks_cluster_arn;The Amazon Resource Name (ARN) of the Amazon EKS cluster associated with the SageMaker HyperPod cluster.
*)}The configuration settings for the Amazon EKS cluster used as the orchestrator for the SageMaker HyperPod cluster.
type nonrec cluster_orchestrator_slurm_config = {slurm_config_strategy : cluster_slurm_config_strategy option;The strategy for managing partitions for the Slurm configuration. Valid values are Managed, Overwrite, and Merge.
}The configuration settings for the Slurm orchestrator used with the SageMaker HyperPod cluster.
type nonrec cluster_orchestrator = {slurm : cluster_orchestrator_slurm_config option;The Slurm orchestrator configuration for the SageMaker HyperPod cluster.
*)eks : cluster_orchestrator_eks_config option;The Amazon EKS cluster used as the orchestrator for the SageMaker HyperPod cluster.
*)}The type of orchestrator used for the SageMaker HyperPod cluster.
type nonrec update_cluster_request = {orchestrator : cluster_orchestrator option;auto_scaling : cluster_auto_scaling_config option;Updates the autoscaling configuration for the cluster. Use to enable or disable automatic node scaling.
*)cluster_role : role_arn option;The Amazon Resource Name (ARN) of the IAM role that HyperPod assumes for cluster autoscaling operations. Cannot be updated while autoscaling is enabled.
*)node_provisioning_mode : cluster_node_provisioning_mode option;Determines how instance provisioning is handled during cluster operations. In Continuous mode, the cluster provisions available instances incrementally and retries until the target count is reached. The cluster becomes operational once cluster-level resources are ready. Use CurrentCount and TargetCount in DescribeCluster to track provisioning progress.
instance_groups_to_delete : cluster_instance_groups_to_delete option;Specify the names of the instance groups to delete. Use a single , as the separator between multiple names.
node_recovery : cluster_node_recovery option;The node recovery mode to be applied to the SageMaker HyperPod cluster.
*)tiered_storage_config : cluster_tiered_storage_config option;Updates the configuration for managed tier checkpointing on the HyperPod cluster. For example, you can enable or disable the feature and modify the percentage of cluster memory allocated for checkpoint storage.
*)restricted_instance_groups_config : cluster_restricted_instance_groups_config
option;The configuration for the restricted instance groups (RIG) in the SageMaker HyperPod cluster.
*)restricted_instance_groups : cluster_restricted_instance_group_specifications
option;The specialized instance groups for training models like Amazon Nova to be created in the SageMaker HyperPod cluster.
*)instance_groups : cluster_instance_group_specifications option;Specify the instance groups to update.
*)cluster_name : cluster_name_or_arn;Specify the name of the SageMaker HyperPod cluster you want to update.
*)}type nonrec update_artifact_response = {artifact_arn : artifact_arn option;The Amazon Resource Name (ARN) of the artifact.
*)}type nonrec artifact_properties =
(string_parameter_value * artifact_property_value) listtype nonrec update_artifact_request = {properties_to_remove : list_lineage_entity_parameter_key option;A list of properties to remove.
*)properties : artifact_properties option;The new list of properties. Overwrites the current property list.
*)artifact_name : experiment_entity_name option;The new name for the artifact.
*)artifact_arn : artifact_arn;The Amazon Resource Name (ARN) of the artifact to update.
*)}type nonrec update_app_image_config_response = {app_image_config_arn : app_image_config_arn option;The ARN for the AppImageConfig.
*)}type nonrec kernel_spec = {display_name : kernel_display_name option;The display name of the kernel.
*)name : kernel_name;The name of the Jupyter kernel in the image. This value is case sensitive.
*)}The specification of a Jupyter kernel.
type nonrec kernel_specs = kernel_spec listtype nonrec file_system_config = {default_gid : default_gid option;The default POSIX group ID (GID). If not specified, defaults to 100.
default_uid : default_uid option;The default POSIX user ID (UID). If not specified, defaults to 1000.
mount_path : mount_path option;The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
*)}The Amazon Elastic File System storage configuration for a SageMaker AI image.
type nonrec kernel_gateway_image_config = {file_system_config : file_system_config option;The Amazon Elastic File System storage configuration for a SageMaker AI image.
*)kernel_specs : kernel_specs;The specification of the Jupyter kernels in the image.
*)}The configuration for the file system and kernels in a SageMaker AI image running as a KernelGateway app.
type nonrec custom_image_container_arguments = non_empty_string64 listtype nonrec custom_image_container_entrypoint = non_empty_string256 listtype nonrec custom_image_container_environment_variables =
(non_empty_string256 * string256) listtype nonrec container_config = {container_environment_variables : custom_image_container_environment_variables
option;The environment variables to set in the container
*)container_entrypoint : custom_image_container_entrypoint option;The entrypoint used to run the application in the container.
*)container_arguments : custom_image_container_arguments option;The arguments for the container when you're running the application.
*)}The configuration used to run the application image container.
type nonrec jupyter_lab_app_image_config = {container_config : container_config option;file_system_config : file_system_config option;}The configuration for the file system and kernels in a SageMaker AI image running as a JupyterLab app. The FileSystemConfig object is not supported.
type nonrec code_editor_app_image_config = {container_config : container_config option;file_system_config : file_system_config option;}The configuration for the file system and kernels in a SageMaker image running as a Code Editor app. The FileSystemConfig object is not supported.
type nonrec update_app_image_config_request = {code_editor_app_image_config : code_editor_app_image_config option;The Code Editor app running on the image.
*)jupyter_lab_app_image_config : jupyter_lab_app_image_config option;The JupyterLab app running on the image.
*)kernel_gateway_image_config : kernel_gateway_image_config option;The new KernelGateway app to run on the image.
*)app_image_config_name : app_image_config_name;The name of the AppImageConfig to update.
*)}type nonrec update_action_response = {action_arn : action_arn option;The Amazon Resource Name (ARN) of the action.
*)}type nonrec update_action_request = {properties_to_remove : list_lineage_entity_parameter_key option;A list of properties to remove.
*)properties : lineage_entity_parameters option;The new list of properties. Overwrites the current property list.
*)status : action_status option;The new status for the action.
*)description : experiment_description option;The new description for the action.
*)action_name : experiment_entity_name;The name of the action to update.
*)}type nonrec ultra_server = {health_status : ultra_server_health_status option;The overall health status of the UltraServer.
*)unhealthy_instance_count : unhealthy_instance_count option;The number of instances in this UltraServer that are currently in an unhealthy state.
*)available_spare_instance_count : available_spare_instance_count option;The number of available spare instances in the UltraServer.
*)in_use_instance_count : in_use_instance_count option;The number of instances currently in use in this UltraServer.
*)available_instance_count : available_instance_count option;The number of instances currently available for use in this UltraServer.
*)configured_spare_instance_count : configured_spare_instance_count option;The number of spare instances configured for this UltraServer to provide enhanced resiliency.
*)total_instance_count : total_instance_count;The total number of instances in this UltraServer.
*)instance_type : reserved_capacity_instance_type;The Amazon EC2 instance type used in the UltraServer.
*)availability_zone : availability_zone;The name of the Availability Zone where the UltraServer is provisioned.
*)ultra_server_type : ultra_server_type;The type of UltraServer, such as ml.u-p6e-gb200x72.
*)ultra_server_id : non_empty_string256;The unique identifier for the UltraServer.
*)}Represents a high-performance compute server used for distributed training in SageMaker AI. An UltraServer consists of multiple instances within a shared NVLink interconnect domain.
type nonrec ultra_servers = ultra_server listtype nonrec ultra_server_summary = {unhealthy_instance_count : unhealthy_instance_count option;The total number of instances across all UltraServers of this type that are currently in an unhealthy state.
*)available_spare_instance_count : available_spare_instance_count option;The number of available spare instances in the UltraServers.
*)ultra_server_count : ultra_server_count option;The number of UltraServers of this type.
*)instance_type : reserved_capacity_instance_type;The Amazon EC2 instance type used in the UltraServer.
*)ultra_server_type : ultra_server_type;The type of UltraServer, such as ml.u-p6e-gb200x72.
*)}A summary of UltraServer resources and their current status.
type nonrec ultra_server_info = {type_ : Smaws_Lib.Smithy_api.Types.string_ option;The type of the UltraServer.
*)id : Smaws_Lib.Smithy_api.Types.string_ option;The unique identifier of the UltraServer.
*)}Contains information about the UltraServer object.
type nonrec ui_template_info = {content_sha256 : template_content_sha256 option;The SHA-256 digest of the contents of the template.
*)url : template_url option;The URL for the user interface template.
*)}Container for user interface template information.
type nonrec ui_template = {content : template_content;The content of the Liquid template for the worker user interface.
*)}The Liquid template for the worker user interface.
type nonrec ui_config = {human_task_ui_arn : human_task_ui_arn option;The ARN of the worker task template used to render the worker UI and tools for labeling job tasks.
Use this parameter when you are creating a labeling job for named entity recognition, 3D point cloud and video frame labeling jobs. Use your labeling job task type to select one of the following ARNs and use it with this parameter when you create a labeling job. Replace aws-region with the Amazon Web Services Region you are creating your labeling job in. For example, replace aws-region with us-west-1 if you create a labeling job in US West (N. California).
Named Entity Recognition
Use the following HumanTaskUiArn for named entity recognition labeling jobs:
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/NamedEntityRecognition
3D Point Cloud HumanTaskUiArns
Use this HumanTaskUiArn for 3D point cloud object detection and 3D point cloud object detection adjustment labeling jobs.
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectDetectionUse this HumanTaskUiArn for 3D point cloud object tracking and 3D point cloud object tracking adjustment labeling jobs.
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectTrackingUse this HumanTaskUiArn for 3D point cloud semantic segmentation and 3D point cloud semantic segmentation adjustment labeling jobs.
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudSemanticSegmentationVideo Frame HumanTaskUiArns
Use this HumanTaskUiArn for video frame object detection and video frame object detection adjustment labeling jobs.
arn:aws:sagemaker:region:394669845002:human-task-ui/VideoObjectDetectionUse this HumanTaskUiArn for video frame object tracking and video frame object tracking adjustment labeling jobs.
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/VideoObjectTrackingui_template_s3_uri : s3_uri option;The Amazon S3 bucket location of the UI template, or worker task template. This is the template used to render the worker UI and tools for labeling job tasks. For more information about the contents of a UI template, see Creating Your Custom Labeling Task Template.
*)}Provided configuration information for the worker UI for a labeling job. Provide either HumanTaskUiArn or UiTemplateS3Uri.
For named entity recognition, 3D point cloud and video frame labeling jobs, use HumanTaskUiArn.
For all other Ground Truth built-in task types and custom task types, use UiTemplateS3Uri to specify the location of a worker task template in Amazon S3.
type nonrec us_d = {tenth_fractions_of_a_cent : tenth_fractions_of_a_cent option;Fractions of a cent, in tenths.
*)cents : cents option;The fractional portion, in cents, of the amount.
*)dollars : dollars option;The whole number of dollars in the amount.
*)}Represents an amount of money in United States dollars.
type nonrec tuning_job_step_meta_data = {arn : hyper_parameter_tuning_job_arn option;The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.
*)}Metadata for a tuning step.
type nonrec best_objective_not_improving = {max_number_of_training_jobs_not_improving : max_number_of_training_jobs_not_improving
option;The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.
*)}A structure that keeps track of which training jobs launched by your hyperparameter tuning job are not improving model performance as evaluated against an objective function.
type nonrec convergence_detected = {complete_on_convergence : complete_on_convergence option;A flag to stop a tuning job once AMT has detected that the job has converged.
*)}A flag to indicating that automatic model tuning (AMT) has detected model convergence, defined as a lack of significant improvement (1% or less) against an objective metric.
type nonrec tuning_job_completion_criteria = {convergence_detected : convergence_detected option;A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.
*)best_objective_not_improving : best_objective_not_improving option;A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function.
*)target_objective_metric_value : target_objective_metric_value option;The value of the objective metric.
*)}The job completion criteria.
type nonrec trial_source = {source_type : source_type option;The source job type.
*)source_arn : trial_source_arn;The Amazon Resource Name (ARN) of the source.
*)}The source of the trial.
type nonrec trial_summary = {last_modified_time : timestamp option;When the trial was last modified.
*)creation_time : timestamp option;When the trial was created.
*)trial_source : trial_source option;display_name : experiment_entity_name option;The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
trial_name : experiment_entity_name option;The name of the trial.
*)trial_arn : trial_arn option;The Amazon Resource Name (ARN) of the trial.
*)}A summary of the properties of a trial. To get the complete set of properties, call the DescribeTrial API and provide the TrialName.
type nonrec trial_summaries = trial_summary listtype nonrec trial_component_source = {source_type : source_type option;The source job type.
*)source_arn : trial_component_source_arn;The source Amazon Resource Name (ARN).
*)}The Amazon Resource Name (ARN) and job type of the source of a trial component.
type nonrec trial_component_summary = {last_modified_by : user_context option;Who last modified the component.
*)last_modified_time : timestamp option;When the component was last modified.
*)created_by : user_context option;Who created the trial component.
*)creation_time : timestamp option;When the component was created.
*)end_time : timestamp option;When the component ended.
*)start_time : timestamp option;When the component started.
*)status : trial_component_status option;The status of the component. States include:
trial_component_source : trial_component_source option;display_name : experiment_entity_name option;The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
trial_component_arn : trial_component_arn option;The Amazon Resource Name (ARN) of the trial component.
*)trial_component_name : experiment_entity_name option;The name of the trial component.
*)}A summary of the properties of a trial component. To get all the properties, call the DescribeTrialComponent API and provide the TrialComponentName.
type nonrec trial_component_summaries = trial_component_summary listtype nonrec trial_component_sources = trial_component_source listtype nonrec model_artifacts = {s3_model_artifacts : s3_uri;The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz.
}Provides information about the location that is configured for storing model artifacts.
Model artifacts are outputs that result from training a model. They typically consist of trained parameters, a model definition that describes how to compute inferences, and other metadata. A SageMaker container stores your trained model artifacts in the /opt/ml/model directory. After training has completed, by default, these artifacts are uploaded to your Amazon S3 bucket as compressed files.
type nonrec hyper_parameters =
(hyper_parameter_key * hyper_parameter_value) listThe training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
type nonrec metric_definition = {regex : metric_regex;A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
*)name : metric_name;The name of the metric.
*)}Specifies a metric that the training algorithm writes to stderr or stdout. You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
type nonrec metric_definition_list = metric_definition listtype nonrec training_container_entrypoint =
training_container_entrypoint_string listtype nonrec training_container_arguments = training_container_argument listtype nonrec training_repository_auth_config = {training_repository_credentials_provider_arn : training_repository_credentials_provider_arn;The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
*)}An object containing authentication information for a private Docker registry.
type nonrec training_image_config = {training_repository_auth_config : training_repository_auth_config option;An object containing authentication information for a private Docker registry containing your training images.
*)training_repository_access_mode : training_repository_access_mode;The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to Vpc.
}The configuration to use an image from a private Docker registry for a training job.
type nonrec algorithm_specification = {training_image_config : training_image_config option;The configuration to use an image from a private Docker registry for a training job.
*)container_arguments : training_container_arguments option;The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
*)container_entrypoint : training_container_entrypoint option;The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
*)enable_sage_maker_metrics_time_series : boolean_ option;To generate and save time-series metrics during training, set to true. The default is false and time-series metrics aren't generated except in the following cases:
You use one of the following Prebuilt SageMaker Docker Images:
metric_definitions : metric_definition_list option;A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
*)training_input_mode : training_input_mode;algorithm_name : arn_or_name option;The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage, and vice versa.
If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.
training_image : algorithm_image option;The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
For more information, see the note in the AlgorithmName parameter description.
}Specifies the training algorithm to use in a CreateTrainingJob request.
SageMaker uses its own SageMaker account credentials to pull and access built-in algorithms so built-in algorithms are universally accessible across all Amazon Web Services accounts. As a result, built-in algorithms have standard, unrestricted access. You cannot restrict built-in algorithms using IAM roles. Use custom algorithms if you require specific access controls.
For more information about algorithms provided by SageMaker, see Algorithms. For information about using your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
type nonrec attribute_names = attribute_name listtype nonrec instance_group_names = instance_group_name listtype nonrec hub_access_config = {hub_content_arn : hub_content_arn;The ARN of your private model hub content. This should be a ModelReference resource type that points to a SageMaker JumpStart public hub model.
}The configuration for a private hub model reference that points to a public SageMaker JumpStart model.
For more information about private hubs, see Private curated hubs for foundation model access control in JumpStart.
type nonrec s3_data_source = {hub_access_config : hub_access_config option;The configuration for a private hub model reference that points to a SageMaker JumpStart public hub model.
*)model_access_config : model_access_config option;instance_group_names : instance_group_names option;A list of names of instance groups that get data from the S3 data source.
*)attribute_names : attribute_names option;A list of one or more attribute names to use that are found in a specified augmented manifest file.
*)s3_data_distribution_type : s3_data_distribution option;If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated.
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File), this copies 1/n of the number of objects.
s3_uri : s3_uri;Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:
s3://bucketname/exampleprefix/A manifest might look like this: s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri. Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets.
The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following S3Uri list:
s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
*)s3_data_type : s3_data_type;If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe.
If you choose Converse, S3Uri identifies an Amazon S3 location that contains data formatted according to Converse format. This format structures conversational messages with specific roles and content types used for training and fine-tuning foundational models.
}Describes the S3 data source.
Your input bucket must be in the same Amazon Web Services region as your training job.
type nonrec file_system_data_source = {directory_path : directory_path;The full path to the directory to associate with the channel.
*)file_system_type : file_system_type;The file system type.
*)file_system_access_mode : file_system_access_mode;The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
file_system_id : file_system_id;The file system id.
*)}Specifies a file system data source for a channel.
type nonrec dataset_source = {dataset_arn : hub_data_set_arn;The Amazon Resource Name (ARN) of the dataset resource.
*)}Specifies a dataset source for a channel.
type nonrec data_source = {dataset_source : dataset_source option;The dataset resource that's associated with a channel.
*)file_system_data_source : file_system_data_source option;The file system that is associated with a channel.
*)s3_data_source : s3_data_source option;The S3 location of the data source that is associated with a channel.
*)}Describes the location of the channel data.
type nonrec seed = Smaws_Lib.CoreTypes.Int64.ttype nonrec shuffle_config = {seed : seed;Determines the shuffling order in ShuffleConfig value.
}A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, the results of the S3 key prefix matches are shuffled. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, when ShuffleConfig is specified shuffling is done at the start of every epoch. With large datasets, this ensures that the order of the training data is different for each epoch, and it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
type nonrec channel = {shuffle_config : shuffle_config option;A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
input_mode : training_input_mode option;(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
record_wrapper_type : record_wrapper option;Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
*)compression_type : compression_type option;If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
content_type : content_type option;The MIME type of the data.
*)data_source : data_source;The location of the channel data.
*)channel_name : channel_name;The name of the channel.
*)}A channel is a named input source that training algorithms can consume.
type nonrec input_data_config = channel listtype nonrec output_data_config = {compression_type : output_compression_type option;The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
s3_output_path : s3_uri;Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias
"alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob, CreateTransformJob, or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
}Provides information about how to store model training results (model artifacts).
type nonrec training_instance_type = | ML_G7E_48XLARGE| ML_G7E_24XLARGE| ML_G7E_12XLARGE| ML_G7E_8XLARGE| ML_G7E_4XLARGE| ML_G7E_2XLARGE| ML_P6_B300_48XLARGE| ML_P5_4XLARGE| ML_P6E_GB200_36XLARGE| ML_R7I_48XLARGE| ML_R7I_24XLARGE| ML_R7I_16XLARGE| ML_R7I_12XLARGE| ML_R7I_8XLARGE| ML_R7I_4XLARGE| ML_R7I_2XLARGE| ML_R7I_XLARGE| ML_R7I_LARGE| ML_C7I_48XLARGE| ML_C7I_24XLARGE| ML_C7I_16XLARGE| ML_C7I_12XLARGE| ML_C7I_8XLARGE| ML_C7I_4XLARGE| ML_C7I_2XLARGE| ML_C7I_XLARGE| ML_C7I_LARGE| ML_M7I_48XLARGE| ML_M7I_24XLARGE| ML_M7I_16XLARGE| ML_M7I_12XLARGE| ML_M7I_8XLARGE| ML_M7I_4XLARGE| ML_M7I_2XLARGE| ML_M7I_XLARGE| ML_M7I_LARGE| ML_P6_B200_48XLARGE| ML_R5_24XLARGE| ML_R5_16XLARGE| ML_R5_12XLARGE| ML_R5_8XLARGE| ML_R5_4XLARGE| ML_R5_2XLARGE| ML_R5_XLARGE| ML_R5_LARGE| ML_T3_2XLARGE| ML_T3_XLARGE| ML_T3_LARGE| ML_T3_MEDIUM| ML_R5D_24XLARGE| ML_R5D_16XLARGE| ML_R5D_12XLARGE| ML_R5D_8XLARGE| ML_R5D_4XLARGE| ML_R5D_2XLARGE| ML_R5D_XLARGE| ML_R5D_LARGE| ML_C6I_32XLARGE| ML_C6I_24XLARGE| ML_C6I_16XLARGE| ML_C6I_12XLARGE| ML_C6I_4XLARGE| ML_C6I_8XLARGE| ML_C6I_2XLARGE| ML_C6I_XLARGE| ML_M6I_32XLARGE| ML_M6I_24XLARGE| ML_M6I_16XLARGE| ML_M6I_12XLARGE| ML_M6I_8XLARGE| ML_M6I_4XLARGE| ML_M6I_2XLARGE| ML_M6I_XLARGE| ML_M6I_LARGE| ML_TRN2_48XLARGE| ML_TRN1N_32XLARGE| ML_TRN1_32XLARGE| ML_TRN1_2XLARGE| ML_G6E_48XLARGE| ML_G6E_24XLARGE| ML_G6E_12XLARGE| ML_G6E_16XLARGE| ML_G6E_8XLARGE| ML_G6E_4XLARGE| ML_G6E_2XLARGE| ML_G6E_XLARGE| ML_G6_48XLARGE| ML_G6_24XLARGE| ML_G6_12XLARGE| ML_G6_16XLARGE| ML_G6_8XLARGE| ML_G6_4XLARGE| ML_G6_2XLARGE| ML_G6_XLARGE| ML_G5_48XLARGE| ML_G5_24XLARGE| ML_G5_12XLARGE| ML_G5_16XLARGE| ML_G5_8XLARGE| ML_G5_4XLARGE| ML_G5_2XLARGE| ML_G5_XLARGE| ML_C5N_18XLARGE| ML_C5N_9XLARGE| ML_C5N_4XLARGE| ML_C5N_2XLARGE| ML_C5N_XLARGE| ML_C5_18XLARGE| ML_C5_9XLARGE| ML_C5_4XLARGE| ML_C5_2XLARGE| ML_C5_XLARGE| ML_P5EN_48XLARGE| ML_P5E_48XLARGE| ML_P5_48XLARGE| ML_P4DE_24XLARGE| ML_P4D_24XLARGE| ML_P3DN_24XLARGE| ML_P3_16XLARGE| ML_P3_8XLARGE| ML_P3_2XLARGE| ML_P2_16XLARGE| ML_P2_8XLARGE| ML_P2_XLARGE| ML_C4_8XLARGE| ML_C4_4XLARGE| ML_C4_2XLARGE| ML_C4_XLARGE| ML_M5_24XLARGE| ML_M5_12XLARGE| ML_M5_4XLARGE| ML_M5_2XLARGE| ML_M5_XLARGE| ML_M5_LARGE| ML_G4DN_16XLARGE| ML_G4DN_12XLARGE| ML_G4DN_8XLARGE| ML_G4DN_4XLARGE| ML_G4DN_2XLARGE| ML_G4DN_XLARGE| ML_M4_16XLARGE| ML_M4_10XLARGE| ML_M4_4XLARGE| ML_M4_2XLARGE| ML_M4_XLARGEtype nonrec instance_group = {instance_group_name : instance_group_name;Specifies the name of the instance group.
*)instance_count : training_instance_count;Specifies the number of instances of the instance group.
*)instance_type : training_instance_type;Specifies the instance type of the instance group.
*)}Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
type nonrec instance_groups = instance_group listtype nonrec placement_specification = {instance_count : training_instance_count;The number of ML compute instances required to be placed together on the same UltraServer. Minimum value of 1.
*)ultra_server_id : string256 option;The unique identifier of the UltraServer where instances should be placed.
*)}Specifies how instances should be placed on a specific UltraServer.
type nonrec placement_specifications = placement_specification listtype nonrec instance_placement_config = {placement_specifications : placement_specifications option;A list of specifications for how instances should be placed on specific UltraServers. Maximum of 10 items is supported.
*)enable_multiple_jobs : boolean_ option;If set to true, allows multiple jobs to share the same UltraServer instances. If set to false, ensures this job's instances are placed on an UltraServer exclusively, with no other jobs sharing the same UltraServer. Default is false.
*)}Configuration for how instances are placed and allocated within UltraServers. This is only applicable for UltraServer capacity.
type nonrec resource_config = {instance_placement_config : instance_placement_config option;Configuration for how training job instances are placed and allocated within UltraServers. Only applicable for UltraServer capacity.
*)training_plan_arn : training_plan_arn option;The Amazon Resource Name (ARN); of the training plan to use for this resource configuration.
*)instance_groups : instance_groups option;The configuration of a heterogeneous cluster in JSON format.
*)keep_alive_period_in_seconds : keep_alive_period_in_seconds option;The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
*)volume_kms_key_id : kms_key_id option;The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes.
For more information about local instance storage encryption, see SSD Instance Store Volumes.
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
volume_size_in_g_b : optional_volume_size_in_g_b option;The size of the ML storage volume that you want to provision.
SageMaker automatically selects the volume size for serverless training jobs. You cannot customize this setting.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d, ml.g4dn, and ml.g5.
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2.
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
*)instance_count : training_instance_count option;The number of ML compute instances to use. For distributed training, provide a value greater than 1.
*)instance_type : training_instance_type option;The ML compute instance type.
*)}Describes the resources, including machine learning (ML) compute instances and ML storage volumes, to use for model training.
type nonrec stopping_condition = {max_pending_time_in_seconds : max_pending_time_in_seconds option;The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
When working with training jobs that use capacity from training plans, not all Pending job states count against the MaxPendingTimeInSeconds limit. The following scenarios do not increment the MaxPendingTimeInSeconds counter:
Scheduled state: Jobs queued (in Pending status) before a plan's start date (waiting for scheduled start time)Pending status between two capacity reservation periodsMaxPendingTimeInSeconds only increments when jobs are actively waiting for capacity in an Active plan.
max_wait_time_in_seconds : max_wait_time_in_seconds option;The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds. If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
max_runtime_in_seconds : max_runtime_in_seconds option;The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
}Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.
To stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel.
The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.
type nonrec secondary_status_transition = {status_message : status_message option;A detailed description of the progress within a secondary status.
SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Training
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For example, at the start of a training job, you might see the following:
TrainingJobStatus - InProgressSecondaryStatus - TrainingStatusMessage - Downloading the training imageend_time : timestamp option;A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
*)start_time : timestamp;A timestamp that shows when the training job transitioned to the current secondary status state.
*)status : secondary_status;Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
InProgress
Starting - Starting the training job.Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.Training - Training is in progress.Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.Completed
Completed - The training job has completed.Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.Stopped - The training job has stopped.Stopping
Stopping - Stopping the training job.We no longer support the following secondary statuses:
LaunchingMLInstancesPreparingTrainingStackDownloadingTrainingImage}An array element of SecondaryStatusTransitions for DescribeTrainingJob. It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
type nonrec secondary_status_transitions = secondary_status_transition listtype nonrec metric_data = {timestamp : timestamp option;The date and time that the algorithm emitted the metric.
*)value : float_ option;The value of the metric.
*)metric_name : metric_name option;The name of the metric.
*)}The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
type nonrec final_metric_data_list = metric_data listtype nonrec checkpoint_config = {local_path : directory_path option;(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/.
s3_uri : s3_uri;Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix.
}Contains information about the output location for managed spot training checkpoint data.
type nonrec hook_parameters = (config_key * config_value) listtype nonrec collection_parameters = (config_key * config_value) listtype nonrec collection_configuration = {collection_parameters : collection_parameters option;Parameter values for the tensor collection. The allowed parameters are "name", "include_regex", "reduction_config", "save_config", "tensor_names", and "save_histogram".
collection_name : collection_name option;The name of the tensor collection. The name must be unique relative to other rule configuration names.
*)}Configuration information for the Amazon SageMaker Debugger output tensor collections.
type nonrec collection_configurations = collection_configuration listtype nonrec debug_hook_config = {collection_configurations : collection_configurations option;Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
hook_parameters : hook_parameters option;Configuration information for the Amazon SageMaker Debugger hook parameters.
*)s3_output_path : s3_uri;Path to Amazon S3 storage location for metrics and tensors.
*)local_path : directory_path option;Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/.
}Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
type nonrec experiment_config = {run_name : experiment_entity_name option;The name of the experiment run to associate with the trial component.
*)trial_component_display_name : experiment_entity_name option;The display name for the trial component. If this key isn't specified, the display name is the trial component name.
*)trial_name : experiment_entity_name option;The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
*)experiment_name : experiment_entity_name option;The name of an existing experiment to associate with the trial component.
*)}Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
type nonrec debug_rule_configuration = {rule_parameters : rule_parameters option;Runtime configuration for rule container.
*)volume_size_in_g_b : optional_volume_size_in_g_b option;The size, in GB, of the ML storage volume attached to the processing instance.
*)instance_type : processing_instance_type option;The instance type to deploy a custom rule for debugging a training job.
*)rule_evaluator_image : algorithm_image;The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
*)s3_output_path : s3_uri option;Path to Amazon S3 storage location for rules.
*)local_path : directory_path option;Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.
rule_configuration_name : rule_configuration_name;The name of the rule configuration. It must be unique relative to other rule configuration names.
*)}Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
type nonrec debug_rule_configurations = debug_rule_configuration listtype nonrec tensor_board_output_config = {s3_output_path : s3_uri;Path to Amazon S3 storage location for TensorBoard output.
*)local_path : directory_path option;Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard.
}Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
type nonrec debug_rule_evaluation_status = {last_modified_time : timestamp option;Timestamp when the rule evaluation status was last modified.
*)status_details : status_details option;Details from the rule evaluation.
*)rule_evaluation_status : rule_evaluation_status option;Status of the rule evaluation.
*)rule_evaluation_job_arn : processing_job_arn option;The Amazon Resource Name (ARN) of the rule evaluation job.
*)rule_configuration_name : rule_configuration_name option;The name of the rule configuration.
*)}Information about the status of the rule evaluation.
type nonrec debug_rule_evaluation_statuses = debug_rule_evaluation_status listtype nonrec model_package_config = {source_model_package_arn : model_package_arn option;The Amazon Resource Name (ARN) of the source model package used for continued fine-tuning and custom model evaluation.
*)model_package_group_arn : model_package_group_arn;The Amazon Resource Name (ARN) of the model package group of output model package.
*)}The configuration for the Model package.
type nonrec profiler_config = {disable_profiler : disable_profiler option;Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to True.
profiling_parameters : profiling_parameters option;Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig, PythonProfilingConfig, and DataLoaderProfilingConfig. The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
profiling_interval_in_milliseconds : profiling_interval_in_milliseconds option;A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
*)s3_output_path : s3_uri option;Path to Amazon S3 storage location for system and framework metrics.
*)}Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
type nonrec training_environment_map =
(training_environment_key * training_environment_value) listtype nonrec retry_strategy = {maximum_retry_attempts : maximum_retry_attempts;The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING.
}The retry strategy to use when a training job fails due to an InternalServerError. RetryStrategy is specified as part of the CreateTrainingJob and CreateHyperParameterTuningJob requests. You can add the StoppingCondition parameter to the request to limit the training time for the complete job.
type nonrec training_job = {retry_strategy : retry_strategy option;The number of times to retry the job when the job fails due to an InternalServerError.
environment : training_environment_map option;The environment variables to set in the Docker container.
*)profiler_config : profiler_config option;model_package_config : model_package_config option;The model package configuration.
*)output_model_package_arn : model_package_arn option;The output model package Amazon Resource Name (ARN) that contains model weights or checkpoint.
*)debug_rule_evaluation_statuses : debug_rule_evaluation_statuses option;Information about the evaluation status of the rules for the training job.
*)tensor_board_output_config : tensor_board_output_config option;debug_rule_configurations : debug_rule_configurations option;Information about the debug rule configuration.
*)experiment_config : experiment_config option;debug_hook_config : debug_hook_config option;billable_time_in_seconds : billable_time_in_seconds option;The billable time in seconds.
*)training_time_in_seconds : training_time_in_seconds option;The training time in seconds.
*)checkpoint_config : checkpoint_config option;enable_managed_spot_training : boolean_ option;When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training.
*)enable_inter_container_traffic_encryption : boolean_ option;To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
enable_network_isolation : boolean_ option;If the TrainingJob was created with network isolation, the value is set to true. If network isolation is enabled, nodes can't communicate beyond the VPC they run in.
final_metric_data_list : final_metric_data_list option;A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
*)secondary_status_transitions : secondary_status_transitions option;A history of all of the secondary statuses that the training job has transitioned through.
*)last_modified_time : timestamp option;A timestamp that indicates when the status of the training job was last modified.
*)training_end_time : timestamp option;Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
training_start_time : timestamp option;Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
creation_time : timestamp option;A timestamp that indicates when the training job was created.
*)stopping_condition : stopping_condition option;Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
vpc_config : vpc_config option;A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
*)warm_pool_status : warm_pool_status option;The status of the warm pool associated with the training job.
*)resource_config : resource_config option;Resources, including ML compute instances and ML storage volumes, that are configured for model training.
*)output_data_config : output_data_config option;The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.
*)input_data_config : input_data_config option;An array of Channel objects that describes each data input channel.
Your input must be in the same Amazon Web Services region as your training job.
*)role_arn : role_arn option;The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
*)algorithm_specification : algorithm_specification option;Information about the algorithm used for training, and algorithm metadata.
*)hyper_parameters : hyper_parameters option;Algorithm-specific parameters.
*)failure_reason : failure_reason option;If the training job failed, the reason it failed.
*)secondary_status : secondary_status option;Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition.
SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.Training - Training is in progress.Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.Completed
Completed - The training job has completed.Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.Stopped - The training job has stopped.Stopping
Stopping - Stopping the training job.Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstancesPreparingTrainingStackDownloadingTrainingImagetraining_job_status : training_job_status option;The status of the training job.
Training job statuses are:
InProgress - The training is in progress.Completed - The training job has completed.Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.Stopping - The training job is stopping.Stopped - The training job has stopped.For more detailed information, see SecondaryStatus.
model_artifacts : model_artifacts option;Information about the Amazon S3 location that is configured for storing model artifacts.
*)auto_ml_job_arn : auto_ml_job_arn option;The Amazon Resource Name (ARN) of the job.
*)labeling_job_arn : labeling_job_arn option;The Amazon Resource Name (ARN) of the labeling job.
*)tuning_job_arn : hyper_parameter_tuning_job_arn option;The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
*)training_job_arn : training_job_arn option;The Amazon Resource Name (ARN) of the training job.
*)training_job_name : training_job_name option;The name of the training job.
*)}Contains information about a training job.
type nonrec processing_s3_input = {s3_compression_type : processing_s3_compression_type option;Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container. Gzip can only be used when Pipe mode is specified as the S3InputMode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume.
s3_data_distribution_type : processing_s3_data_distribution_type option;Whether to distribute the data from Amazon S3 to all processing instances with FullyReplicated, or whether the data from Amazon S3 is sharded by Amazon S3 key, downloading one shard of data to each processing instance.
s3_input_mode : processing_s3_input_mode option;Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume.
s3_data_type : processing_s3_data_type;Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.
local_path : processing_local_path option;The local path in your container where you want Amazon SageMaker to write input data to. LocalPath is an absolute path to the input data and must begin with /opt/ml/processing/. LocalPath is a required parameter when AppManaged is False (default).
s3_uri : s3_uri;The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
*)}Configuration for downloading input data from Amazon S3 into the processing container.
The data storage format for Athena query results.
The compression used for Athena query results.
type nonrec athena_dataset_definition = {output_compression : athena_result_compression_type option;output_format : athena_result_format;kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
*)output_s3_uri : s3_uri;The location in Amazon S3 where Athena query results are stored.
*)work_group : athena_work_group option;query_string : athena_query_string;database : athena_database;catalog : athena_catalog;}Configuration for Athena Dataset Definition input.
The data storage format for Redshift query results.
The compression used for Redshift query results.
type nonrec redshift_dataset_definition = {output_compression : redshift_result_compression_type option;output_format : redshift_result_format;kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
*)output_s3_uri : s3_uri;The location in Amazon S3 where the Redshift query results are stored.
*)cluster_role_arn : role_arn;The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
*)query_string : redshift_query_string;db_user : redshift_user_name;database : redshift_database;cluster_id : redshift_cluster_id;}Configuration for Redshift Dataset Definition input.
type nonrec dataset_definition = {input_mode : input_mode option;Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
data_distribution_type : data_distribution_type option;Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).
local_path : processing_local_path option;The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
redshift_dataset_definition : redshift_dataset_definition option;athena_dataset_definition : athena_dataset_definition option;}Configuration for Dataset Definition inputs. The Dataset Definition input must specify exactly one of either AthenaDatasetDefinition or RedshiftDatasetDefinition types.
type nonrec processing_input = {dataset_definition : dataset_definition option;Configuration for a Dataset Definition input.
*)s3_input : processing_s3_input option;Configuration for downloading input data from Amazon S3 into the processing container.
*)app_managed : app_managed option;When True, input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker.
input_name : string_;The name for the processing job input.
*)}The inputs for a processing job. The processing input must specify exactly one of either S3Input or DatasetDefinition types.
type nonrec processing_inputs = processing_input listtype nonrec processing_s3_output = {s3_upload_mode : processing_s3_upload_mode;Whether to upload the results of the processing job continuously or after the job completes.
*)local_path : processing_local_path option;The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3. LocalPath is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked.
s3_uri : s3_uri;A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
*)}Configuration for uploading output data to Amazon S3 from the processing container.
type nonrec processing_feature_store_output = {feature_group_name : feature_group_name;The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
*)}Configuration for processing job outputs in Amazon SageMaker Feature Store.
type nonrec processing_output = {app_managed : app_managed option;When True, output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker.
feature_store_output : processing_feature_store_output option;Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified.
s3_output : processing_s3_output option;Configuration for processing job outputs in Amazon S3.
*)output_name : string_;The name for the processing job output.
*)}Describes the results of a processing job. The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.
type nonrec processing_outputs = processing_output listtype nonrec processing_output_config = {kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.
outputs : processing_outputs;An array of outputs configuring the data to upload from the processing container.
*)}Configuration for uploading output from the processing container.
type nonrec processing_cluster_config = {volume_kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes.
For more information about local instance storage encryption, see SSD Instance Store Volumes.
*)volume_size_in_g_b : processing_volume_size_in_g_b;The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.
*)instance_type : processing_instance_type;The ML compute instance type for the processing job.
*)instance_count : processing_instance_count;The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
*)}Configuration for the cluster used to run a processing job.
type nonrec processing_resources = {cluster_config : processing_cluster_config;The configuration for the resources in a cluster used to run the processing job.
*)}Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
type nonrec processing_stopping_condition = {max_runtime_in_seconds : processing_max_runtime_in_seconds;Specifies the maximum runtime in seconds.
*)}Configures conditions under which the processing job should be stopped, such as how long the processing job has been running. After the condition is met, the processing job is stopped.
type nonrec container_arguments = container_argument listtype nonrec app_specification = {container_arguments : container_arguments option;The arguments for a container used to run a processing job.
*)container_entrypoint : container_entrypoint option;The entrypoint for a container used to run a processing job.
*)image_uri : image_uri;The container image to be run by the processing job.
*)}Configuration to run a processing job in a specified container image.
type nonrec processing_environment_map =
(processing_environment_key * processing_environment_value) listtype nonrec processing_job = {training_job_arn : training_job_arn option;The ARN of the training job associated with this processing job.
*)auto_ml_job_arn : auto_ml_job_arn option;The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.
*)monitoring_schedule_arn : monitoring_schedule_arn option;The ARN of a monitoring schedule for an endpoint associated with this processing job.
*)creation_time : timestamp option;The time the processing job was created.
*)last_modified_time : timestamp option;The time the processing job was last modified.
*)processing_start_time : timestamp option;The time that the processing job started.
*)processing_end_time : timestamp option;The time that the processing job ended.
*)failure_reason : failure_reason option;A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
*)exit_message : exit_message option;A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
*)processing_job_status : processing_job_status option;The status of the processing job.
*)processing_job_arn : processing_job_arn option;The ARN of the processing job.
*)experiment_config : experiment_config option;role_arn : role_arn option;The ARN of the role used to create the processing job.
*)network_config : network_config option;environment : processing_environment_map option;Sets the environment variables in the Docker container.
*)app_specification : app_specification option;stopping_condition : processing_stopping_condition option;processing_resources : processing_resources option;processing_job_name : processing_job_name option;The name of the processing job.
*)processing_output_config : processing_output_config option;processing_inputs : processing_inputs option;List of input configurations for the processing job.
*)}An Amazon SageMaker processing job that is used to analyze data and evaluate models. For more information, see Process Data and Evaluate Models.
type nonrec model_client_config = {invocations_max_retries : invocations_max_retries option;The maximum number of retries when invocation requests are failing. The default value is 3.
*)invocations_timeout_in_seconds : invocations_timeout_in_seconds option;The timeout value in seconds for an invocation request. The default value is 600.
*)}Configures the timeout and maximum number of retries for processing a transform job invocation.
type nonrec transform_environment_map =
(transform_environment_key * transform_environment_value) listtype nonrec transform_s3_data_source = {s3_uri : s3_uri;Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:
s3://bucketname/exampleprefix/.A manifest might look like this: s3://bucketname/example.manifest
The manifest is an S3 object which is a JSON file with the following format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
The preceding JSON matches the following S3Uris:
s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
s3_data_type : s3_data_type;If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile, S3Prefix
The following value is not compatible: AugmentedManifestFile
}Describes the S3 data source.
type nonrec transform_data_source = {s3_data_source : transform_s3_data_source;The S3 location of the data source that is associated with a channel.
*)}Describes the location of the channel data.
type nonrec transform_input = {split_type : split_type option;The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord. Padding is not removed if the value of BatchStrategy is set to MultiRecord.
For more information about RecordIO, see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord, see Consuming TFRecord data in the TensorFlow documentation.
compression_type : compression_type option;If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.
content_type : content_type option;The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
*)data_source : transform_data_source;Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
*)}Describes the input source of a transform job and the way the transform job consumes it.
type nonrec transform_output = {kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
1234abcd-12ab-34cd-56ef-1234567890abarn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890abalias/ExampleAliasarn:aws:kms:us-west-2:111122223333:alias/ExampleAliasIf you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
*)assemble_with : assembly_type option;Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None. To add a newline character at the end of every transformed record, specify Line.
accept : accept option;The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
*)s3_output_path : s3_uri;The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix.
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .out suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv, batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
}Describes the results of a transform job.
type nonrec batch_data_capture_config = {generate_inference_id : boolean_ option;Flag that indicates whether to append inference id to the output.
*)kms_key_id : kms_key_id option;The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the batch transform job.
The KmsKeyId can be any of the following formats:
1234abcd-12ab-34cd-56ef-1234567890abarn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890abalias/ExampleAliasarn:aws:kms:us-west-2:111122223333:alias/ExampleAliasdestination_s3_uri : s3_uri;The Amazon S3 location being used to capture the data.
*)}Configuration to control how SageMaker captures inference data for batch transform jobs.
type nonrec transform_resources = {transform_ami_version : transform_ami_version option;Specifies an option from a collection of preconfigured Amazon Machine Image (AMI) images. Each image is configured by Amazon Web Services with a set of software and driver versions.
al2-ami-sagemaker-batch-gpu-470
al2-ami-sagemaker-batch-gpu-535
volume_kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes.
For more information about local instance storage encryption, see SSD Instance Store Volumes.
The VolumeKmsKeyId can be any of the following formats:
1234abcd-12ab-34cd-56ef-1234567890abarn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890abalias/ExampleAliasarn:aws:kms:us-west-2:111122223333:alias/ExampleAliasinstance_count : transform_instance_count;The number of ML compute instances to use in the transform job. The default value is 1, and the maximum is 100. For distributed transform jobs, specify a value greater than 1.
instance_type : transform_instance_type;The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.largeinstance types.
}Describes the resources, including ML instance types and ML instance count, to use for transform job.
type nonrec data_processing = {join_source : join_source option;Specifies the source of the data to join with the transformed data. The valid values are None and Input. The default value is None, which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input. You can specify OutputFilter as an additional filter to select a portion of the joined dataset and store it in the output file.
For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput. The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput.
For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
*)output_filter : json_path option;A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default value, $. If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.
Examples: "$", "$[0,5:]", "$['id','SageMakerOutput']"
input_filter : json_path option;A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want SageMaker to pass the entire input dataset to the algorithm, accept the default value $.
Examples: "$", "$[1:]", "$.features"
}The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.
type nonrec transform_job = {experiment_config : experiment_config option;data_processing : data_processing option;auto_ml_job_arn : auto_ml_job_arn option;The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
*)labeling_job_arn : labeling_job_arn option;The Amazon Resource Name (ARN) of the labeling job that created the transform job.
*)transform_end_time : timestamp option;Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime.
transform_start_time : timestamp option;Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.
creation_time : timestamp option;A timestamp that shows when the transform Job was created.
*)transform_resources : transform_resources option;data_capture_config : batch_data_capture_config option;transform_output : transform_output option;transform_input : transform_input option;environment : transform_environment_map option;The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
*)batch_strategy : batch_strategy option;Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
*)max_payload_in_m_b : max_payload_in_m_b option;The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.
model_client_config : model_client_config option;max_concurrent_transforms : max_concurrent_transforms option;The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms.
model_name : model_name option;The name of the model associated with the transform job.
*)failure_reason : failure_reason option;If the transform job failed, the reason it failed.
*)transform_job_status : transform_job_status option;The status of the transform job.
Transform job statuses are:
InProgress - The job is in progress.Completed - The job has completed.Failed - The transform job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTransformJob call.Stopping - The transform job is stopping.Stopped - The transform job has stopped.transform_job_arn : transform_job_arn option;The Amazon Resource Name (ARN) of the transform job.
*)transform_job_name : transform_job_name option;The name of the transform job.
*)}A batch transform job. For information about SageMaker batch transform, see Use Batch Transform.
type nonrec trial_component_source_detail = {transform_job : transform_job option;Information about a transform job that's the source of a trial component.
*)processing_job : processing_job option;Information about a processing job that's the source of a trial component.
*)training_job : training_job option;Information about a training job that's the source of a trial component.
*)source_arn : trial_component_source_arn option;The Amazon Resource Name (ARN) of the source.
*)}Detailed information about the source of a trial component. Either ProcessingJob or TrainingJob is returned.
type nonrec trial_component_simple_summary = {created_by : user_context option;creation_time : timestamp option;When the component was created.
*)trial_component_source : trial_component_source option;trial_component_arn : trial_component_arn option;The Amazon Resource Name (ARN) of the trial component.
*)trial_component_name : experiment_entity_name option;The name of the trial component.
*)}A short summary of a trial component.
type nonrec trial_component_simple_summaries =
trial_component_simple_summary listtype nonrec trial_component_metric_summary = {std_dev : optional_double option;The standard deviation of the metric.
*)avg : optional_double option;The average value of the metric.
*)count : optional_integer option;The number of samples used to generate the metric.
*)last : optional_double option;The most recent value of the metric.
*)min : optional_double option;The minimum value of the metric.
*)max : optional_double option;The maximum value of the metric.
*)time_stamp : timestamp option;When the metric was last updated.
*)source_arn : trial_component_source_arn option;The Amazon Resource Name (ARN) of the source.
*)metric_name : metric_name option;The name of the metric.
*)}A summary of the metrics of a trial component.
type nonrec trial_component_metric_summaries =
trial_component_metric_summary listtype nonrec metadata_properties = {project_id : metadata_property_value option;The project ID.
*)generated_by : metadata_property_value option;The entity this entity was generated by.
*)repository : metadata_property_value option;The repository.
*)commit_id : metadata_property_value option;The commit ID.
*)}Metadata properties of the tracking entity, trial, or trial component.
type nonrec parent = {experiment_name : experiment_entity_name option;The name of the experiment.
*)trial_name : experiment_entity_name option;The name of the trial.
*)}The trial that a trial component is associated with and the experiment the trial is part of. A component might not be associated with a trial. A component can be associated with multiple trials.
type nonrec parents = parent listtype nonrec trial_component = {run_name : experiment_entity_name option;The name of the experiment run.
*)parents : parents option;An array of the parents of the component. A parent is a trial the component is associated with and the experiment the trial is part of. A component might not have any parents.
*)lineage_group_arn : lineage_group_arn option;The Amazon Resource Name (ARN) of the lineage group resource.
*)source_detail : trial_component_source_detail option;Details of the source of the component.
*)metadata_properties : metadata_properties option;metrics : trial_component_metric_summaries option;The metrics for the component.
*)output_artifacts : trial_component_artifacts option;The output artifacts of the component.
*)input_artifacts : trial_component_artifacts option;The input artifacts of the component.
*)parameters : trial_component_parameters option;The hyperparameters of the component.
*)last_modified_by : user_context option;last_modified_time : timestamp option;When the component was last modified.
*)created_by : user_context option;Who created the trial component.
*)creation_time : timestamp option;When the component was created.
*)end_time : timestamp option;When the component ended.
*)start_time : timestamp option;When the component started.
*)status : trial_component_status option;source : trial_component_source option;The Amazon Resource Name (ARN) and job type of the source of the component.
*)trial_component_arn : trial_component_arn option;The Amazon Resource Name (ARN) of the trial component.
*)display_name : experiment_entity_name option;The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
trial_component_name : experiment_entity_name option;The name of the trial component.
*)}The properties of a trial component as returned by the Search API.
type nonrec trial = {trial_component_summaries : trial_component_simple_summaries option;A list of the components associated with the trial. For each component, a summary of the component's properties is included.
*)metadata_properties : metadata_properties option;last_modified_by : user_context option;last_modified_time : timestamp option;Who last modified the trial.
*)created_by : user_context option;Who created the trial.
*)creation_time : timestamp option;When the trial was created.
*)source : trial_source option;experiment_name : experiment_entity_name option;The name of the experiment the trial is part of.
*)display_name : experiment_entity_name option;The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
trial_arn : trial_arn option;The Amazon Resource Name (ARN) of the trial.
*)trial_name : experiment_entity_name option;The name of the trial.
*)}The properties of a trial as returned by the Search API.
type nonrec transform_job_summary = {failure_reason : failure_reason option;If the transform job failed, the reason it failed.
*)transform_job_status : transform_job_status;The status of the transform job.
*)last_modified_time : timestamp option;Indicates when the transform job was last modified.
*)transform_end_time : timestamp option;Indicates when the transform job ends on compute instances. For successful jobs and stopped jobs, this is the exact time recorded after the results are uploaded. For failed jobs, this is when Amazon SageMaker detected that the job failed.
*)creation_time : timestamp;A timestamp that shows when the transform Job was created.
*)transform_job_arn : transform_job_arn;The Amazon Resource Name (ARN) of the transform job.
*)transform_job_name : transform_job_name;The name of the transform job.
*)}Provides a summary of a transform job. Multiple TransformJobSummary objects are returned as a list after in response to a ListTransformJobs call.
type nonrec transform_job_summaries = transform_job_summary listtype nonrec transform_job_step_metadata = {arn : transform_job_arn option;The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
*)}Metadata for a transform job step.
type nonrec transform_job_definition = {transform_resources : transform_resources;Identifies the ML compute instances for the transform job.
*)transform_output : transform_output;Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
*)transform_input : transform_input;A description of the input source and the way the transform job consumes it.
*)environment : transform_environment_map option;The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
*)batch_strategy : batch_strategy option;A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
max_payload_in_m_b : max_payload_in_m_b option;The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
*)max_concurrent_transforms : max_concurrent_transforms option;The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
*)}Defines the input needed to run a transform job using the inference specification specified in the algorithm.
type nonrec training_step_index = Smaws_Lib.CoreTypes.Int64.ttype nonrec integer_parameter_range_specification = {max_value : parameter_value;The maximum integer value allowed.
*)min_value : parameter_value;The minimum integer value allowed.
*)}Defines the possible values for an integer hyperparameter.
type nonrec continuous_parameter_range_specification = {max_value : parameter_value;The maximum floating-point value allowed.
*)min_value : parameter_value;The minimum floating-point value allowed.
*)}Defines the possible values for a continuous hyperparameter.
type nonrec parameter_values = parameter_value listtype nonrec categorical_parameter_range_specification = {values : parameter_values;The allowed categories for the hyperparameter.
*)}Defines the possible values for a categorical hyperparameter.
type nonrec parameter_range = {categorical_parameter_range_specification : categorical_parameter_range_specification
option;A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.
continuous_parameter_range_specification : continuous_parameter_range_specification
option;A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.
integer_parameter_range_specification : integer_parameter_range_specification
option;A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.
}Defines the possible values for categorical, continuous, and integer hyperparameters to be used by an algorithm.
type nonrec hyper_parameter_specification = {default_value : hyper_parameter_value option;The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.
*)is_required : boolean_ option;Indicates whether this hyperparameter is required.
*)is_tunable : boolean_ option;Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
*)range : parameter_range option;The allowed range for this hyperparameter.
*)type_ : parameter_type;The type of this hyperparameter. The valid types are Integer, Continuous, Categorical, and FreeText.
description : entity_description option;A brief description of the hyperparameter.
*)name : parameter_name;The name of this hyperparameter. The name must be unique.
*)}Defines a hyperparameter to be used by an algorithm.
type nonrec hyper_parameter_specifications = hyper_parameter_specification listtype nonrec training_instance_types = training_instance_type listtype nonrec compression_types = compression_type listtype nonrec input_modes = training_input_mode listtype nonrec channel_specification = {supported_input_modes : input_modes;The allowed input mode, either FILE or PIPE.
In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.
In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
*)supported_compression_types : compression_types option;The allowed compression types, if data compression is used.
*)supported_content_types : content_types;The supported MIME types for the data.
*)is_required : boolean_ option;Indicates whether the channel is required by the algorithm.
*)description : entity_description option;A brief description of the channel.
*)name : channel_name;The name of the channel.
*)}Defines a named input source, called a channel, to be used by an algorithm.
type nonrec channel_specifications = channel_specification listtype nonrec hyper_parameter_tuning_job_objective = {metric_name : metric_name;The name of the metric to use for the objective metric.
*)type_ : hyper_parameter_tuning_job_objective_type;Whether to minimize or maximize the objective metric.
*)}Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables.
type nonrec hyper_parameter_tuning_job_objectives =
hyper_parameter_tuning_job_objective listtype nonrec training_specification = {additional_s3_data_source : additional_s3_data_source option;The additional data source used during the training job.
*)supported_tuning_job_objective_metrics : hyper_parameter_tuning_job_objectives
option;A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
*)training_channels : channel_specifications;A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.
metric_definitions : metric_definition_list option;A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.
supports_distributed_training : boolean_ option;Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
*)supported_training_instance_types : training_instance_types;A list of the instance types that this algorithm can use for training.
*)supported_hyper_parameters : hyper_parameter_specifications option;A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>
training_image_digest : image_digest option;An MD5 hash of the training algorithm that identifies the Docker image used for training.
*)training_image : container_image;The Amazon ECR registry path of the Docker image that contains the training algorithm.
*)}Defines how the algorithm is used for a training job.
type nonrec total_step_count_per_epoch = Smaws_Lib.CoreTypes.Int64.ttype nonrec training_epoch_index = Smaws_Lib.CoreTypes.Int64.ttype nonrec training_epoch_count = Smaws_Lib.CoreTypes.Int64.ttype nonrec training_progress_info = {max_epoch : training_epoch_count option;The maximum number of epochs for this job.
*)current_epoch : training_epoch_index option;The current epoch number.
*)current_step : training_step_index option;The current step number.
*)total_step_count_per_epoch : total_step_count_per_epoch option;The total step count per epoch.
*)}The serverless training job progress information.
type nonrec training_plan_duration_hours = Smaws_Lib.CoreTypes.Int64.ttype nonrec training_plan_duration_minutes = Smaws_Lib.CoreTypes.Int64.ttype nonrec sage_maker_resource_names = sage_maker_resource_name listtype nonrec reserved_capacity_duration_hours = Smaws_Lib.CoreTypes.Int64.ttype nonrec reserved_capacity_duration_minutes = Smaws_Lib.CoreTypes.Int64.ttype nonrec reserved_capacity_summary = {end_time : timestamp option;The end time of the reserved capacity.
*)start_time : timestamp option;The start time of the reserved capacity.
*)duration_minutes : reserved_capacity_duration_minutes option;The additional minutes beyond whole hours in the total duration for this reserved capacity.
*)duration_hours : reserved_capacity_duration_hours option;The number of whole hours in the total duration for this reserved capacity.
*)availability_zone_id : availability_zone_id option;The Availability Zone ID of the reserved capacity.
*)availability_zone : availability_zone option;The availability zone for the reserved capacity.
*)status : reserved_capacity_status;The current status of the reserved capacity.
*)total_instance_count : total_instance_count;The total number of instances in the reserved capacity.
*)instance_type : reserved_capacity_instance_type;The instance type for the reserved capacity.
*)ultra_server_count : ultra_server_count option;The number of UltraServers included in this reserved capacity.
*)ultra_server_type : ultra_server_type option;The type of UltraServer included in this reserved capacity, such as ml.u-p6e-gb200x72.
*)reserved_capacity_type : reserved_capacity_type option;The type of reserved capacity.
*)reserved_capacity_arn : reserved_capacity_arn;The Amazon Resource Name (ARN); of the reserved capacity.
*)}Details of a reserved capacity for the training plan.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingPlan.html}CreateTrainingPlan} .
type nonrec reserved_capacity_summaries = reserved_capacity_summary listtype nonrec training_plan_summary = {reserved_capacity_summaries : reserved_capacity_summaries option;A list of reserved capacities associated with this training plan, including details such as instance types, counts, and availability zones.
*)target_resources : sage_maker_resource_names option;The target resources (e.g., training jobs, HyperPod clusters, Endpoints, Studio apps) that can use this training plan.
Training plans are specific to their target resource.
total_ultra_server_count : ultra_server_count option;The total number of UltraServers allocated to this training plan.
*)in_use_instance_count : in_use_instance_count option;The number of instances currently in use from this training plan.
*)available_instance_count : available_instance_count option;The number of instances currently available for use in this training plan.
*)total_instance_count : total_instance_count option;The total number of instances reserved in this training plan.
*)currency_code : currency_code option;The currency code for the upfront fee (e.g., USD).
*)upfront_fee : string256 option;The upfront fee for the training plan.
*)end_time : timestamp option;The end time of the training plan.
*)start_time : timestamp option;The start time of the training plan.
*)duration_minutes : training_plan_duration_minutes option;The additional minutes beyond whole hours in the total duration for this training plan.
*)duration_hours : training_plan_duration_hours option;The number of whole hours in the total duration for this training plan.
*)status_message : training_plan_status_message option;A message providing additional information about the current status of the training plan.
*)status : training_plan_status;The current status of the training plan (e.g., Pending, Active, Expired). To see the complete list of status values available for a training plan, refer to the Status attribute within the
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TrainingPlanSummary.html}TrainingPlanSummary} object.
*)training_plan_name : training_plan_name;The name of the training plan.
*)training_plan_arn : training_plan_arn;The Amazon Resource Name (ARN); of the training plan.
*)}Details of the training plan.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingPlan.html}CreateTrainingPlan} .
type nonrec training_plan_summaries = training_plan_summary listtype nonrec reserved_capacity_offering = {extension_end_time : timestamp option;The end time of the extension for the reserved capacity offering.
*)extension_start_time : timestamp option;The start time of the extension for the reserved capacity offering.
*)end_time : timestamp option;The end time of the reserved capacity offering.
*)start_time : timestamp option;The start time of the reserved capacity offering.
*)duration_minutes : reserved_capacity_duration_minutes option;The additional minutes beyond whole hours in the total duration for this reserved capacity offering.
*)duration_hours : reserved_capacity_duration_hours option;The number of whole hours in the total duration for this reserved capacity offering.
*)availability_zone : availability_zone option;The availability zone for the reserved capacity offering.
*)instance_count : reserved_capacity_instance_count;The number of instances in the reserved capacity offering.
*)instance_type : reserved_capacity_instance_type;The instance type for the reserved capacity offering.
*)ultra_server_count : ultra_server_count option;The number of UltraServers included in this reserved capacity offering.
*)ultra_server_type : ultra_server_type option;The type of UltraServer included in this reserved capacity offering, such as ml.u-p6e-gb200x72.
*)reserved_capacity_type : reserved_capacity_type option;The type of reserved capacity offering.
*)}Details about a reserved capacity offering for a training plan offering.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingPlan.html}CreateTrainingPlan} .
type nonrec reserved_capacity_offerings = reserved_capacity_offering listtype nonrec training_plan_offering = {reserved_capacity_offerings : reserved_capacity_offerings option;A list of reserved capacity offerings associated with this training plan offering.
*)currency_code : currency_code option;The currency code for the upfront fee (e.g., USD).
*)upfront_fee : string256 option;The upfront fee for this training plan offering.
*)duration_minutes : training_plan_duration_minutes option;The additional minutes beyond whole hours in the total duration for this training plan offering.
*)duration_hours : training_plan_duration_hours option;The number of whole hours in the total duration for this training plan offering.
*)requested_end_time_before : timestamp option;The requested end time that the user specified when searching for the training plan offering.
*)requested_start_time_after : timestamp option;The requested start time that the user specified when searching for the training plan offering.
*)target_resources : sage_maker_resource_names;The target resources (e.g., SageMaker Training Jobs, SageMaker HyperPod, SageMaker Endpoints, Studio apps) for this training plan offering.
Training plans are specific to their target resource.
training_plan_offering_id : training_plan_offering_id;The unique identifier for this training plan offering.
*)}Details about a training plan offering.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingPlan.html}CreateTrainingPlan} .
type nonrec training_plan_offerings = training_plan_offering listtype nonrec training_plan_filter = {value : string64;The value to filter by for the specified field.
*)name : training_plan_filter_name;The name of the filter field (e.g., Status, InstanceType).
*)}A filter to apply when listing or searching for training plans.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingPlan.html}CreateTrainingPlan} .
type nonrec training_plan_filters = training_plan_filter listtype nonrec training_plan_extension = {currency_code : currency_code option;The currency code for the upfront fee (e.g., USD).
*)upfront_fee : string256 option;The upfront fee for the extension.
*)duration_hours : training_plan_extension_duration_hours option;The duration of the extension in hours.
*)availability_zone_id : availability_zone_id option;The Availability Zone ID of the extension.
*)availability_zone : string256 option;The Availability Zone of the extension.
*)payment_status : string256 option;The payment processing status of the extension.
*)status : string256 option;The current status of the extension (e.g., Pending, Active, Scheduled, Failed, Expired).
*)end_date : timestamp option;The end date of the extension period.
*)start_date : timestamp option;The start date of the extension period.
*)extended_at : timestamp option;The timestamp when the extension was created.
*)training_plan_extension_offering_id : training_plan_extension_offering_id;The unique identifier of the extension offering that was used to create this extension.
*)}Details about an extension to a training plan, including the offering ID, dates, status, and cost information.
type nonrec training_plan_extensions = training_plan_extension listtype nonrec training_plan_extension_offering = {currency_code : currency_code option;The currency code for the upfront fee (e.g., USD).
*)upfront_fee : string256 option;The upfront fee for this extension offering.
*)duration_hours : training_plan_extension_duration_hours option;The duration of this extension offering in hours.
*)end_date : timestamp option;The end date of this extension offering.
*)start_date : timestamp option;The start date of this extension offering.
*)availability_zone : string256 option;The Availability Zone for this extension offering.
*)training_plan_extension_offering_id : training_plan_extension_offering_id;The unique identifier for this extension offering.
*)}Details about an available extension offering for a training plan. Use the offering ID with the
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ExtendTrainingPlan.html}ExtendTrainingPlan} API to extend a training plan.
type nonrec training_plan_extension_offerings =
training_plan_extension_offering listtype nonrec training_plan_duration_hours_input = Smaws_Lib.CoreTypes.Int64.ttype nonrec training_plan_arns = training_plan_arn listtype nonrec training_job_summary = {training_plan_arn : training_plan_arn option;The Amazon Resource Name (ARN); of the training plan associated with this training job.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingPlan.html}CreateTrainingPlan} .
*)warm_pool_status : warm_pool_status option;The status of the warm pool associated with the training job.
*)secondary_status : secondary_status option;The secondary status of the training job.
*)training_job_status : training_job_status;The status of the training job.
*)last_modified_time : timestamp option;Timestamp when the training job was last modified.
*)training_end_time : timestamp option;A timestamp that shows when the training job ended. This field is set only if the training job has one of the terminal statuses (Completed, Failed, or Stopped).
creation_time : timestamp;A timestamp that shows when the training job was created.
*)training_job_arn : training_job_arn;The Amazon Resource Name (ARN) of the training job.
*)training_job_name : training_job_name;The name of the training job that you want a summary for.
*)}Provides summary information about a training job.
type nonrec training_job_summaries = training_job_summary listtype nonrec training_job_step_metadata = {arn : training_job_arn option;The Amazon Resource Name (ARN) of the training job that was run by this step execution.
*)}Metadata for a training job step.
type nonrec training_job_status_counters = {stopped : training_job_status_counter option;The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
*)non_retryable_error : training_job_status_counter option;The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.
*)retryable_error : training_job_status_counter option;The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
*)in_progress : training_job_status_counter option;The number of in-progress training jobs launched by a hyperparameter tuning job.
*)completed : training_job_status_counter option;The number of completed training jobs launched by the hyperparameter tuning job.
*)}The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.
type nonrec training_job_definition = {stopping_condition : stopping_condition;Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
*)resource_config : resource_config;The resources, including the ML compute instances and ML storage volumes, to use for model training.
*)output_data_config : output_data_config;the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
*)input_data_config : input_data_config;An array of Channel objects, each of which specifies an input source.
hyper_parameters : hyper_parameters option;The hyperparameters used for the training job.
*)training_input_mode : training_input_mode;}Defines the input needed to run a training job using the algorithm.
type nonrec phase = {duration_in_seconds : traffic_duration_in_seconds option;Specifies how long a traffic phase should be. For custom load tests, the value should be between 120 and 3600. This value should not exceed JobDurationInSeconds.
spawn_rate : spawn_rate option;Specified how many new users to spawn in a minute.
*)initial_number_of_users : initial_number_of_users option;Specifies how many concurrent users to start with. The value should be between 1 and 3.
*)}Defines the traffic pattern.
type nonrec phases = phase listtype nonrec stairs = {users_per_step : users_per_step option;Specifies how many new users to spawn in each step.
*)number_of_steps : number_of_steps option;Specifies how many steps to perform during traffic.
*)duration_in_seconds : traffic_duration_in_seconds option;Defines how long each traffic step should be.
*)}Defines the stairs traffic pattern for an Inference Recommender load test. This pattern type consists of multiple steps where the number of users increases at each step.
Specify either the stairs or phases traffic pattern.
type nonrec traffic_pattern = {stairs : stairs option;Defines the stairs traffic pattern.
*)phases : phases option;Defines the phases traffic specification.
*)traffic_type : traffic_type option;Defines the traffic patterns. Choose either PHASES or STAIRS.
}Defines the traffic pattern of the load test.
type nonrec tracking_server_summary = {mlflow_version : mlflow_version option;The MLflow version used for a listed tracking server.
*)is_active : is_tracking_server_active option;The activity status of a listed tracking server.
*)tracking_server_status : tracking_server_status option;The creation status of a listed tracking server.
*)last_modified_time : timestamp option;The last modified time of a listed tracking server.
*)creation_time : timestamp option;The creation time of a listed tracking server.
*)tracking_server_name : tracking_server_name option;The name of a listed tracking server.
*)tracking_server_arn : tracking_server_arn option;The ARN of a listed tracking server.
*)}The summary of the tracking server to list.
type nonrec tracking_server_summary_list = tracking_server_summary listtype nonrec long = Smaws_Lib.CoreTypes.Int64.ttype nonrec total_hits = {relation : relation option;Indicates the relationship between the returned Value and the actual total number of matching results. Possible values are:
EqualTo: The Value is the exact count of matching results.GreaterThanOrEqualTo: The Value is a lower bound of the actual count of matching results.value : long option;The total number of matching results. This value may be exact or an estimate, depending on the Relation field.
}Represents the total number of matching results and indicates how accurate that count is.
The Value field provides the count, which may be exact or estimated. The Relation field indicates whether it's an exact figure or a lower bound. This helps understand the full scope of search results, especially when dealing with large result sets.
type nonrec filling_transformation_map =
(filling_type * filling_transformation_value) listtype nonrec filling_transformations =
(transformation_attribute_name * filling_transformation_map) listtype nonrec aggregation_transformations =
(transformation_attribute_name * aggregation_transformation_value) listtype nonrec time_series_transformations = {aggregation : aggregation_transformations option;A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method.
The supported aggregation methods are sum (default), avg, first, min, max.
Aggregation is only supported for the target column.
*)filling : filling_transformations option;A key value pair defining the filling method for a column, where the key is the column name and the value is an object which defines the filling logic. You can specify multiple filling methods for a single column.
The supported filling methods and their corresponding options are:
frontfill: none (Supported only for target column)middlefill: zero, value, median, mean, min, maxbackfill: zero, value, median, mean, min, maxfuturefill: zero, value, median, mean, min, maxTo set a filling method to a specific value, set the fill parameter to the chosen filling method value (for example "backfill" : "value"), and define the filling value in an additional parameter prefixed with "_value". For example, to set backfill to a value of 2, you must include two parameters: "backfill": "value" and "backfill_value":"2".
}Transformations allowed on the dataset. Supported transformations are Filling and Aggregation. Filling specifies how to add values to missing values in the dataset. Aggregation defines how to aggregate data that does not align with forecast frequency.
type nonrec auto_ml_job_completion_criteria = {max_auto_ml_job_runtime_in_seconds : max_auto_ml_job_runtime_in_seconds option;The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
*)max_runtime_per_training_job_in_seconds : max_runtime_per_training_job_in_seconds
option;The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
*)max_candidates : max_candidates option;The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
*)}How long a job is allowed to run, or how many candidates a job is allowed to generate.
type nonrec forecast_quantiles = forecast_quantile listtype nonrec grouping_attribute_names = grouping_attribute_name listtype nonrec time_series_config = {grouping_attribute_names : grouping_attribute_names option;A set of columns names that can be grouped with the item identifier column to create a composite key for which a target value is predicted.
*)item_identifier_attribute_name : item_identifier_attribute_name;The name of the column that represents the set of item identifiers for which you want to predict the target value.
*)timestamp_attribute_name : timestamp_attribute_name;The name of the column indicating a point in time at which the target value of a given item is recorded.
*)target_attribute_name : target_attribute_name;The name of the column representing the target variable that you want to predict for each item in your dataset. The data type of the target variable must be numerical.
*)}The collection of components that defines the time-series.
type nonrec holiday_config_attributes = {country_code : country_code option;The country code for the holiday calendar.
For the list of public holiday calendars supported by AutoML job V2, see Country Codes. Use the country code corresponding to the country of your choice.
*)}Stores the holiday featurization attributes applicable to each item of time-series datasets during the training of a forecasting model. This allows the model to identify patterns associated with specific holidays.
type nonrec holiday_config = holiday_config_attributes listtype nonrec auto_ml_algorithms = auto_ml_algorithm listtype nonrec auto_ml_algorithm_config = {auto_ml_algorithms : auto_ml_algorithms;The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
For the tabular problem type TabularJobConfig:
Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.
In ENSEMBLING mode:
In HYPERPARAMETER_TUNING mode:
For the time-series forecasting problem type TimeSeriesForecastingJobConfig:
Choose your algorithms from this list.
}The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
type nonrec auto_ml_algorithms_config = auto_ml_algorithm_config listtype nonrec candidate_generation_config = {algorithms_config : auto_ml_algorithms_config option;Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.
AlgorithmsConfig stores the customized selection of algorithms to train on your data.
For the tabular problem type TabularJobConfig, the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode.
AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO.When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.
If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.
AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.For the list of all algorithms per training mode, see AlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your algorithms from the list provided in AlgorithmConfig.
For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.
When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.
If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.}Stores the configuration information for how model candidates are generated using an AutoML job V2.
type nonrec time_series_forecasting_job_config = {candidate_generation_config : candidate_generation_config option;holiday_config : holiday_config option;The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.
*)time_series_config : time_series_config;The collection of components that defines the time-series.
*)transformations : time_series_transformations option;The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.
*)forecast_quantiles : forecast_quantiles option;The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.
forecast_horizon : forecast_horizon;The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.
*)forecast_frequency : forecast_frequency;The frequency of predictions in a forecast.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, 1D indicates every day and 15min indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min.
The valid values for each frequency are the following:
completion_criteria : auto_ml_job_completion_criteria option;feature_specification_s3_uri : s3_uri option;A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig. If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig.
You can input FeatureAttributeNames (optional) in JSON format as shown below:
{ "FeatureAttributeNames":["col1", "col2", ...] }.
You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Autopilot supports the following data types: numeric, categorical, text, and datetime.
These column keys must not include any column set in TimeSeriesConfig.
}The collection of settings used by an AutoML job V2 for the time-series forecasting problem type.
type nonrec throughput_config_description = {provisioned_write_capacity_units : capacity_unit option;For provisioned feature groups, this indicates the write throughput you are billed for and can consume without throttling.
This field is not applicable for on-demand feature groups.
*)provisioned_read_capacity_units : capacity_unit option;For provisioned feature groups with online store enabled, this indicates the read throughput you are billed for and can consume without throttling.
This field is not applicable for on-demand feature groups.
*)throughput_mode : throughput_mode;The mode used for your feature group throughput: ON_DEMAND or PROVISIONED.
}Active throughput configuration of the feature group. There are two modes: ON_DEMAND and PROVISIONED. With on-demand mode, you are charged for data reads and writes that your application performs on your feature group. You do not need to specify read and write throughput because Feature Store accommodates your workloads as they ramp up and down. You can switch a feature group to on-demand only once in a 24 hour period. With provisioned throughput mode, you specify the read and write capacity per second that you expect your application to require, and you are billed based on those limits. Exceeding provisioned throughput will result in your requests being throttled.
Note: PROVISIONED throughput mode is supported only for feature groups that are offline-only, or use the Standard tier online store.
type nonrec throughput_config = {provisioned_write_capacity_units : capacity_unit option;For provisioned feature groups, this indicates the write throughput you are billed for and can consume without throttling.
This field is not applicable for on-demand feature groups.
*)provisioned_read_capacity_units : capacity_unit option;For provisioned feature groups with online store enabled, this indicates the read throughput you are billed for and can consume without throttling.
This field is not applicable for on-demand feature groups.
*)throughput_mode : throughput_mode;The mode used for your feature group throughput: ON_DEMAND or PROVISIONED.
}Used to set feature group throughput configuration. There are two modes: ON_DEMAND and PROVISIONED. With on-demand mode, you are charged for data reads and writes that your application performs on your feature group. You do not need to specify read and write throughput because Feature Store accommodates your workloads as they ramp up and down. You can switch a feature group to on-demand only once in a 24 hour period. With provisioned throughput mode, you specify the read and write capacity per second that you expect your application to require, and you are billed based on those limits. Exceeding provisioned throughput will result in your requests being throttled.
Note: PROVISIONED throughput mode is supported only for feature groups that are offline-only, or use the Standard tier online store.
type nonrec text_generation_resolved_attributes = {base_model_name : base_model_name option;The name of the base model to fine-tune.
*)}The resolved attributes specific to the text generation problem type.
type nonrec text_generation_hyper_parameters =
(text_generation_hyper_parameter_key * text_generation_hyper_parameter_value)
listtype nonrec text_generation_job_config = {model_access_config : model_access_config option;text_generation_hyper_parameters : text_generation_hyper_parameters option;The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.
"epochCount": The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10"."batchSize": The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64"."learningRate": The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1"."learningRateWarmupSteps": The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".Here is an example where all four hyperparameters are configured.
{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }
base_model_name : base_model_name option;The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct.
completion_criteria : auto_ml_job_completion_criteria option;How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).
}The collection of settings used by an AutoML job V2 for the text generation problem type.
The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions.
type nonrec text_classification_job_config = {target_label_column : target_label_column;The name of the column used to provide the class labels. It should not be same as the content column.
*)content_column : content_column;The name of the column used to provide the sentences to be classified. It should not be the same as the target column.
*)completion_criteria : auto_ml_job_completion_criteria option;How long a job is allowed to run, or how many candidates a job is allowed to generate.
*)}The collection of settings used by an AutoML job V2 for the text classification problem type.
type nonrec cfn_stack_parameter = {value : cfn_stack_parameter_value option;The value of the CloudFormation parameter.
*)key : cfn_stack_parameter_key;The name of the CloudFormation parameter.
*)}A key-value pair representing a parameter used in the CloudFormation stack.
type nonrec cfn_stack_parameters = cfn_stack_parameter listtype nonrec cfn_stack_detail = {status_message : cfn_stack_status_message;A human-readable message about the stack's current status.
*)id : cfn_stack_id option;The unique identifier of the CloudFormation stack.
*)name : cfn_stack_name option;The name of the CloudFormation stack.
*)}Details about the CloudFormation stack.
type nonrec cfn_template_provider_detail = {stack_detail : cfn_stack_detail option;Information about the CloudFormation stack created by the template provider.
*)parameters : cfn_stack_parameters option;An array of CloudFormation stack parameters.
*)role_ar_n : role_arn option;The IAM role used by CloudFormation to create the stack.
*)template_ur_l : cfn_template_ur_l;The Amazon S3 URL of the CloudFormation template.
*)template_name : cfn_template_name;The unique identifier of the template within the project.
*)}Details about a CloudFormation template provider configuration and associated provisioning information.
type nonrec template_provider_detail = {cfn_template_provider_detail : cfn_template_provider_detail option;Details about a CloudFormation template provider configuration and associated provisioning information.
*)}Details about a template provider configuration and associated provisioning information.
type nonrec template_provider_detail_list = template_provider_detail listtype nonrec task_keywords = task_keyword listtype nonrec predefined_metric_specification = {predefined_metric_type : string_ option;The metric type. You can only apply SageMaker metric types to SageMaker endpoints.
*)}A specification for a predefined metric.
type nonrec customized_metric_specification = {statistic : statistic option;The statistic of the customized metric.
*)namespace : string_ option;The namespace of the customized metric.
*)metric_name : string_ option;The name of the customized metric.
*)}A customized metric.
type nonrec metric_specification = | Customized of customized_metric_specificationInformation about a customized metric.
*)| Predefined of predefined_metric_specificationInformation about a predefined metric.
*)An object containing information about a metric.
type nonrec target_tracking_scaling_policy_configuration = {target_value : double option;The recommended target value to specify for the metric when creating a scaling policy.
*)metric_specification : metric_specification option;An object containing information about a metric.
*)}A target tracking scaling policy. Includes support for predefined or customized metrics.
When using the PutScalingPolicy API, this parameter is required when you are creating a policy with the policy type TargetTrackingScaling.
type nonrec target_platform = {accelerator : target_platform_accelerator option;Specifies a target platform accelerator (optional).
NVIDIA: Nvidia graphics processing unit. It also requires gpu-code, trt-ver, cuda-ver compiler optionsMALI: ARM Mali graphics processorINTEL_GRAPHICS: Integrated Intel graphicsarch : target_platform_arch;Specifies a target platform architecture.
X86_64: 64-bit version of the x86 instruction set.X86: 32-bit version of the x86 instruction set.ARM64: ARMv8 64-bit CPU.ARM_EABIHF: ARMv7 32-bit, Hard Float.ARM_EABI: ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.os : target_platform_os;Specifies a target platform OS.
LINUX: Linux-based operating systems.ANDROID: Android operating systems. Android API level can be specified using the ANDROID_PLATFORM compiler option. For example, "CompilerOptions": {'ANDROID_PLATFORM': 28}}Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.
type nonrec target_device = | IMX8MPLUS| JACINTO_TDA4VM| COREML| X86_WIN64| X86_WIN32| AMBA_CV25| AMBA_CV22| AMBA_CV2| SITARA_AM57X| QCS603| QCS605| SBE_C| AISAGE| RK3288| RK3399| DEEPLENS| IMX8QM| RASP4B| RASP3B| JETSON_XAVIER| JETSON_NANO| JETSON_TX2| JETSON_TX1| ML_EIA2| ML_TRN1| ML_INF2| ML_INF1| ML_G4DN| ML_P3| ML_P2| ML_C6G| ML_C5| ML_C4| ML_M6G| ML_M5| ML_M4| LAMBDAtype nonrec tag_key_list = tag_key listtype nonrec tabular_resolved_attributes = {problem_type : problem_type option;The type of supervised learning problem available for the model candidates of the AutoML job V2 (Binary Classification, Multiclass Classification, Regression). For more information, see SageMaker Autopilot problem types.
*)}The resolved attributes specific to the tabular problem type.
type nonrec tabular_job_config = {sample_weight_attribute_name : sample_weight_attribute_name option;If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
Support for sample weights is available in Ensembling mode only.
*)target_attribute_name : target_attribute_name;The name of the target variable in supervised learning, usually represented by 'y'.
*)problem_type : problem_type option;The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.
You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.
generate_candidate_definitions_only : generate_candidate_definitions_only
option;Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
*)mode : auto_ml_mode option;The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.
The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
feature_specification_s3_uri : s3_uri option;A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:
{ "FeatureAttributeNames":["col1", "col2", ...] }.
You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
These column keys may not include the target column.
In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.
If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data.
If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.
The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.
completion_criteria : auto_ml_job_completion_criteria option;candidate_generation_config : candidate_generation_config option;The configuration information of how model candidates are generated.
*)}The collection of settings used by an AutoML job V2 for the tabular problem type.
type nonrec property_name_query = {property_name_hint : property_name_hint;Text that begins a property's name.
*)}Part of the SuggestionQuery type. Specifies a hint for retrieving property names that begin with the specified text.
type nonrec suggestion_query = {property_name_query : property_name_query option;Defines a property name hint. Only property names that begin with the specified hint are included in the response.
*)}Specified in the GetSearchSuggestions request. Limits the property names that are included in the response.
type nonrec subscribed_workteam = {listing_id : string_ option;Marketplace product listing ID.
*)marketplace_description : string200 option;The description of the vendor from the Amazon Marketplace.
*)seller_name : string_ option;The name of the vendor in the Amazon Marketplace.
*)marketplace_title : string200 option;The title of the service provided by the vendor in the Amazon Marketplace.
*)workteam_arn : workteam_arn;The Amazon Resource Name (ARN) of the vendor that you have subscribed.
*)}Describes a work team of a vendor that does the labelling job.
type nonrec subscribed_workteams = subscribed_workteam listtype nonrec studio_lifecycle_config_details = {studio_lifecycle_config_app_type : studio_lifecycle_config_app_type option;The App type to which the Lifecycle Configuration is attached.
*)last_modified_time : timestamp option;This value is equivalent to CreationTime because Amazon SageMaker AI Studio Lifecycle Configurations are immutable.
*)creation_time : timestamp option;The creation time of the Amazon SageMaker AI Studio Lifecycle Configuration.
*)studio_lifecycle_config_name : studio_lifecycle_config_name option;The name of the Amazon SageMaker AI Studio Lifecycle Configuration.
*)studio_lifecycle_config_arn : studio_lifecycle_config_arn option;The Amazon Resource Name (ARN) of the Lifecycle Configuration.
*)}Details of the Amazon SageMaker AI Studio Lifecycle Configuration.
type nonrec studio_lifecycle_configs_list =
studio_lifecycle_config_details listtype nonrec stop_transform_job_request = {transform_job_name : transform_job_name;The name of the batch transform job to stop.
*)}type nonrec stop_training_job_request = {training_job_name : training_job_name;The name of the training job to stop.
*)}type nonrec stop_processing_job_request = {processing_job_name : processing_job_name;The name of the processing job to stop.
*)}type nonrec stop_pipeline_execution_response = {pipeline_execution_arn : pipeline_execution_arn option;The Amazon Resource Name (ARN) of the pipeline execution.
*)}type nonrec stop_pipeline_execution_request = {client_request_token : idempotency_token;A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
*)pipeline_execution_arn : pipeline_execution_arn;The Amazon Resource Name (ARN) of the pipeline execution.
*)}type nonrec stop_optimization_job_request = {optimization_job_name : entity_name;The name that you assigned to the optimization job.
*)}type nonrec stop_notebook_instance_input = {notebook_instance_name : notebook_instance_name;The name of the notebook instance to terminate.
*)}type nonrec stop_monitoring_schedule_request = {monitoring_schedule_name : monitoring_schedule_name;The name of the schedule to stop.
*)}type nonrec stop_mlflow_tracking_server_response = {tracking_server_arn : tracking_server_arn option;The ARN of the stopped tracking server.
*)}type nonrec stop_mlflow_tracking_server_request = {tracking_server_name : tracking_server_name;The name of the tracking server to stop.
*)}type nonrec stop_labeling_job_request = {labeling_job_name : labeling_job_name;The name of the labeling job to stop.
*)}type nonrec stop_job_request = {job_category : job_category;The category of the job to stop.
*)job_name : job_name;The name of the job to stop.
*)}type nonrec stop_inference_recommendations_job_request = {job_name : recommendation_job_name;The name of the job you want to stop.
*)}type nonrec stop_inference_experiment_response = {inference_experiment_arn : inference_experiment_arn;The ARN of the stopped inference experiment.
*)}type nonrec model_variant_action_map =
(model_variant_name * model_variant_action) listtype nonrec stop_inference_experiment_request = {reason : inference_experiment_status_reason option;The reason for stopping the experiment.
*)desired_state : inference_experiment_stop_desired_state option;The desired state of the experiment after stopping. The possible states are the following:
Completed: The experiment completed successfullyCancelled: The experiment was canceleddesired_model_variants : model_variant_config_list option;An array of ModelVariantConfig objects. There is one for each variant that you want to deploy after the inference experiment stops. Each ModelVariantConfig describes the infrastructure configuration for deploying the corresponding variant.
model_variant_actions : model_variant_action_map;Array of key-value pairs, with names of variants mapped to actions. The possible actions are the following:
Promote - Promote the shadow variant to a production variantRemove - Delete the variantRetain - Keep the variant as it isname : inference_experiment_name;The name of the inference experiment to stop.
*)}type nonrec stop_hyper_parameter_tuning_job_request = {hyper_parameter_tuning_job_name : hyper_parameter_tuning_job_name;The name of the tuning job to stop.
*)}type nonrec stop_edge_packaging_job_request = {edge_packaging_job_name : entity_name;The name of the edge packaging job.
*)}type nonrec stop_edge_deployment_stage_request = {stage_name : entity_name;The name of the stage to stop.
*)edge_deployment_plan_name : entity_name;The name of the edge deployment plan to stop.
*)}type nonrec stop_compilation_job_request = {compilation_job_name : entity_name;The name of the model compilation job to stop.
*)}type nonrec stop_auto_ml_job_request = {auto_ml_job_name : auto_ml_job_name;The name of the object you are requesting.
*)}type nonrec stop_ai_recommendation_job_response = {ai_recommendation_job_arn : ai_recommendation_job_arn;The Amazon Resource Name (ARN) of the stopped recommendation job.
*)}type nonrec stop_ai_recommendation_job_request = {ai_recommendation_job_name : ai_entity_name;The name of the AI recommendation job to stop.
*)}type nonrec stop_ai_benchmark_job_response = {ai_benchmark_job_arn : ai_benchmark_job_arn;The Amazon Resource Name (ARN) of the stopped benchmark job.
*)}type nonrec stop_ai_benchmark_job_request = {ai_benchmark_job_name : ai_entity_name;The name of the AI benchmark job to stop.
*)}type nonrec status_details_map =
(scheduler_config_component * scheduler_resource_status) listtype nonrec start_session_response = {token_value : token_value option;An encrypted token value containing session and caller information.
*)stream_url : stream_url option;A WebSocket URL used to establish a SSH connection between the local IDE and remote SageMaker space.
*)session_id : session_id option;A unique identifier for the established remote connection session.
*)}type nonrec start_session_request = {resource_identifier : resource_identifier;The Amazon Resource Name (ARN) of the resource to which the remote connection will be established. For example, this identifies the specific ARN space application you want to connect to from your local IDE.
*)}type nonrec start_pipeline_execution_response = {pipeline_execution_arn : pipeline_execution_arn option;The Amazon Resource Name (ARN) of the pipeline execution.
*)}type nonrec parameter = {value : string1024;The literal value for the parameter.
*)name : pipeline_parameter_name;The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
*)}Assigns a value to a named Pipeline parameter.
type nonrec parameter_list = parameter listA step selected to run in selective execution mode.
type nonrec selected_step_list = selected_step listtype nonrec selective_execution_config = {selected_steps : selected_step_list;A list of pipeline steps to run. All step(s) in all path(s) between two selected steps should be included.
*)source_pipeline_execution_arn : pipeline_execution_arn option;The ARN from a reference execution of the current pipeline. Used to copy input collaterals needed for the selected steps to run. The execution status of the pipeline can be either Failed or Success.
This field is required if the steps you specify for SelectedSteps depend on output collaterals from any non-specified pipeline steps. For more information, see Selective Execution for Pipeline Steps.
}The selective execution configuration applied to the pipeline run.
type nonrec start_pipeline_execution_request = {mlflow_experiment_name : mlflow_experiment_entity_name option;The MLflow experiment name of the pipeline execution.
*)pipeline_version_id : pipeline_version_id option;The ID of the pipeline version to start execution from.
*)selective_execution_config : selective_execution_config option;The selective execution configuration applied to the pipeline run.
*)parallelism_configuration : parallelism_configuration option;This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.
*)client_request_token : idempotency_token;A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
*)pipeline_execution_description : pipeline_execution_description option;The description of the pipeline execution.
*)pipeline_parameters : parameter_list option;Contains a list of pipeline parameters. This list can be empty.
*)pipeline_execution_display_name : pipeline_execution_name option;The display name of the pipeline execution.
*)pipeline_name : pipeline_name_or_arn;The name or Amazon Resource Name (ARN) of the pipeline.
*)}type nonrec start_notebook_instance_input = {notebook_instance_name : notebook_instance_name;The name of the notebook instance to start.
*)}type nonrec start_monitoring_schedule_request = {monitoring_schedule_name : monitoring_schedule_name;The name of the schedule to start.
*)}type nonrec start_mlflow_tracking_server_response = {tracking_server_arn : tracking_server_arn option;The ARN of the started tracking server.
*)}type nonrec start_mlflow_tracking_server_request = {tracking_server_name : tracking_server_name;The name of the tracking server to start.
*)}type nonrec start_inference_experiment_response = {inference_experiment_arn : inference_experiment_arn;The ARN of the started inference experiment to start.
*)}type nonrec start_inference_experiment_request = {name : inference_experiment_name;The name of the inference experiment to start.
*)}type nonrec start_edge_deployment_stage_request = {stage_name : entity_name;The name of the stage to start.
*)edge_deployment_plan_name : entity_name;The name of the edge deployment plan to start.
*)}type nonrec start_cluster_health_check_response = {cluster_arn : cluster_arn;The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster on which the deep health checks were initiated.
*)}type nonrec instance_ids = cluster_node_id listtype nonrec deep_health_checks = deep_health_check_type listtype nonrec instance_group_health_check_configuration = {deep_health_checks : deep_health_checks;A list of deep health checks to be performed.
*)instance_ids : instance_ids option;A list of Amazon Elastic Compute Cloud (EC2) instance IDs on which to perform deep health checks.
Leave this field blank to perform deep health checks on the entire instance group.
*)instance_group_name : cluster_instance_group_name;The name of the instance group.
*)}The configuration of deep health checks for an instance group.
Overlapping deep health check configurations will be merged into a single operation.
type nonrec deep_health_check_configurations =
instance_group_health_check_configuration listtype nonrec start_cluster_health_check_request = {deep_health_check_configurations : deep_health_check_configurations;A list of configurations containing instance group names, EC2 instance IDs, and deep health checks to perform.
*)cluster_name : cluster_name_or_arn;The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.
*)}type nonrec space_sharing_settings_summary = {sharing_type : sharing_type option;Specifies the sharing type of the space.
*)}Specifies summary information about the space sharing settings.
type nonrec space_sharing_settings = {sharing_type : sharing_type;Specifies the sharing type of the space.
*)}A collection of space sharing settings.
type nonrec space_settings_summary = {space_storage_settings : space_storage_settings option;The storage settings for a space.
*)remote_access : feature_status option;A setting that enables or disables remote access for a SageMaker space. When enabled, this allows you to connect to the remote space from your local IDE.
*)app_type : app_type option;The type of app created within the space.
*)}Specifies summary information about the space settings.
type nonrec ownership_settings_summary = {owner_user_profile_name : user_profile_name option;The user profile who is the owner of the space.
*)}Specifies summary information about the ownership settings.
type nonrec space_details = {space_display_name : non_empty_string64 option;The name of the space that appears in the Studio UI.
*)ownership_settings_summary : ownership_settings_summary option;Specifies summary information about the ownership settings.
*)space_sharing_settings_summary : space_sharing_settings_summary option;Specifies summary information about the space sharing settings.
*)space_settings_summary : space_settings_summary option;Specifies summary information about the space settings.
*)last_modified_time : last_modified_time option;The last modified time.
*)creation_time : creation_time option;The creation time.
*)status : space_status option;The status.
*)space_name : space_name option;The name of the space.
*)domain_id : domain_id option;The ID of the associated domain.
*)}The space's details.
type nonrec space_list = space_details listtype nonrec source_algorithm = {algorithm_name : arn_or_name;The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
*)model_data_e_tag : string_ option;The ETag associated with Model Data URL.
*)model_data_source : model_data_source option;Specifies the location of ML model data to deploy during endpoint creation.
*)model_data_url : url option;The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
The model artifacts must be in an S3 bucket that is in the same Amazon Web Services region as the algorithm.
*)}Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
type nonrec source_algorithm_list = source_algorithm listtype nonrec source_algorithm_specification = {source_algorithms : source_algorithm_list;A list of the algorithms that were used to create a model package.
*)}A list of algorithms that were used to create a model package.
type nonrec session_chaining_config = {enable_session_tag_chaining : enable_session_tag_chaining option;Set to True to allow SageMaker to extract session tags from a training job creation role and reuse these tags when assuming the training job execution role.
}Contains information about attribute-based access control (ABAC) for a training job. The session chaining configuration uses Amazon Security Token Service (STS) for your training job to request temporary, limited-privilege credentials to tenants. For more information, see Attribute-based access control (ABAC) for multi-tenancy training.
type nonrec service_catalog_provisioning_details = {provisioning_parameters : provisioning_parameters option;A list of key value pairs that you specify when you provision a product.
*)path_id : service_catalog_entity_id option;The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path.
*)provisioning_artifact_id : service_catalog_entity_id option;The ID of the provisioning artifact.
*)product_id : service_catalog_entity_id;The ID of the product to provision.
*)}Details that you specify to provision a service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog.
type nonrec service_catalog_provisioned_product_details = {provisioned_product_status_message : provisioned_product_status_message option;The current status of the product.
AVAILABLE - Stable state, ready to perform any operation. The most recent operation succeeded and completed.UNDER_CHANGE - Transitive state. Operations performed might not have valid results. Wait for an AVAILABLE status before performing operations.TAINTED - Stable state, ready to perform any operation. The stack has completed the requested operation but is not exactly what was requested. For example, a request to update to a new version failed and the stack rolled back to the current version.ERROR - An unexpected error occurred. The provisioned product exists but the stack is not running. For example, CloudFormation received a parameter value that was not valid and could not launch the stack.PLAN_IN_PROGRESS - Transitive state. The plan operations were performed to provision a new product, but resources have not yet been created. After reviewing the list of resources to be created, execute the plan. Wait for an AVAILABLE status before performing operations.provisioned_product_id : service_catalog_entity_id option;The ID of the provisioned product.
*)}Details of a provisioned service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog.
type nonrec serverless_job_config = {evaluator_arn : evaluator_arn option;The evaluator Amazon Resource Name (ARN) used as reward function or reward prompt.
*)evaluation_type : evaluation_type option;The evaluation job type. Required when serverless job type is Evaluation.
peft : peft option;The parameter-efficient fine-tuning configuration.
*)customization_technique : customization_technique option;The model customization technique.
*)job_type : serverless_job_type;The serverless training job type.
*)accept_eula : accept_eula option;Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. For more information, see End-user license agreements section for more details on accepting the EULA.
base_model_arn : serverless_job_base_model_arn;The base model Amazon Resource Name (ARN) in SageMaker Public Hub. SageMaker always selects the latest version of the provided model.
*)}The configuration for the serverless training job.
type nonrec send_pipeline_execution_step_success_response = {pipeline_execution_arn : pipeline_execution_arn option;The Amazon Resource Name (ARN) of the pipeline execution.
*)}type nonrec output_parameter = {value : string1024;The value of the output parameter.
*)name : string256;The name of the output parameter.
*)}An output parameter of a pipeline step.
type nonrec output_parameter_list = output_parameter listtype nonrec send_pipeline_execution_step_success_request = {client_request_token : idempotency_token option;A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
*)output_parameters : output_parameter_list option;A list of the output parameters of the callback step.
*)callback_token : callback_token;The pipeline generated token from the Amazon SQS queue.
*)}type nonrec send_pipeline_execution_step_failure_response = {pipeline_execution_arn : pipeline_execution_arn option;The Amazon Resource Name (ARN) of the pipeline execution.
*)}type nonrec send_pipeline_execution_step_failure_request = {client_request_token : idempotency_token option;A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
*)failure_reason : string256 option;A message describing why the step failed.
*)callback_token : callback_token;The pipeline generated token from the Amazon SQS queue.
*)}type nonrec selective_execution_result = {source_pipeline_execution_arn : pipeline_execution_arn option;The ARN from an execution of the current pipeline.
*)}The ARN from an execution of the current pipeline.
type nonrec search_training_plan_offerings_response = {training_plan_extension_offerings : training_plan_extension_offerings option;A list of extension offerings available for the specified training plan. These offerings can be used with the
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ExtendTrainingPlan.html}ExtendTrainingPlan} API to extend an existing training plan.
*)training_plan_offerings : training_plan_offerings;A list of training plan offerings that match the search criteria.
*)}type nonrec search_training_plan_offerings_request = {training_plan_arn : string_ option;The Amazon Resource Name (ARN); of an existing training plan to search for extension offerings. When specified, the API returns extension offerings that can be used to extend the specified training plan.
*)target_resources : sage_maker_resource_names option;The target resources (e.g., SageMaker Training Jobs, SageMaker HyperPod, SageMaker Endpoints, Studio apps) to search for in the offerings.
Training plans are specific to their target resource.
duration_hours : training_plan_duration_hours_input option;The desired duration in hours for the training plan offerings.
*)end_time_before : timestamp option;A filter to search for reserved capacity offerings with an end time before a specified date.
*)start_time_after : timestamp option;A filter to search for training plan offerings with a start time after a specified date.
*)ultra_server_count : ultra_server_count option;The number of UltraServers to search for.
*)ultra_server_type : ultra_server_type option;The type of UltraServer to search for, such as ml.u-p6e-gb200x72.
*)instance_count : reserved_capacity_instance_count option;The number of instances you want to reserve in the training plan offerings. This allows you to specify the quantity of compute resources needed for your SageMaker training jobs or SageMaker HyperPod clusters, helping you find reserved capacity offerings that match your requirements.
*)instance_type : reserved_capacity_instance_type option;The type of instance you want to search for in the available training plan offerings. This field allows you to filter the search results based on the specific compute resources you require for your SageMaker training jobs or SageMaker HyperPod clusters. When searching for training plan offerings, specifying the instance type helps you find Reserved Instances that match your computational needs.
*)}type nonrec experiment_source = {source_type : source_type option;The source type.
*)source_arn : experiment_source_arn;The Amazon Resource Name (ARN) of the source.
*)}The source of the experiment.
type nonrec experiment = {last_modified_by : user_context option;last_modified_time : timestamp option;When the experiment was last modified.
*)created_by : user_context option;Who created the experiment.
*)creation_time : timestamp option;When the experiment was created.
*)description : experiment_description option;The description of the experiment.
*)source : experiment_source option;display_name : experiment_entity_name option;The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
experiment_arn : experiment_arn option;The Amazon Resource Name (ARN) of the experiment.
*)experiment_name : experiment_entity_name option;The name of the experiment.
*)}The properties of an experiment as returned by the Search API. For information about experiments, see the CreateExperiment API.
type nonrec deployed_image = {resolution_time : timestamp option;The date and time when the image path for the model resolved to the ResolvedImage
resolved_image : container_image option;The specific digest path of the image hosted in this ProductionVariant.
specified_image : container_image option;The image path you specified when you created the model.
*)}Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant.
If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant, the path resolves to a path of the form registry/repository[@digest]. A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide.
type nonrec deployed_images = deployed_image listtype nonrec instance_pool_summary = {current_instance_count : task_count;The current number of instances of this type in the instance pool.
*)instance_type : production_variant_instance_type;The ML compute instance type for the instance pool.
*)}A summary of an instance pool for a production variant, including the instance type and the current number of instances.
type nonrec instance_pool_summary_list = instance_pool_summary listtype nonrec production_variant_status = {start_time : timestamp option;The start time of the current status change.
*)status_message : variant_status_message option;A message that describes the status of the production variant.
*)status : variant_status;The endpoint variant status which describes the current deployment stage status or operational status.
Creating: Creating inference resources for the production variant.Deleting: Terminating inference resources for the production variant.Updating: Updating capacity for the production variant.ActivatingTraffic: Turning on traffic for the production variant.Baking: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.}Describes the status of the production variant.
type nonrec production_variant_status_list = production_variant_status listtype nonrec production_variant_serverless_config = {provisioned_concurrency : serverless_provisioned_concurrency option;The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency.
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
*)max_concurrency : serverless_max_concurrency;The maximum number of concurrent invocations your serverless endpoint can process.
*)memory_size_in_m_b : serverless_memory_size_in_m_b;The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
*)}Specifies the serverless configuration for an endpoint variant.
type nonrec production_variant_managed_instance_scaling_scale_in_policy = {cooldown_in_minutes : managed_instance_scaling_cooldown_in_minutes option;The cooldown period, in minutes, after the last endpoint operation before the endpoint evaluates consolidation scale-in opportunities.
Default value: 20.
maximum_step_size : managed_instance_scaling_maximum_step_size option;The maximum number of instances that the endpoint can terminate at a time during a consolidation scale-in operation.
Default value: 1.
strategy : managed_instance_scaling_scale_in_strategy;The strategy for scaling in instances.
IDLE_RELEASE Releases instances that have no hosted inference component copies.
CONSOLIDATION Consolidates inference component copies onto fewer instances to release more instances. Consolidation honors the scheduling configuration of each inference component. For example, if an inference component specifies Availability Zone balance, consolidation only proceeds when the resulting distribution does not increase the imbalance.
*)}Configures the scale-in behavior for managed instance scaling.
type nonrec production_variant_managed_instance_scaling = {scale_in_policy : production_variant_managed_instance_scaling_scale_in_policy
option;Configures the scale-in behavior for managed instance scaling.
*)max_instance_count : managed_instance_scaling_max_instance_count option;The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
*)min_instance_count : managed_instance_scaling_min_instance_count option;The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
*)status : managed_instance_scaling_status option;Indicates whether managed instance scaling is enabled.
*)}Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
type nonrec production_variant_routing_config = {routing_strategy : routing_strategy;Sets how the endpoint routes incoming traffic:
LEAST_OUTSTANDING_REQUESTS: The endpoint routes requests to the specific instances that have more capacity to process them.RANDOM: The endpoint routes each request to a randomly chosen instance.}Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
type nonrec ec2_capacity_reservation = {used_by_current_endpoint : task_count option;The number of instances from the EC2 capacity reservation that are being used by the endpoint.
*)available_instance_count : task_count option;The number of instances that are currently available in the EC2 capacity reservation.
*)total_instance_count : task_count option;The number of instances that you allocated to the EC2 capacity reservation.
*)ec2_capacity_reservation_id : ec2_capacity_reservation_id option;The unique identifier for an EC2 capacity reservation that's part of the ML capacity reservation.
*)}The EC2 capacity reservations that are shared to an ML capacity reservation.
type nonrec ec2_capacity_reservations_list = ec2_capacity_reservation listtype nonrec production_variant_capacity_reservation_summary = {ec2_capacity_reservations : ec2_capacity_reservations_list option;The EC2 capacity reservations that are shared to this ML capacity reservation, if any.
*)used_by_current_endpoint : task_count option;The number of instances from the ML capacity reservation that are being used by the endpoint.
*)available_instance_count : task_count option;The number of instances that are currently available in the ML capacity reservation.
*)total_instance_count : task_count option;The number of instances that you allocated to the ML capacity reservation.
*)capacity_reservation_preference : capacity_reservation_preference option;The option that you chose for the capacity reservation. SageMaker AI supports the following options:
capacity-reservations-only SageMaker AI launches instances only into an ML capacity reservation. If no capacity is available, the instances fail to launch.
*)ml_reservation_arn : ml_reservation_arn option;The Amazon Resource Name (ARN) that uniquely identifies the ML capacity reservation that SageMaker AI applies when it deploys the endpoint.
*)}Details about an ML capacity reservation.
type nonrec production_variant_summary = {capacity_reservation_config : production_variant_capacity_reservation_summary
option;Settings for the capacity reservation for the compute instances that SageMaker AI reserves for an endpoint.
*)routing_config : production_variant_routing_config option;Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
*)managed_instance_scaling : production_variant_managed_instance_scaling option;Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
*)desired_serverless_config : production_variant_serverless_config option;The serverless configuration requested for the endpoint update.
*)current_serverless_config : production_variant_serverless_config option;The serverless configuration for the endpoint.
*)variant_status : production_variant_status_list option;The endpoint variant status which describes the current deployment stage status or operational status.
*)instance_pools : instance_pool_summary_list option;A list of instance pools for the production variant. Each pool indicates the instance type and the current number of instances of that type.
*)desired_instance_count : task_count option;The number of instances requested in the UpdateEndpointWeightsAndCapacities request.
current_instance_count : task_count option;The number of instances associated with the variant.
*)desired_weight : variant_weight option;The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.
current_weight : variant_weight option;The weight associated with the variant.
*)deployed_images : deployed_images option;An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant.
variant_name : variant_name;The name of the variant.
*)}Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating, you get different desired and current values.
type nonrec production_variant_summary_list = production_variant_summary listtype nonrec data_capture_config_summary = {kms_key_id : kms_key_id;The KMS key being used to encrypt the data in Amazon S3.
*)destination_s3_uri : destination_s3_uri;The Amazon S3 location being used to capture the data.
*)current_sampling_percentage : sampling_percentage;The percentage of requests being captured by your Endpoint.
*)capture_status : capture_status;Whether data capture is currently functional.
*)enable_capture : enable_capture;Whether data capture is enabled or disabled.
*)}The currently active data capture configuration used by your Endpoint.
type nonrec monitoring_execution_summary = {monitoring_type : monitoring_type option;The type of the monitoring job.
*)monitoring_job_definition_name : monitoring_job_definition_name option;The name of the monitoring job.
*)failure_reason : failure_reason option;Contains the reason a monitoring job failed, if it failed.
*)endpoint_name : endpoint_name option;The name of the endpoint used to run the monitoring job.
*)processing_job_arn : processing_job_arn option;The Amazon Resource Name (ARN) of the monitoring job.
*)monitoring_execution_status : execution_status;The status of the monitoring job.
*)last_modified_time : timestamp;A timestamp that indicates the last time the monitoring job was modified.
*)creation_time : timestamp;The time at which the monitoring job was created.
*)scheduled_time : timestamp;The time the monitoring job was scheduled.
*)monitoring_schedule_name : monitoring_schedule_name;The name of the monitoring schedule.
*)}Summary of information about the last monitoring job to run.
type nonrec monitoring_schedule = {last_monitoring_execution_summary : monitoring_execution_summary option;endpoint_name : endpoint_name option;The endpoint that hosts the model being monitored.
*)monitoring_schedule_config : monitoring_schedule_config option;last_modified_time : timestamp option;The last time the monitoring schedule was changed.
*)creation_time : timestamp option;The time that the monitoring schedule was created.
*)failure_reason : failure_reason option;If the monitoring schedule failed, the reason it failed.
*)monitoring_type : monitoring_type option;The type of the monitoring job definition to schedule.
*)monitoring_schedule_status : schedule_status option;The status of the monitoring schedule. This can be one of the following values.
PENDING - The schedule is pending being created.FAILED - The schedule failed.SCHEDULED - The schedule was successfully created.STOPPED - The schedule was stopped.monitoring_schedule_name : monitoring_schedule_name option;The name of the monitoring schedule.
*)monitoring_schedule_arn : monitoring_schedule_arn option;The Amazon Resource Name (ARN) of the monitoring schedule.
*)}A schedule for a model monitoring job. For information about model monitor, see Amazon SageMaker Model Monitor.
type nonrec monitoring_schedule_list = monitoring_schedule listtype nonrec endpoint = {shadow_production_variants : production_variant_summary_list option;A list of the shadow variants hosted on the endpoint. Each shadow variant is a model in shadow mode with production traffic replicated from the production variant.
*)monitoring_schedules : monitoring_schedule_list option;A list of monitoring schedules for the endpoint. For information about model monitoring, see Amazon SageMaker Model Monitor.
*)last_modified_time : timestamp;The last time the endpoint was modified.
*)creation_time : timestamp;The time that the endpoint was created.
*)failure_reason : failure_reason option;If the endpoint failed, the reason it failed.
*)endpoint_status : endpoint_status;The status of the endpoint.
*)data_capture_config : data_capture_config_summary option;production_variants : production_variant_summary_list option;A list of the production variants hosted on the endpoint. Each production variant is a model.
*)endpoint_config_name : endpoint_config_name;The endpoint configuration associated with the endpoint.
*)endpoint_arn : endpoint_arn;The Amazon Resource Name (ARN) of the endpoint.
*)endpoint_name : endpoint_name;The name of the endpoint.
*)}A hosted endpoint for real-time inference.
type nonrec model_package_validation_profile = {transform_job_definition : transform_job_definition;The TransformJobDefinition object that describes the transform job used for the validation of the model package.
profile_name : entity_name;The name of the profile for the model package.
*)}Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
type nonrec model_package_validation_profiles =
model_package_validation_profile listtype nonrec model_package_validation_specification = {validation_profiles : model_package_validation_profiles;An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that SageMaker runs to validate your model package.
validation_role : role_arn;The IAM roles to be used for the validation of the model package.
*)}Specifies batch transform jobs that SageMaker runs to validate your model package.
type nonrec model_package_status_item = {failure_reason : string_ option;if the overall status is Failed, the reason for the failure.
status : detailed_model_package_status;The current status.
*)name : entity_name;The name of the model package for which the overall status is being reported.
*)}Represents the overall status of a model package.
type nonrec model_package_status_item_list = model_package_status_item listtype nonrec model_package_status_details = {image_scan_statuses : model_package_status_item_list option;The status of the scan of the Docker image container for the model package.
*)validation_statuses : model_package_status_item_list;The validation status of the model package.
*)}Specifies the validation and image scan statuses of the model package.
type nonrec metrics_source = {s3_uri : s3_uri;The S3 URI for the metrics source.
*)content_digest : content_digest option;The hash key used for the metrics source.
*)content_type : content_type;The metric source content type.
*)}Details about the metrics source.
type nonrec model_quality = {constraints : metrics_source option;Model quality constraints.
*)statistics : metrics_source option;Model quality statistics.
*)}Model quality statistics and constraints.
type nonrec model_data_quality = {constraints : metrics_source option;Data quality constraints for a model.
*)statistics : metrics_source option;Data quality statistics for a model.
*)}Data quality constraints and statistics for a model.
type nonrec bias = {post_training_report : metrics_source option;The post-training bias report for a model.
*)pre_training_report : metrics_source option;The pre-training bias report for a model.
*)report : metrics_source option;The bias report for a model
*)}Contains bias metrics for a model.
type nonrec explainability = {report : metrics_source option;The explainability report for a model.
*)}Contains explainability metrics for a model.
type nonrec model_metrics = {explainability : explainability option;Metrics that help explain a model.
*)bias : bias option;Metrics that measure bias in a model.
*)model_data_quality : model_data_quality option;Metrics that measure the quality of the input data for a model.
*)model_quality : model_quality option;Metrics that measure the quality of a model.
*)}Contains metrics captured from a model.
type nonrec model_package_security_config = {kms_key_id : kms_key_id;The KMS Key ID (KMSKeyId) used for encryption of model package information.
}An optional Key Management Service key to encrypt, decrypt, and re-encrypt model package information for regulated workloads with highly sensitive data.
type nonrec file_source = {s3_uri : s3_uri;The Amazon S3 URI for the file source.
*)content_digest : content_digest option;The digest of the file source.
*)content_type : content_type option;The type of content stored in the file source.
*)}Contains details regarding the file source.
type nonrec drift_check_bias = {post_training_constraints : metrics_source option;The post-training constraints.
*)pre_training_constraints : metrics_source option;The pre-training constraints.
*)config_file : file_source option;The bias config file for a model.
*)}Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
type nonrec drift_check_explainability = {config_file : file_source option;The explainability config file for the model.
*)constraints : metrics_source option;The drift check explainability constraints.
*)}Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
type nonrec drift_check_model_quality = {constraints : metrics_source option;The drift check model quality constraints.
*)statistics : metrics_source option;The drift check model quality statistics.
*)}Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
type nonrec drift_check_model_data_quality = {constraints : metrics_source option;The drift check model data quality constraints.
*)statistics : metrics_source option;The drift check model data quality statistics.
*)}Represents the drift check data quality baselines that can be used when the model monitor is set using the model package.
type nonrec drift_check_baselines = {model_data_quality : drift_check_model_data_quality option;Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.
*)model_quality : drift_check_model_quality option;Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
*)explainability : drift_check_explainability option;Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
*)bias : drift_check_bias option;Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
*)}Represents the drift check baselines that can be used when the model monitor is set using the model package.
type nonrec model_package = {skip_model_validation : skip_model_validation option;Indicates if you want to skip model validation.
*)drift_check_baselines : drift_check_baselines option;Represents the drift check baselines that can be used when the model monitor is set using the model package.
*)customer_metadata_properties : customer_metadata_map option;The metadata properties for the model package.
*)model_life_cycle : model_life_cycle option;A structure describing the current state of the model in its life cycle.
*)model_card : model_package_model_card option;security_config : model_package_security_config option;source_uri : model_package_source_uri option;The URI of the source for the model package.
*)additional_inference_specifications : additional_inference_specifications
option;An array of additional Inference Specification objects.
*)sample_payload_url : string_ option;The Amazon Simple Storage Service path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
*)task : string_ option;The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification.
*)domain : string_ option;The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
*)approval_description : approval_description option;A description provided when the model approval is set.
*)last_modified_by : user_context option;Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.
*)last_modified_time : timestamp option;The last time the model package was modified.
*)model_metrics : model_metrics option;Metrics for the model.
*)metadata_properties : metadata_properties option;Metadata properties of the tracking entity, trial, or trial component.
*)created_by : user_context option;Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.
*)model_approval_status : model_approval_status option;The approval status of the model. This can be one of the following values.
APPROVED - The model is approvedREJECTED - The model is rejected.PENDING_MANUAL_APPROVAL - The model is waiting for manual approval.certify_for_marketplace : certify_for_marketplace option;Whether the model package is to be certified to be listed on Amazon Web Services Marketplace. For information about listing model packages on Amazon Web Services Marketplace, see List Your Algorithm or Model Package on Amazon Web Services Marketplace.
*)model_package_status_details : model_package_status_details option;Specifies the validation and image scan statuses of the model package.
*)model_package_status : model_package_status option;The status of the model package. This can be one of the following values.
PENDING - The model package is pending being created.IN_PROGRESS - The model package is in the process of being created.COMPLETED - The model package was successfully created.FAILED - The model package failed.DELETING - The model package is in the process of being deleted.validation_specification : model_package_validation_specification option;Specifies batch transform jobs that SageMaker runs to validate your model package.
*)source_algorithm_specification : source_algorithm_specification option;A list of algorithms that were used to create a model package.
*)inference_specification : inference_specification option;Defines how to perform inference generation after a training job is run.
*)creation_time : creation_time option;The time that the model package was created.
*)model_package_description : entity_description option;The description of the model package.
*)model_package_arn : model_package_arn option;The Amazon Resource Name (ARN) of the model package.
*)model_package_registration_type : model_package_registration_type option;The package registration type of the model package.
*)model_package_version : model_package_version option;The version number of a versioned model.
*)model_package_group_name : entity_name option;The model group to which the model belongs.
*)model_package_name : entity_name option;The name of the model package. The name can be as follows:
ModelPackageGroupName/ModelPackageVersion'.}A container for your trained model that can be deployed for SageMaker inference. This can include inference code, artifacts, and metadata. The model package type can be one of the following.
For more information, see CreateModelPackage.
type nonrec model_package_group = {model_package_group_status : model_package_group_status option;The status of the model group. This can be one of the following values.
PENDING - The model group is pending being created.IN_PROGRESS - The model group is in the process of being created.COMPLETED - The model group was successfully created.FAILED - The model group failed.DELETING - The model group is in the process of being deleted.DELETE_FAILED - SageMaker failed to delete the model group.created_by : user_context option;creation_time : creation_time option;The time that the model group was created.
*)model_package_group_description : entity_description option;The description for the model group.
*)model_package_group_arn : model_package_group_arn option;The Amazon Resource Name (ARN) of the model group.
*)model_package_group_name : entity_name option;The name of the model group.
*)}A group of versioned models in the Model Registry.
type nonrec pipeline = {parallelism_configuration : parallelism_configuration option;The parallelism configuration applied to the pipeline.
*)last_modified_by : user_context option;created_by : user_context option;last_run_time : timestamp option;The time when the pipeline was last run.
*)last_modified_time : timestamp option;The time that the pipeline was last modified.
*)creation_time : timestamp option;The creation time of the pipeline.
*)pipeline_status : pipeline_status option;The status of the pipeline.
*)role_arn : role_arn option;The Amazon Resource Name (ARN) of the role that created the pipeline.
*)pipeline_description : pipeline_description option;The description of the pipeline.
*)pipeline_display_name : pipeline_name option;The display name of the pipeline.
*)pipeline_name : pipeline_name option;The name of the pipeline.
*)pipeline_arn : pipeline_arn option;The Amazon Resource Name (ARN) of the pipeline.
*)}A SageMaker Model Building Pipeline instance.
type nonrec pipeline_experiment_config = {trial_name : experiment_entity_name option;The name of the trial.
*)experiment_name : experiment_entity_name option;The name of the experiment.
*)}Specifies the names of the experiment and trial created by a pipeline.
type nonrec pipeline_execution = {pipeline_version_display_name : pipeline_version_name option;The display name of the pipeline version that started this execution.
*)pipeline_version_id : pipeline_version_id option;The ID of the pipeline version that started this execution.
*)pipeline_parameters : parameter_list option;Contains a list of pipeline parameters. This list can be empty.
*)selective_execution_config : selective_execution_config option;The selective execution configuration applied to the pipeline run.
*)parallelism_configuration : parallelism_configuration option;The parallelism configuration applied to the pipeline execution.
*)last_modified_by : user_context option;created_by : user_context option;last_modified_time : timestamp option;The time that the pipeline execution was last modified.
*)creation_time : timestamp option;The creation time of the pipeline execution.
*)failure_reason : pipeline_execution_failure_reason option;If the execution failed, a message describing why.
*)pipeline_experiment_config : pipeline_experiment_config option;pipeline_execution_description : pipeline_execution_description option;The description of the pipeline execution.
*)pipeline_execution_status : pipeline_execution_status option;The status of the pipeline status.
*)pipeline_execution_display_name : pipeline_execution_name option;The display name of the pipeline execution.
*)pipeline_execution_arn : pipeline_execution_arn option;The Amazon Resource Name (ARN) of the pipeline execution.
*)pipeline_arn : pipeline_arn option;The Amazon Resource Name (ARN) of the pipeline that was executed.
*)}An execution of a pipeline.
type nonrec pipeline_version = {last_executed_pipeline_execution_status : pipeline_execution_status option;The status of the most recent pipeline execution created from this pipeline version.
*)last_executed_pipeline_execution_display_name : pipeline_execution_name option;The display name of the most recent pipeline execution created from this pipeline version.
*)last_executed_pipeline_execution_arn : pipeline_execution_arn option;The Amazon Resource Name (ARN) of the most recent pipeline execution created from this pipeline version.
*)last_modified_by : user_context option;created_by : user_context option;last_modified_time : timestamp option;The time when the pipeline version was last modified.
*)creation_time : timestamp option;The creation time of the pipeline version.
*)pipeline_version_description : pipeline_version_description option;The description of the pipeline version.
*)pipeline_version_display_name : pipeline_version_name option;The display name of the pipeline version.
*)pipeline_version_id : pipeline_version_id option;The ID of the pipeline version.
*)pipeline_arn : pipeline_arn option;The Amazon Resource Name (ARN) of the pipeline.
*)}The version of the pipeline.
type nonrec feature_definitions = feature_definition listtype nonrec online_store_security_config = {kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service (KMS) key ARN that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId:
"kms:Encrypt""kms:Decrypt""kms:DescribeKey""kms:CreateGrant""kms:RetireGrant""kms:ReEncryptFrom""kms:ReEncryptTo""kms:GenerateDataKey""kms:ListAliases""kms:ListGrants""kms:RevokeGrant"The caller (either user or IAM role) to all DataPlane operations (PutRecord, GetRecord, DeleteRecord) must have the following permissions to the KmsKeyId:
"kms:Decrypt"}The security configuration for OnlineStore.
type nonrec online_store_config = {storage_type : storage_type option;Option for different tiers of low latency storage for real-time data retrieval.
Standard: A managed low latency data store for feature groups.InMemory: A managed data store for feature groups that supports very low latency retrieval.ttl_duration : ttl_duration option;Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration. For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.
enable_online_store : boolean_ option;Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.
The default value is False.
security_config : online_store_security_config option;Use to specify KMS Key ID (KMSKeyId) for at-rest encryption of your OnlineStore.
}Use this to specify the Amazon Web Services Key Management Service (KMS) Key ID, or KMSKeyId, for at rest data encryption. You can turn OnlineStore on or off by specifying the EnableOnlineStore flag at General Assembly.
The default value is False.
type nonrec s3_storage_config = {resolved_output_s3_uri : s3_uri option;The S3 path where offline records are written.
*)kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service (KMS) key ARN of the key used to encrypt any objects written into the OfflineStore S3 location.
The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId:
"kms:GenerateDataKey"s3_uri : s3_uri;The S3 URI, or location in Amazon S3, of OfflineStore.
S3 URIs have a format similar to the following: s3://example-bucket/prefix/.
}The Amazon Simple Storage (Amazon S3) location and security configuration for OfflineStore.
type nonrec data_catalog_config = {database : database;The name of the Glue table database.
*)catalog : catalog;The name of the Glue table catalog.
*)table_name : table_name;The name of the Glue table.
*)}The meta data of the Glue table which serves as data catalog for the OfflineStore.
type nonrec offline_store_config = {table_format : table_format option;data_catalog_config : data_catalog_config option;The meta data of the Glue table for the OfflineStore. If not provided, Feature Store auto-generates the table name, database, and catalog when the OfflineStore is created. You can optionally provide this configuration to specify custom values. This applies to both Glue and Apache Iceberg table formats.
disable_glue_table_creation : boolean_ option;Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring an OfflineStore. If set to True and DataCatalogConfig is provided, Feature Store associates the provided catalog configuration with the feature group without creating a table. In this case, you are responsible for creating and managing the Glue table. If set to True without DataCatalogConfig, no Glue table is created or associated with the feature group. The Iceberg table format is only supported when this is set to False.
If set to False and DataCatalogConfig is provided, Feature Store creates the table using the specified names. If set to False without DataCatalogConfig, Feature Store auto-generates the table name following Athena's naming recommendations. This applies to both Glue and Apache Iceberg table formats.
The default value is False.
s3_storage_config : s3_storage_config;The Amazon Simple Storage (Amazon S3) location of OfflineStore.
}The configuration of an OfflineStore.
Provide an OfflineStoreConfig in a request to CreateFeatureGroup to create an OfflineStore.
To encrypt an OfflineStore using at rest data encryption, specify Amazon Web Services Key Management Service (KMS) key ID, or KMSKeyId, in S3StorageConfig.
type nonrec offline_store_status = {blocked_reason : blocked_reason option;The justification for why the OfflineStoreStatus is Blocked (if applicable).
*)status : offline_store_status_value;An OfflineStore status.
}The status of OfflineStore.
type nonrec last_update_status = {failure_reason : failure_reason option;If the update wasn't successful, indicates the reason why it failed.
*)status : last_update_status_value;A value that indicates whether the update was made successful.
*)}A value that indicates whether the update was successful.
type nonrec feature_group = {description : description option;A free form description of a FeatureGroup.
failure_reason : failure_reason option;The reason that the FeatureGroup failed to be replicated in the OfflineStore. This is failure may be due to a failure to create a FeatureGroup in or delete a FeatureGroup from the OfflineStore.
last_update_status : last_update_status option;A value that indicates whether the feature group was updated successfully.
*)offline_store_status : offline_store_status option;feature_group_status : feature_group_status option;A FeatureGroup status.
role_arn : role_arn option;The Amazon Resource Name (ARN) of the IAM execution role used to create the feature group.
*)offline_store_config : offline_store_config option;online_store_config : online_store_config option;last_modified_time : last_modified_time option;A timestamp indicating the last time you updated the feature group.
*)creation_time : creation_time option;The time a FeatureGroup was created.
feature_definitions : feature_definitions option;A list of Features. Each Feature must include a FeatureName and a FeatureType.
Valid FeatureTypes are Integral, Fractional and String.
FeatureNames cannot be any of the following: is_deleted, write_time, api_invocation_time.
You can create up to 2,500 FeatureDefinitions per FeatureGroup.
event_time_feature_name : feature_name option;The name of the feature that stores the EventTime of a Record in a FeatureGroup.
A EventTime is point in time when a new event occurs that corresponds to the creation or update of a Record in FeatureGroup. All Records in the FeatureGroup must have a corresponding EventTime.
record_identifier_feature_name : feature_name option;The name of the Feature whose value uniquely identifies a Record defined in the FeatureGroup FeatureDefinitions.
feature_group_name : feature_group_name option;The name of the FeatureGroup.
feature_group_arn : feature_group_arn option;The Amazon Resource Name (ARN) of a FeatureGroup.
}Amazon SageMaker Feature Store stores features in a collection called Feature Group. A Feature Group can be visualized as a table which has rows, with a unique identifier for each row where each column in the table is a feature. In principle, a Feature Group is composed of features and values per features.
type nonrec feature_parameters = feature_parameter listtype nonrec feature_metadata = {parameters : feature_parameters option;Optional key-value pairs that you specify to better describe the feature.
*)description : feature_description option;An optional description that you specify to better describe the feature.
*)last_modified_time : last_modified_time option;A timestamp indicating when the feature was last modified.
*)creation_time : creation_time option;A timestamp indicating when the feature was created.
*)feature_type : feature_type option;The data type of the feature.
*)feature_name : feature_name option;The name of feature.
*)feature_group_name : feature_group_name option;The name of the feature group containing the feature.
*)feature_group_arn : feature_group_arn option;The Amazon Resource Number (ARN) of the feature group.
*)}The metadata for a feature. It can either be metadata that you specify, or metadata that is updated automatically.
type nonrec project = {last_modified_by : user_context option;last_modified_time : timestamp option;A timestamp container for when the project was last modified.
*)template_provider_details : template_provider_detail_list option;An array of template providers associated with the project.
*)creation_time : timestamp option;A timestamp specifying when the project was created.
*)created_by : user_context option;Who created the project.
*)project_status : project_status option;The status of the project.
*)service_catalog_provisioned_product_details : service_catalog_provisioned_product_details
option;service_catalog_provisioning_details : service_catalog_provisioning_details
option;project_description : entity_description option;The description of the project.
*)project_id : project_id option;The ID of the project.
*)project_name : project_entity_name option;The name of the project.
*)project_arn : project_arn option;The Amazon Resource Name (ARN) of the project.
*)}The properties of a project as returned by the Search API.
The strategy hyperparameter tuning uses to find the best combination of hyperparameters for your model.
type nonrec hyperband_strategy_config = {max_resource : hyperband_strategy_max_resource option;The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the MaxResource value, it is stopped. If a value for MaxResource is not provided, and Hyperband is selected as the hyperparameter tuning strategy, HyperbandTraining attempts to infer MaxResource from the following keys (if present) in StaticsHyperParameters:
epochsnumepochsn-epochsn_epochsnum_epochsIf HyperbandStrategyConfig is unable to infer a value for MaxResource, it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions. For distributed training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.
min_resource : hyperband_strategy_min_resource option;The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for MinResource has not been reached, the training job is not stopped by Hyperband.
}The configuration for Hyperband, a multi-fidelity based hyperparameter tuning strategy. Hyperband uses the final and intermediate results of a training job to dynamically allocate resources to utilized hyperparameter configurations while automatically stopping under-performing configurations. This parameter should be provided only if Hyperband is selected as the StrategyConfig under the HyperParameterTuningJobConfig API.
type nonrec hyper_parameter_tuning_job_strategy_config = {hyperband_strategy_config : hyperband_strategy_config option;The configuration for the object that specifies the Hyperband strategy. This parameter is only supported for the Hyperband selection for Strategy within the HyperParameterTuningJobConfig API.
}The configuration for a training job launched by a hyperparameter tuning job. Choose Bayesian for Bayesian optimization, and Random for random search optimization. For more advanced use cases, use Hyperband, which evaluates objective metrics for training jobs after every epoch. For more information about strategies, see How Hyperparameter Tuning Works.
type nonrec resource_limits = {max_runtime_in_seconds : hyper_parameter_tuning_max_runtime_in_seconds option;The maximum time in seconds that a hyperparameter tuning job can run.
*)max_parallel_training_jobs : max_parallel_training_jobs;The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
*)max_number_of_training_jobs : max_number_of_training_jobs option;The maximum number of training jobs that a hyperparameter tuning job can launch.
*)}Specifies the maximum number of training jobs and parallel training jobs that a hyperparameter tuning job can launch.
type nonrec integer_parameter_range = {scaling_type : hyper_parameter_scaling_type option;The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
Auto SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
*)max_value : parameter_value;The maximum value of the hyperparameter to search.
*)min_value : parameter_value;The minimum value of the hyperparameter to search.
*)name : parameter_key;The name of the hyperparameter to search.
*)}For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
type nonrec integer_parameter_ranges = integer_parameter_range listtype nonrec continuous_parameter_range = {scaling_type : hyper_parameter_scaling_type option;The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
Auto SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
*)max_value : parameter_value;The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
min_value : parameter_value;The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValuefor tuning.
name : parameter_key;The name of the continuous hyperparameter to tune.
*)}A list of continuous hyperparameters to tune.
type nonrec continuous_parameter_ranges = continuous_parameter_range listtype nonrec categorical_parameter_range = {values : parameter_values;A list of the categories for the hyperparameter.
*)name : parameter_key;The name of the categorical hyperparameter to tune.
*)}A list of categorical hyperparameters to tune.
type nonrec categorical_parameter_ranges = categorical_parameter_range listtype nonrec auto_parameter = {value_hint : parameter_value;An example value of the hyperparameter to optimize using Autotune.
*)name : parameter_key;The name of the hyperparameter to optimize using Autotune.
*)}The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
type nonrec auto_parameters = auto_parameter listtype nonrec parameter_ranges = {auto_parameters : auto_parameters option;A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
*)categorical_parameter_ranges : categorical_parameter_ranges option;The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
*)continuous_parameter_ranges : continuous_parameter_ranges option;The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
*)integer_parameter_ranges : integer_parameter_ranges option;The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
*)}Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.
type nonrec hyper_parameter_tuning_job_config = {random_seed : random_seed option;A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.
*)tuning_job_completion_criteria : tuning_job_completion_criteria option;The tuning job's completion criteria.
*)training_job_early_stopping_type : training_job_early_stopping_type option;Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband. This parameter can take on one of the following values (the default value is OFF):
OFF Training jobs launched by the hyperparameter tuning job do not use early stopping.
AUTO SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.
*)parameter_ranges : parameter_ranges option;The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.
*)resource_limits : resource_limits;The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
*)hyper_parameter_tuning_job_objective : hyper_parameter_tuning_job_objective
option;The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
*)strategy_config : hyper_parameter_tuning_job_strategy_config option;The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig.
strategy : hyper_parameter_tuning_job_strategy_type;Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works.
*)}Configures a hyperparameter tuning job.
type nonrec hyper_parameter_algorithm_specification = {metric_definitions : metric_definition_list option;algorithm_name : arn_or_name option;The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage.
training_input_mode : training_input_mode;training_image : algorithm_image option;The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
}Specifies which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.
type nonrec hyper_parameter_tuning_instance_config = {volume_size_in_g_b : volume_size_in_g_b;The volume size in GB of the data to be processed for hyperparameter optimization (optional).
*)instance_count : training_instance_count;The number of instances of the type specified by InstanceType. Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
instance_type : training_instance_type;The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.
*)}The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).
type nonrec hyper_parameter_tuning_instance_configs =
hyper_parameter_tuning_instance_config listtype nonrec hyper_parameter_tuning_resource_config = {instance_configs : hyper_parameter_tuning_instance_configs option;A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.
If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs. Instead, use InstanceType, VolumeSizeInGB and InstanceCount. If you use InstanceConfigs, do not provide values for InstanceType, VolumeSizeInGB or InstanceCount.
allocation_strategy : hyper_parameter_tuning_allocation_strategy option;The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
volume_kms_key_id : kms_key_id option;A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId. For a list of instance types that use local storage, see instance store volumes. For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
volume_size_in_g_b : optional_volume_size_in_g_b option;The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.
Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes.
SageMaker supports only the General Purpose SSD (gp2) storage volume type.
*)instance_count : training_instance_count option;The number of compute instances of type InstanceType to use. For distributed training, select a value greater than 1.
instance_type : training_instance_type option;The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
*)}The configuration of resources, including compute instances and storage volumes for use in training jobs launched by hyperparameter tuning jobs. HyperParameterTuningResourceConfig is similar to ResourceConfig, but has the additional InstanceConfigs and AllocationStrategy fields to allow for flexible instance management. Specify one or more instance types, count, and the allocation strategy for instance selection.
HyperParameterTuningResourceConfig supports the capabilities of ResourceConfig with the exception of KeepAlivePeriodInSeconds. Hyperparameter tuning jobs use warm pools by default, which reuse clusters between training jobs.
type nonrec hyper_parameter_training_job_environment_map =
(hyper_parameter_training_job_environment_key
* hyper_parameter_training_job_environment_value)
listtype nonrec hyper_parameter_training_job_definition = {environment : hyper_parameter_training_job_environment_map option;An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
retry_strategy : retry_strategy option;The number of times to retry the job when the job fails due to an InternalServerError.
checkpoint_config : checkpoint_config option;enable_managed_spot_training : boolean_ option;A Boolean indicating whether managed spot training is enabled (True) or not (False).
enable_inter_container_traffic_encryption : boolean_ option;To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
enable_network_isolation : boolean_ option;Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
*)stopping_condition : stopping_condition;Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
*)hyper_parameter_tuning_resource_config : hyper_parameter_tuning_resource_config
option;The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).
resource_config : resource_config option;The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.
output_data_config : output_data_config;Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
*)vpc_config : vpc_config option;The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
*)input_data_config : input_data_config option;An array of Channel objects that specify the input for the training jobs that the tuning job launches.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
*)algorithm_specification : hyper_parameter_algorithm_specification;The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
*)static_hyper_parameters : hyper_parameters option;Specifies the values of hyperparameters that do not change for the tuning job.
*)hyper_parameter_ranges : parameter_ranges option;tuning_objective : hyper_parameter_tuning_job_objective option;definition_name : hyper_parameter_training_job_definition_name option;The job definition name.
*)}Defines the training jobs launched by a hyperparameter tuning job.
type nonrec hyper_parameter_training_job_definitions =
hyper_parameter_training_job_definition listtype nonrec objective_status_counters = {failed : objective_status_counter option;The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
*)pending : objective_status_counter option;The number of training jobs that are in progress and pending evaluation of their final objective metric.
*)succeeded : objective_status_counter option;The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
*)}Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.
type nonrec final_hyper_parameter_tuning_job_objective_metric = {value : metric_value;The value of the objective metric.
*)metric_name : metric_name;The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables.
*)type_ : hyper_parameter_tuning_job_objective_type option;Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
*)}Shows the latest objective metric emitted by a training job that was launched by a hyperparameter tuning job. You define the objective metric in the HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig.
type nonrec hyper_parameter_training_job_summary = {objective_status : objective_status option;The status of the objective metric for the training job:
final_hyper_parameter_tuning_job_objective_metric : final_hyper_parameter_tuning_job_objective_metric
option;The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
*)failure_reason : failure_reason option;The reason that the training job failed.
*)tuned_hyper_parameters : hyper_parameters;A list of the hyperparameters for which you specified ranges to search.
*)training_job_status : training_job_status;The status of the training job.
*)training_end_time : timestamp option;Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
training_start_time : timestamp option;The date and time that the training job started.
*)creation_time : timestamp;The date and time that the training job was created.
*)tuning_job_name : hyper_parameter_tuning_job_name option;The HyperParameter tuning job that launched the training job.
*)training_job_arn : training_job_arn;The Amazon Resource Name (ARN) of the training job.
*)training_job_name : training_job_name;The name of the training job.
*)training_job_definition_name : hyper_parameter_training_job_definition_name
option;The training job definition name.
*)}The container for the summary information about a training job.
type nonrec parent_hyper_parameter_tuning_job = {hyper_parameter_tuning_job_name : hyper_parameter_tuning_job_name option;The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
*)}A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
type nonrec parent_hyper_parameter_tuning_jobs =
parent_hyper_parameter_tuning_job listtype nonrec hyper_parameter_tuning_job_warm_start_config = {warm_start_type : hyper_parameter_tuning_job_warm_start_type;Specifies one of the following:
IDENTICAL_DATA_AND_ALGORITHM The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
TRANSFER_LEARNING The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
*)parent_hyper_parameter_tuning_jobs : parent_hyper_parameter_tuning_jobs;An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point.
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
*)}Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric, and the training job that performs the best is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.
All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.
type nonrec hyper_parameter_tuning_job_completion_details = {convergence_detected_time : timestamp option;The time in timestamp format that AMT detected model convergence, as defined by a lack of significant improvement over time based on criteria developed over a wide range of diverse benchmarking tests.
*)number_of_training_jobs_objective_not_improving : integer option;The number of training jobs launched by a tuning job that are not improving (1% or less) as measured by model performance evaluated against an objective function.
*)}A structure that contains runtime information about both current and completed hyperparameter tuning jobs.
type nonrec hyper_parameter_tuning_job_consumed_resources = {runtime_in_seconds : integer option;The wall clock runtime in seconds used by your hyperparameter tuning job.
*)}The total resources consumed by your hyperparameter tuning job.
type nonrec hyper_parameter_tuning_job_search_entity = {consumed_resources : hyper_parameter_tuning_job_consumed_resources option;The total amount of resources consumed by a hyperparameter tuning job.
*)tuning_job_completion_details : hyper_parameter_tuning_job_completion_details
option;Information about either a current or completed hyperparameter tuning job.
*)failure_reason : failure_reason option;The error that was created when a hyperparameter tuning job failed.
*)warm_start_config : hyper_parameter_tuning_job_warm_start_config option;overall_best_training_job : hyper_parameter_training_job_summary option;best_training_job : hyper_parameter_training_job_summary option;objective_status_counters : objective_status_counters option;training_job_status_counters : training_job_status_counters option;last_modified_time : timestamp option;The time that a hyperparameter tuning job was last modified.
*)hyper_parameter_tuning_end_time : timestamp option;The time that a hyperparameter tuning job ended.
*)creation_time : timestamp option;The time that a hyperparameter tuning job was created.
*)hyper_parameter_tuning_job_status : hyper_parameter_tuning_job_status option;The status of a hyperparameter tuning job.
*)training_job_definitions : hyper_parameter_training_job_definitions option;The job definitions included in a hyperparameter tuning job.
*)training_job_definition : hyper_parameter_training_job_definition option;hyper_parameter_tuning_job_config : hyper_parameter_tuning_job_config option;hyper_parameter_tuning_job_arn : hyper_parameter_tuning_job_arn option;The Amazon Resource Name (ARN) of a hyperparameter tuning job.
*)hyper_parameter_tuning_job_name : hyper_parameter_tuning_job_name option;The name of a hyperparameter tuning job.
*)}An entity returned by the SearchRecord API containing the properties of a hyperparameter tuning job.
Configure the security settings to protect model card data.
type nonrec model_card = {model_package_group_name : string_ option;The model package group that contains the model package. Only relevant for model cards created for model packages in the Amazon SageMaker Model Registry.
*)risk_rating : string_ option;The risk rating of the model. Different organizations might have different criteria for model card risk ratings. For more information, see Risk ratings.
*)model_id : string_ option;The unique name (ID) of the model.
*)last_modified_by : user_context option;last_modified_time : timestamp option;The date and time that the model card was last modified.
*)created_by : user_context option;creation_time : timestamp option;The date and time that the model card was created.
*)security_config : model_card_security_config option;The security configuration used to protect model card data.
*)model_card_status : model_card_status option;The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
Draft: The model card is a work in progress.PendingReview: The model card is pending review.Approved: The model card is approved.Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.content : model_card_content option;The content of the model card. Content uses the model card JSON schema and provided as a string.
*)model_card_version : integer option;The version of the model card.
*)model_card_name : entity_name option;The unique name of the model card.
*)model_card_arn : model_card_arn option;The Amazon Resource Name (ARN) of the model card.
*)}An Amazon SageMaker Model Card.
type nonrec repository_auth_config = {repository_credentials_provider_arn : repository_credentials_provider_arn;The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide.
*)}Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field of the ImageConfig object that you passed to a call to CreateModel and the private Docker registry where the model image is hosted requires authentication.
type nonrec image_config = {repository_auth_config : repository_auth_config option;(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
repository_access_mode : repository_access_mode;Set this to one of the following values:
Platform - The model image is hosted in Amazon ECR.Vpc - The model image is hosted in a private Docker registry in your VPC.}Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).
type nonrec multi_model_config = {model_cache_setting : model_cache_setting option;Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled.
}Specifies additional configuration for hosting multi-model endpoints.
type nonrec container_definition = {container_metrics_config : container_metrics_config option;The configuration for container metrics scraping. Specifies the metrics endpoint path and publishing frequency. If not specified when EnableDetailedObservability is True, the default path /metrics on port 8080 is used. For first-party and Deep Learning Containers (DLC), the endpoint path is determined automatically and this configuration is optional.
multi_model_config : multi_model_config option;Specifies additional configuration for multi-model endpoints.
*)inference_specification_name : inference_specification_name option;The inference specification name in the model package version.
*)model_package_name : versioned_arn_or_name option;The name or Amazon Resource Name (ARN) of the model package to use to create the model.
*)environment : environment_map option;The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables.
The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.
additional_model_data_sources : additional_model_data_sources option;Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.
model_data_source : model_data_source option;Specifies the location of ML model data to deploy.
Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
model_data_url : url option;The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.
mode : container_mode option;Whether the container hosts a single model or multiple models.
*)image_config : image_config option;Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
*)image : container_image option;The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
*)container_hostname : container_hostname option;This parameter is ignored for models that contain only a PrimaryContainer.
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
}Describes the container, as part of model definition.
type nonrec container_definition_list = container_definition listtype nonrec inference_execution_config = {mode : inference_execution_mode;How containers in a multi-container are run. The following values are valid.
SERIAL - Containers run as a serial pipeline.DIRECT - Only the individual container that you specify is run.}Specifies details about how containers in a multi-container endpoint are run.
type nonrec real_time_inference_recommendation = {environment : environment_map option;The recommended environment variables to set in the model container for Real-Time Inference.
*)instance_type : production_variant_instance_type;The recommended instance type for Real-Time Inference.
*)recommendation_id : string_;The recommendation ID which uniquely identifies each recommendation.
*)}The recommended configuration to use for Real-Time Inference.
type nonrec real_time_inference_recommendations =
real_time_inference_recommendation listtype nonrec deployment_recommendation = {real_time_inference_recommendations : real_time_inference_recommendations
option;recommendation_status : recommendation_status;Status of the deployment recommendation. The status NOT_APPLICABLE means that SageMaker is unable to provide a default recommendation for the model using the information provided. If the deployment status is IN_PROGRESS, retry your API call after a few seconds to get a COMPLETED deployment recommendation.
}A set of recommended deployment configurations for the model. To get more advanced recommendations, see CreateInferenceRecommendationsJob to create an inference recommendation job.
type nonrec model = {deployment_recommendation : deployment_recommendation option;A set of recommended deployment configurations for the model.
*)enable_network_isolation : boolean_ option;Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
*)model_arn : model_arn option;The Amazon Resource Name (ARN) of the model.
*)creation_time : timestamp option;A timestamp that indicates when the model was created.
*)vpc_config : vpc_config option;execution_role_arn : role_arn option;The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
*)inference_execution_config : inference_execution_config option;containers : container_definition_list option;The containers in the inference pipeline.
*)primary_container : container_definition option;model_name : model_name option;The name of the model.
*)}The properties of a model as returned by the Search API.
type nonrec model_dashboard_endpoint = {endpoint_status : endpoint_status;The endpoint status.
*)last_modified_time : timestamp;The last time the endpoint was modified.
*)creation_time : timestamp;A timestamp that indicates when the endpoint was created.
*)endpoint_arn : endpoint_arn;The Amazon Resource Name (ARN) of the endpoint.
*)endpoint_name : endpoint_name;The endpoint name.
*)}An endpoint that hosts a model displayed in the Amazon SageMaker Model Dashboard.
type nonrec model_dashboard_endpoints = model_dashboard_endpoint listtype nonrec model_dashboard_indicator_action = {enabled : boolean_ option;Indicates whether the alert action is turned on.
*)}An alert action taken to light up an icon on the Amazon SageMaker Model Dashboard when an alert goes into InAlert status.
type nonrec monitoring_alert_actions = {model_dashboard_indicator : model_dashboard_indicator_action option;An alert action taken to light up an icon on the Model Dashboard when an alert goes into InAlert status.
}A list of alert actions taken in response to an alert going into InAlert status.
type nonrec monitoring_alert_summary = {actions : monitoring_alert_actions;A list of alert actions taken in response to an alert going into InAlert status.
evaluation_period : monitoring_evaluation_period;The number of most recent monitoring executions to consider when evaluating alert status.
*)datapoints_to_alert : monitoring_datapoints_to_alert;Within EvaluationPeriod, how many execution failures will raise an alert.
alert_status : monitoring_alert_status;The current status of an alert.
*)last_modified_time : timestamp;A timestamp that indicates when a monitor alert was last updated.
*)creation_time : timestamp;A timestamp that indicates when a monitor alert was created.
*)monitoring_alert_name : monitoring_alert_name;The name of a monitoring alert.
*)}Provides summary information about a monitor alert.
type nonrec monitoring_alert_summary_list = monitoring_alert_summary listtype nonrec model_dashboard_monitoring_schedule = {batch_transform_input : batch_transform_input option;last_monitoring_execution_summary : monitoring_execution_summary option;monitoring_alert_summaries : monitoring_alert_summary_list option;A JSON array where each element is a summary for a monitoring alert.
*)endpoint_name : endpoint_name option;The endpoint which is monitored.
*)monitoring_schedule_config : monitoring_schedule_config option;last_modified_time : timestamp option;A timestamp that indicates when the monitoring schedule was last updated.
*)creation_time : timestamp option;A timestamp that indicates when the monitoring schedule was created.
*)failure_reason : failure_reason option;If a monitoring job failed, provides the reason.
*)monitoring_type : monitoring_type option;The monitor type of a model monitor.
*)monitoring_schedule_status : schedule_status option;The status of the monitoring schedule.
*)monitoring_schedule_name : monitoring_schedule_name option;The name of a monitoring schedule.
*)monitoring_schedule_arn : monitoring_schedule_arn option;The Amazon Resource Name (ARN) of a monitoring schedule.
*)}A monitoring schedule for a model displayed in the Amazon SageMaker Model Dashboard.
type nonrec model_dashboard_monitoring_schedules =
model_dashboard_monitoring_schedule listtype nonrec model_dashboard_model_card = {risk_rating : string_ option;A model card's risk rating. Can be low, medium, or high.
*)model_id : string_ option;For models created in SageMaker, this is the model ARN. For models created outside of SageMaker, this is a user-customized string.
*)last_modified_by : user_context option;last_modified_time : timestamp option;A timestamp that indicates when the model card was last updated.
*)created_by : user_context option;creation_time : timestamp option;A timestamp that indicates when the model card was created.
*)security_config : model_card_security_config option;The KMS Key ID (KMSKeyId) for encryption of model card information.
model_card_status : model_card_status option;The model card status.
*)model_card_version : integer option;The model card version.
*)model_card_name : entity_name option;The name of a model card.
*)model_card_arn : model_card_arn option;The Amazon Resource Name (ARN) for a model card.
*)}The model card for a model displayed in the Amazon SageMaker Model Dashboard.
type nonrec model_dashboard_model = {model_card : model_dashboard_model_card option;The model card for a model.
*)monitoring_schedules : model_dashboard_monitoring_schedules option;The monitoring schedules for a model.
*)last_batch_transform_job : transform_job option;endpoints : model_dashboard_endpoints option;The endpoints that host a model.
*)model : model option;A model displayed in the Model Dashboard.
*)}A model displayed in the Amazon SageMaker Model Dashboard.
type nonrec job_secondary_status_transition = {status_message : string_ option;A detailed message about the status transition.
*)end_time : timestamp option;The date and time that the status transition ended.
*)start_time : timestamp;The date and time that the status transition started.
*)status : job_secondary_status;The secondary status of the job at this transition point.
*)}Represents a secondary status transition for a job. Jobs progress through multiple secondary statuses during execution. Each transition records the status, start time, optional end time, and an optional message with additional details.
type nonrec job_secondary_status_transitions =
job_secondary_status_transition listtype nonrec job = {failure_reason : failure_reason option;If the job failed, the reason it failed.
*)secondary_status_transitions : job_secondary_status_transitions option;A list of secondary status transitions for the job, with timestamps and optional status messages.
*)secondary_status : job_secondary_status option;The detailed secondary status of the job, providing more granular information about the job's progress.
*)job_status : job_status option;The current status of the job.
*)end_time : timestamp option;The date and time that the job ended.
*)last_modified_time : timestamp option;The date and time that the job was last modified.
*)creation_time : timestamp option;The date and time that the job was created.
*)job_config_document : job_config_document option;The JSON configuration document for the job.
*)job_config_schema_version : job_schema_version option;The schema version used for the job configuration document.
*)job_category : job_category option;The category of the job.
*)role_arn : role_arn option;The ARN of the IAM role associated with the job.
*)job_arn : job_arn option;The Amazon Resource Name (ARN) of the job.
*)job_name : job_name option;The name of the job.
*)}The properties of a job returned by the Search API.
type nonrec search_record = {job : job option;The properties of a job.
*)model : model_dashboard_model option;model_card : model_card option;An Amazon SageMaker Model Card that documents details about a machine learning model.
*)hyper_parameter_tuning_job : hyper_parameter_tuning_job_search_entity option;The properties of a hyperparameter tuning job.
*)project : project option;The properties of a project.
*)feature_metadata : feature_metadata option;The feature metadata used to search through the features.
*)feature_group : feature_group option;pipeline_version : pipeline_version option;The version of the pipeline.
*)pipeline_execution : pipeline_execution option;pipeline : pipeline option;model_package_group : model_package_group option;model_package : model_package option;endpoint : endpoint option;trial_component : trial_component option;The properties of a trial component.
*)trial : trial option;The properties of a trial.
*)experiment : experiment option;The properties of an experiment.
*)training_job : training_job option;The properties of a training job.
*)}A single resource returned as part of the Search API response.
type nonrec search_results_list = search_record listtype nonrec search_response = {total_hits : total_hits option;The total number of matching results.
*)next_token : next_token option;If the result of the previous Search request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request.
results : search_results_list option;A list of SearchRecord objects.
}type nonrec filter = {value : filter_value option;A value used with Name and Operator to determine which resources satisfy the filter's condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS.
operator : operator option;A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
Equals The value of Name equals Value.
NotEquals The value of Name doesn't equal Value.
Exists The Name property exists.
NotExists The Name property does not exist.
GreaterThan The value of Name is greater than Value. Not supported for text properties.
GreaterThanOrEqualTo The value of Name is greater than or equal to Value. Not supported for text properties.
LessThan The value of Name is less than Value. Not supported for text properties.
LessThanOrEqualTo The value of Name is less than or equal to Value. Not supported for text properties.
In The value of Name is one of the comma delimited strings in Value. Only supported for text properties.
Contains The value of Name contains the string Value. Only supported for text properties.
A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:
Experiment.DisplayNameExperiment.ExperimentNameExperiment.TagsTrial.DisplayNameTrial.TrialNameTrial.TagsTrialComponent.DisplayNameTrialComponent.TrialComponentNameTrialComponent.TagsTrialComponent.InputArtifactsTrialComponent.OutputArtifactsA SearchExpression can include only one Contains operator for all other values of Name. In these cases, if you include multiple Contains operators in the SearchExpression, the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded."
name : resource_property_name;A resource property name. For example, TrainingJobName. For valid property names, see SearchRecord. You must specify a valid property for the resource.
}A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.
If you specify a Value, but not an Operator, SageMaker uses the equals operator.
In search, there are several property types:
Metrics To define a metric filter, enter a value using the form "Metrics.<name>", where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9":
{
"Name": "Metrics.accuracy",
"Operator": "GreaterThan",
"Value": "0.9"
}
HyperParameters To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>". Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5":
{
"Name": "HyperParameters.learning_rate",
"Operator": "LessThan",
"Value": "0.5"
}
Tags To define a tag filter, enter a value with the form Tags.<key>.
type nonrec filter_list = filter listtype nonrec nested_filters = {filters : filter_list;A list of filters. Each filter acts on a property. Filters must contain at least one Filters value. For example, a NestedFilters call might include a filter on the PropertyName parameter of the InputDataConfig property: InputDataConfig.DataSource.S3DataSource.S3Uri.
nested_property_name : resource_property_name;The name of the property to use in the nested filters. The value must match a listed property name, such as InputDataConfig.
}A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API.
For example, to filter on a training job's InputDataConfig property with a specific channel name and S3Uri prefix, define the following filters:
'{Name:"InputDataConfig.ChannelName", "Operator":"Equals", "Value":"train"}','{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"Contains", "Value":"mybucket/catdata"}'type nonrec nested_filters_list = nested_filters listtype search_expression = {operator : boolean_operator option;A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify And. If only a single conditional statement needs to be true for the entire search expression to be true, specify Or. The default value is And.
sub_expressions : search_expression_list option;A list of search expression objects.
*)nested_filters : nested_filters_list option;A list of nested filter objects.
*)filters : filter_list option;A list of filter objects.
*)}A multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A SearchExpression can contain up to twenty elements.
A SearchExpression contains the following components:
Filter objects. Each filter defines a simple Boolean expression comprised of a resource property name, Boolean operator, and value.NestedFilter objects. Each nested filter defines a list of Boolean expressions using a list of resource properties. A nested filter is satisfied if a single object in the list satisfies all Boolean expressions.SearchExpression objects. A search expression object can be nested in a list of search expression objects.And or Or.and search_expression_list = search_expression listtype nonrec search_request = {visibility_conditions : visibility_conditions_list option;Limits the results of your search request to the resources that you can access.
*)cross_account_filter_option : cross_account_filter_option option;A cross account filter option. When the value is "CrossAccount" the search results will only include resources made discoverable to you from other accounts. When the value is "SameAccount" or null the search results will only include resources from your account. Default is null. For more information on searching for resources made discoverable to your account, see Search discoverable resources in the SageMaker Developer Guide. The maximum number of ResourceCatalogs viewable is 1000.
max_results : max_results option;The maximum number of results to return.
*)next_token : next_token option;If more than MaxResults resources match the specified SearchExpression, the response includes a NextToken. The NextToken can be passed to the next SearchRequest to continue retrieving results.
sort_order : search_sort_order option;How SearchResults are ordered. Valid values are Ascending or Descending. The default is Descending.
sort_by : resource_property_name option;The name of the resource property used to sort the SearchResults. The default is LastModifiedTime.
search_expression : search_expression option;A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive SubExpressions, NestedFilters, and Filters that can be included in a SearchExpression object is 50.
resource : resource_type;The name of the SageMaker resource to search for.
*)}type nonrec scaling_policy_objective = {max_invocations_per_minute : integer option;The maximum number of expected requests to your endpoint per minute.
*)min_invocations_per_minute : integer option;The minimum number of expected requests to your endpoint per minute.
*)}An object where you specify the anticipated traffic pattern for an endpoint.
type nonrec scaling_policy_metric = {model_latency : integer option;The interval of time taken by a model to respond as viewed from SageMaker. This interval includes the local communication times taken to send the request and to fetch the response from the container of a model and the time taken to complete the inference in the container.
*)invocations_per_instance : integer option;The number of invocations sent to a model, normalized by InstanceCount in each ProductionVariant. 1/numberOfInstances is sent as the value on each request, where numberOfInstances is the number of active instances for the ProductionVariant behind the endpoint at the time of the request.
}The metric for a scaling policy.
type nonrec scaling_policy = | TargetTracking of target_tracking_scaling_policy_configurationA target tracking scaling policy. Includes support for predefined or customized metrics.
*)An object containing a recommended scaling policy.
type nonrec scaling_policies = scaling_policy listtype nonrec retry_pipeline_execution_response = {pipeline_execution_arn : pipeline_execution_arn option;The Amazon Resource Name (ARN) of the pipeline execution.
*)}type nonrec retry_pipeline_execution_request = {parallelism_configuration : parallelism_configuration option;This configuration, if specified, overrides the parallelism configuration of the parent pipeline.
*)client_request_token : idempotency_token;A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
*)pipeline_execution_arn : pipeline_execution_arn;The Amazon Resource Name (ARN) of the pipeline execution.
*)}type nonrec rendering_error = {message : string_;A human-readable message describing the error.
*)code : string_;A unique identifier for a specific class of errors.
*)}A description of an error that occurred while rendering the template.
type nonrec rendering_error_list = rendering_error listtype nonrec render_ui_template_response = {errors : rendering_error_list;A list of one or more RenderingError objects if any were encountered while rendering the template. If there were no errors, the list is empty.
rendered_content : string_;A Liquid template that renders the HTML for the worker UI.
*)}type nonrec renderable_task = {input : task_input;A JSON object that contains values for the variables defined in the template. It is made available to the template under the substitution variable task.input. For example, if you define a variable task.input.text in your template, you can supply the variable in the JSON object as "text": "sample text".
}Contains input values for a task.
type nonrec render_ui_template_request = {human_task_ui_arn : human_task_ui_arn option;The HumanTaskUiArn of the worker UI that you want to render. Do not provide a HumanTaskUiArn if you use the UiTemplate parameter.
See a list of available Human Ui Amazon Resource Names (ARNs) in UiConfig.
*)role_arn : role_arn;The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the template.
*)task : renderable_task;A RenderableTask object containing a representative task to render.
ui_template : ui_template option;A Template object containing the worker UI template to render.
}type nonrec register_devices_request = {devices : devices;A list of devices to register with SageMaker Edge Manager.
*)device_fleet_name : entity_name;The name of the fleet.
*)}type nonrec edge = {association_type : association_edge_type option;The type of the Association(Edge) between the source and destination. For example ContributedTo, Produced, or DerivedFrom.
destination_arn : association_entity_arn option;The Amazon Resource Name (ARN) of the destination lineage entity of the directed edge.
*)source_arn : association_entity_arn option;The Amazon Resource Name (ARN) of the source lineage entity of the directed edge.
*)}A directed edge connecting two lineage entities.
type nonrec edges = edge listtype nonrec query_lineage_response = {next_token : string8192 option;Limits the number of vertices in the response. Use the NextToken in a response to to retrieve the next page of results.
edges : edges option;A list of edges that connect vertices in the response.
*)vertices : vertices option;A list of vertices connected to the start entity(ies) in the lineage graph.
*)}type nonrec query_lineage_start_arns = association_entity_arn listtype nonrec query_types = string40 listtype nonrec query_lineage_types = lineage_type listtype nonrec query_filters = {properties : query_properties option;Filter the lineage entities connected to the StartArn(s) by a set if property key value pairs. If multiple pairs are provided, an entity is included in the results if it matches any of the provided pairs.
modified_after : timestamp option;Filter the lineage entities connected to the StartArn(s) after the last modified date.
modified_before : timestamp option;Filter the lineage entities connected to the StartArn(s) before the last modified date.
created_after : timestamp option;Filter the lineage entities connected to the StartArn(s) after the create date.
created_before : timestamp option;Filter the lineage entities connected to the StartArn(s) by created date.
lineage_types : query_lineage_types option;Filter the lineage entities connected to the StartArn(s) by the type of the lineage entity.
types : query_types option;Filter the lineage entities connected to the StartArn by type. For example: DataSet, Model, Endpoint, or ModelDeployment.
}A set of filters to narrow the set of lineage entities connected to the StartArn(s) returned by the QueryLineage API action.
type nonrec query_lineage_request = {next_token : string8192 option;Limits the number of vertices in the request. Use the NextToken in a response to to retrieve the next page of results.
max_results : query_lineage_max_results option;Limits the number of vertices in the results. Use the NextToken in a response to to retrieve the next page of results.
max_depth : query_lineage_max_depth option;The maximum depth in lineage relationships from the StartArns that are traversed. Depth is a measure of the number of Associations from the StartArn entity to the matched results.
filters : query_filters option;A set of filtering parameters that allow you to specify which entities should be returned.
TrialComponent, Artifact, or Context.include_edges : boolean_ option;Setting this value to True retrieves not only the entities of interest but also the Associations and lineage entities on the path. Set to False to only return lineage entities that match your query.
direction : direction option;Associations between lineage entities have a direction. This parameter determines the direction from the StartArn(s) that the query traverses.
*)start_arns : query_lineage_start_arns option;A list of resource Amazon Resource Name (ARN) that represent the starting point for your lineage query.
*)}type nonrec put_model_package_group_policy_output = {model_package_group_arn : model_package_group_arn;The Amazon Resource Name (ARN) of the model package group.
*)}type nonrec put_model_package_group_policy_input = {resource_policy : policy_string;The resource policy for the model group.
*)model_package_group_name : entity_name;The name of the model group to add a resource policy to.
*)}type nonrec list_workteams_response = {next_token : next_token option;If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.
*)workteams : workteams;An array of Workteam objects, each describing a work team.
}type nonrec list_workteams_request = {max_results : max_results option;The maximum number of work teams to return in each page of the response.
*)next_token : next_token option;If the result of the previous ListWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
name_contains : workteam_name option;A string in the work team's name. This filter returns only work teams whose name contains the specified string.
*)sort_order : sort_order option;The sort order for results. The default is Ascending.
sort_by : list_workteams_sort_by_options option;The field to sort results by. The default is CreationTime.
}type nonrec list_workforces_response = {next_token : next_token option;A token to resume pagination.
*)workforces : workforces;A list containing information about your workforce.
*)}type nonrec list_workforces_request = {max_results : max_results option;The maximum number of workforces returned in the response.
*)next_token : next_token option;A token to resume pagination.
*)name_contains : workforce_name option;A filter you can use to search for workforces using part of the workforce name.
*)sort_order : sort_order option;Sort workforces in ascending or descending order.
*)sort_by : list_workforces_sort_by_options option;Sort workforces using the workforce name or creation date.
*)}type nonrec list_user_profiles_response = {next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)user_profiles : user_profile_list option;The list of user profiles.
*)}type nonrec list_user_profiles_request = {user_profile_name_contains : user_profile_name option;A parameter by which to filter the results.
*)domain_id_equals : domain_id option;A parameter by which to filter the results.
*)sort_by : user_profile_sort_key option;The parameter by which to sort the results. The default is CreationTime.
*)sort_order : sort_order option;The sort order for the results. The default is Ascending.
*)max_results : max_results option;This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.
next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)}type nonrec list_ultra_servers_by_reserved_capacity_response = {ultra_servers : ultra_servers;A list of UltraServers that are part of the specified reserved capacity.
*)next_token : next_token option;If the response is truncated, SageMaker returns this token. Use it in the next request to retrieve the next set of UltraServers.
*)}type nonrec list_ultra_servers_by_reserved_capacity_request = {next_token : next_token option;If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
*)max_results : max_results option;The maximum number of UltraServers to return in the response. The default value is 10.
*)reserved_capacity_arn : reserved_capacity_arn;The ARN of the reserved capacity to list UltraServers for.
*)}type nonrec list_trials_response = {next_token : next_token option;A token for getting the next set of trials, if there are any.
*)trial_summaries : trial_summaries option;A list of the summaries of your trials.
*)}type nonrec list_trials_request = {next_token : next_token option;If the previous call to ListTrials didn't return the full set of trials, the call returns a token for getting the next set of trials.
max_results : max_results option;The maximum number of trials to return in the response. The default value is 10.
*)sort_order : sort_order option;The sort order. The default value is Descending.
sort_by : sort_trials_by option;The property used to sort results. The default value is CreationTime.
created_before : timestamp option;A filter that returns only trials created before the specified time.
*)created_after : timestamp option;A filter that returns only trials created after the specified time.
*)trial_component_name : experiment_entity_name option;A filter that returns only trials that are associated with the specified trial component.
*)experiment_name : experiment_entity_name option;A filter that returns only trials that are part of the specified experiment.
*)}type nonrec list_trial_components_response = {next_token : next_token option;A token for getting the next set of components, if there are any.
*)trial_component_summaries : trial_component_summaries option;A list of the summaries of your trial components.
*)}type nonrec list_trial_components_request = {next_token : next_token option;If the previous call to ListTrialComponents didn't return the full set of components, the call returns a token for getting the next set of components.
max_results : max_results option;The maximum number of components to return in the response. The default value is 10.
*)sort_order : sort_order option;The sort order. The default value is Descending.
sort_by : sort_trial_components_by option;The property used to sort results. The default value is CreationTime.
created_before : timestamp option;A filter that returns only components created before the specified time.
*)created_after : timestamp option;A filter that returns only components created after the specified time.
*)source_arn : string256 option;A filter that returns only components that have the specified source Amazon Resource Name (ARN). If you specify SourceArn, you can't filter by ExperimentName or TrialName.
trial_name : experiment_entity_name option;A filter that returns only components that are part of the specified trial. If you specify TrialName, you can't filter by ExperimentName or SourceArn.
experiment_name : experiment_entity_name option;A filter that returns only components that are part of the specified experiment. If you specify ExperimentName, you can't filter by SourceArn or TrialName.
}type nonrec list_transform_jobs_response = {next_token : next_token option;If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of transform jobs, use it in the next request.
*)transform_job_summaries : transform_job_summaries;An array of TransformJobSummary objects.
}type nonrec list_transform_jobs_request = {max_results : max_results option;The maximum number of transform jobs to return in the response. The default value is 10.
next_token : next_token option;If the result of the previous ListTransformJobs request was truncated, the response includes a NextToken. To retrieve the next set of transform jobs, use the token in the next request.
sort_order : sort_order option;The sort order for results. The default is Descending.
sort_by : sort_by option;The field to sort results by. The default is CreationTime.
status_equals : transform_job_status option;A filter that retrieves only transform jobs with a specific status.
*)name_contains : name_contains option;A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.
*)last_modified_time_before : timestamp option;A filter that returns only transform jobs modified before the specified time.
*)last_modified_time_after : timestamp option;A filter that returns only transform jobs modified after the specified time.
*)creation_time_before : timestamp option;A filter that returns only transform jobs created before the specified time.
*)creation_time_after : timestamp option;A filter that returns only transform jobs created after the specified time.
*)}type nonrec list_training_plans_response = {training_plan_summaries : training_plan_summaries;A list of summary information for the training plans.
*)next_token : next_token option;A token to continue pagination if more results are available.
*)}type nonrec list_training_plans_request = {filters : training_plan_filters option;Additional filters to apply to the list of training plans.
*)sort_order : training_plan_sort_order option;The order to sort the results (Ascending or Descending).
*)sort_by : training_plan_sort_by option;The training plan field to sort the results by (e.g., StartTime, Status).
*)start_time_before : timestamp option;Filter to list only training plans with an actual start time before this date.
*)start_time_after : timestamp option;Filter to list only training plans with an actual start time after this date.
*)max_results : max_results option;The maximum number of results to return in the response.
*)next_token : next_token option;A token to continue pagination if more results are available.
*)}type nonrec hyper_parameter_training_job_summaries =
hyper_parameter_training_job_summary listtype nonrec list_training_jobs_for_hyper_parameter_tuning_job_response = {next_token : next_token option;If the result of this ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.
training_job_summaries : hyper_parameter_training_job_summaries;A list of TrainingJobSummary objects that describe the training jobs that the ListTrainingJobsForHyperParameterTuningJob request returned.
}type nonrec list_training_jobs_for_hyper_parameter_tuning_job_request = {sort_order : sort_order option;The sort order for results. The default is Ascending.
sort_by : training_job_sort_by_options option;The field to sort results by. The default is Name.
If the value of this field is FinalObjectiveMetricValue, any training jobs that did not return an objective metric are not listed.
status_equals : training_job_status option;A filter that returns only training jobs with the specified status.
*)max_results : max_results option;The maximum number of training jobs to return. The default value is 10.
*)next_token : next_token option;If the result of the previous ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.
hyper_parameter_tuning_job_name : hyper_parameter_tuning_job_name;The name of the tuning job whose training jobs you want to list.
*)}type nonrec list_training_jobs_response = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.
*)training_job_summaries : training_job_summaries;An array of TrainingJobSummary objects, each listing a training job.
}type nonrec list_training_jobs_request = {training_plan_arn_equals : training_plan_arn option;The Amazon Resource Name (ARN); of the training plan to filter training jobs by. For more information about reserving GPU capacity for your SageMaker training jobs using Amazon SageMaker Training Plan, see
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingPlan.html}CreateTrainingPlan} .
*)warm_pool_status_equals : warm_pool_resource_status option;A filter that retrieves only training jobs with a specific warm pool status.
*)sort_order : sort_order option;The sort order for results. The default is Ascending.
sort_by : sort_by option;The field to sort results by. The default is CreationTime.
status_equals : training_job_status option;A filter that retrieves only training jobs with a specific status.
*)name_contains : name_contains option;A string in the training job name. This filter returns only training jobs whose name contains the specified string.
*)last_modified_time_before : timestamp option;A filter that returns only training jobs modified before the specified time (timestamp).
*)last_modified_time_after : timestamp option;A filter that returns only training jobs modified after the specified time (timestamp).
*)creation_time_before : timestamp option;A filter that returns only training jobs created before the specified time (timestamp).
*)creation_time_after : timestamp option;A filter that returns only training jobs created after the specified time (timestamp).
*)max_results : max_results option;The maximum number of training jobs to return in the response.
*)next_token : next_token option;If the result of the previous ListTrainingJobs request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.
}type nonrec list_subscribed_workteams_response = {next_token : next_token option;If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.
*)subscribed_workteams : subscribed_workteams;An array of Workteam objects, each describing a work team.
}type nonrec list_subscribed_workteams_request = {max_results : max_results option;The maximum number of work teams to return in each page of the response.
*)next_token : next_token option;If the result of the previous ListSubscribedWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
name_contains : workteam_name option;A string in the work team name. This filter returns only work teams whose name contains the specified string.
*)}type nonrec list_studio_lifecycle_configs_response = {studio_lifecycle_configs : studio_lifecycle_configs_list option;A list of Lifecycle Configurations and their properties.
*)next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)}type nonrec list_studio_lifecycle_configs_request = {sort_order : sort_order option;The sort order. The default value is Descending.
*)sort_by : studio_lifecycle_config_sort_key option;The property used to sort results. The default value is CreationTime.
*)modified_time_after : timestamp option;A filter that returns only Lifecycle Configurations modified after the specified time.
*)modified_time_before : timestamp option;A filter that returns only Lifecycle Configurations modified before the specified time.
*)creation_time_after : timestamp option;A filter that returns only Lifecycle Configurations created on or after the specified time.
*)creation_time_before : timestamp option;A filter that returns only Lifecycle Configurations created on or before the specified time.
*)app_type_equals : studio_lifecycle_config_app_type option;A parameter to search for the App Type to which the Lifecycle Configuration is attached.
*)name_contains : studio_lifecycle_config_name option;A string in the Lifecycle Configuration name. This filter returns only Lifecycle Configurations whose name contains the specified string.
*)next_token : next_token option;If the previous call to ListStudioLifecycleConfigs didn't return the full set of Lifecycle Configurations, the call returns a token for getting the next set of Lifecycle Configurations.
*)max_results : max_results option;The total number of items to return in the response. If the total number of items available is more than the value specified, a NextToken is provided in the response. To resume pagination, provide the NextToken value in the as part of a subsequent call. The default value is 10.
}type nonrec device_deployment_summary = {deployment_start_time : timestamp option;The time when the deployment on the device started.
*)description : device_description option;The description of the device.
*)device_deployment_status_message : string_ option;The detailed error message for the deployoment status result.
*)device_deployment_status : device_deployment_status option;The deployment status of the device.
*)device_arn : device_arn;The ARN of the device.
*)device_name : device_name;The name of the device.
*)device_fleet_name : entity_name option;The name of the fleet to which the device belongs to.
*)deployed_stage_name : entity_name option;The name of the deployed stage.
*)stage_name : entity_name;The name of the stage in the edge deployment plan.
*)edge_deployment_plan_name : entity_name;The name of the edge deployment plan.
*)edge_deployment_plan_arn : edge_deployment_plan_arn;The ARN of the edge deployment plan.
*)}Contains information summarizing device details and deployment status.
type nonrec device_deployment_summaries = device_deployment_summary listtype nonrec list_stage_devices_response = {next_token : next_token option;The token to use when calling the next page of results.
*)device_deployment_summaries : device_deployment_summaries;List of summaries of devices allocated to the stage.
*)}type nonrec list_stage_devices_request = {stage_name : entity_name;The name of the stage in the deployment.
*)exclude_devices_deployed_in_other_stage : boolean_ option;Toggle for excluding devices deployed in other stages.
*)edge_deployment_plan_name : entity_name;The name of the edge deployment plan.
*)max_results : list_max_results option;The maximum number of requests to select.
*)next_token : next_token option;The response from the last list when returning a list large enough to neeed tokening.
*)}type nonrec list_spaces_response = {next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)spaces : space_list option;The list of spaces.
*)}type nonrec list_spaces_request = {space_name_contains : space_name option;A parameter by which to filter the results.
*)domain_id_equals : domain_id option;A parameter to search for the domain ID.
*)sort_by : space_sort_key option;The parameter by which to sort the results. The default is CreationTime.
sort_order : sort_order option;The sort order for the results. The default is Ascending.
max_results : max_results option;This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.
next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)}type nonrec resource_catalog = {creation_time : timestamp;The time the ResourceCatalog was created.
description : resource_catalog_description;A free form description of the ResourceCatalog.
resource_catalog_name : resource_catalog_name;The name of the ResourceCatalog.
resource_catalog_arn : resource_catalog_arn;The Amazon Resource Name (ARN) of the ResourceCatalog.
}A resource catalog containing all of the resources of a specific resource type within a resource owner account. For an example on sharing the Amazon SageMaker Feature Store DefaultFeatureGroupCatalog, see Share Amazon SageMaker Catalog resource type in the Amazon SageMaker Developer Guide.
type nonrec resource_catalog_list = resource_catalog listtype nonrec list_resource_catalogs_response = {next_token : next_token option;A token to resume pagination of ListResourceCatalogs results.
resource_catalogs : resource_catalog_list option;A list of the requested ResourceCatalogs.
}type nonrec list_resource_catalogs_request = {next_token : next_token option;A token to resume pagination of ListResourceCatalogs results.
max_results : max_results option;The maximum number of results returned by ListResourceCatalogs.
sort_by : resource_catalog_sort_by option;The value on which the resource catalog list is sorted.
*)sort_order : resource_catalog_sort_order option;The order in which the resource catalogs are listed.
*)creation_time_before : timestamp option;Use this parameter to search for ResourceCatalogs created before a specific date and time.
creation_time_after : timestamp option;Use this parameter to search for ResourceCatalogs created after a specific date and time.
name_contains : resource_catalog_name option;A string that partially matches one or more ResourceCatalogs names. Filters ResourceCatalog by name.
}type nonrec project_summary = {project_status : project_status;The status of the project.
*)creation_time : timestamp;The time that the project was created.
*)project_id : project_id;The ID of the project.
*)project_arn : project_arn;The Amazon Resource Name (ARN) of the project.
*)project_description : entity_description option;The description of the project.
*)project_name : project_entity_name;The name of the project.
*)}Information about a project.
type nonrec project_summary_list = project_summary listtype nonrec list_projects_output = {next_token : next_token option;If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken. To retrieve the next set of model compilation jobs, use the token in the next request.
project_summary_list : project_summary_list;A list of summaries of projects.
*)}type nonrec list_projects_input = {sort_order : project_sort_order option;The sort order for results. The default is Ascending.
sort_by : project_sort_by option;The field by which to sort results. The default is CreationTime.
next_token : next_token option;If the result of the previous ListProjects request was truncated, the response includes a NextToken. To retrieve the next set of projects, use the token in the next request.
name_contains : project_entity_name option;A filter that returns the projects whose name contains a specified string.
*)max_results : max_results option;The maximum number of projects to return in the response.
*)creation_time_before : timestamp option;A filter that returns the projects that were created before a specified time.
*)creation_time_after : timestamp option;A filter that returns the projects that were created after a specified time.
*)}type nonrec processing_job_summary = {exit_message : exit_message option;An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
*)failure_reason : failure_reason option;A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
*)processing_job_status : processing_job_status;The status of the processing job.
*)last_modified_time : timestamp option;A timestamp that indicates the last time the processing job was modified.
*)processing_end_time : timestamp option;The time at which the processing job completed.
*)creation_time : timestamp;The time at which the processing job was created.
*)processing_job_arn : processing_job_arn;The Amazon Resource Name (ARN) of the processing job..
*)processing_job_name : processing_job_name;The name of the processing job.
*)}Summary of information about a processing job.
type nonrec processing_job_summaries = processing_job_summary listtype nonrec list_processing_jobs_response = {next_token : next_token option;If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of processing jobs, use it in the subsequent request.
*)processing_job_summaries : processing_job_summaries;An array of ProcessingJobSummary objects, each listing a processing job.
}type nonrec list_processing_jobs_request = {max_results : max_results option;The maximum number of processing jobs to return in the response.
*)next_token : next_token option;If the result of the previous ListProcessingJobs request was truncated, the response includes a NextToken. To retrieve the next set of processing jobs, use the token in the next request.
sort_order : sort_order option;The sort order for results. The default is Ascending.
sort_by : sort_by option;The field to sort results by. The default is CreationTime.
status_equals : processing_job_status option;A filter that retrieves only processing jobs with a specific status.
*)name_contains : string_ option;A string in the processing job name. This filter returns only processing jobs whose name contains the specified string.
*)last_modified_time_before : timestamp option;A filter that returns only processing jobs modified before the specified time.
*)last_modified_time_after : timestamp option;A filter that returns only processing jobs modified after the specified time.
*)creation_time_before : timestamp option;A filter that returns only processing jobs created after the specified time.
*)creation_time_after : timestamp option;A filter that returns only processing jobs created after the specified time.
*)}type nonrec pipeline_version_summary = {last_execution_pipeline_execution_arn : pipeline_execution_arn option;The Amazon Resource Name (ARN) of the most recent pipeline execution created from this pipeline version.
*)pipeline_version_display_name : pipeline_version_name option;The display name of the pipeline version.
*)pipeline_version_description : pipeline_version_description option;The description of the pipeline version.
*)creation_time : timestamp option;The creation time of the pipeline version.
*)pipeline_version_id : pipeline_version_id option;The ID of the pipeline version.
*)pipeline_arn : pipeline_arn option;The Amazon Resource Name (ARN) of the pipeline.
*)}The summary of the pipeline version.
type nonrec pipeline_version_summary_list = pipeline_version_summary listtype nonrec list_pipeline_versions_response = {next_token : next_token option;If the result of the previous ListPipelineVersions request was truncated, the response includes a NextToken. To retrieve the next set of pipeline versions, use this token in your next request.
pipeline_version_summaries : pipeline_version_summary_list option;Contains a sorted list of pipeline version summary objects matching the specified filters. Each version summary includes the pipeline version ID, the creation date, and the last pipeline execution created from that version. This list can be empty.
*)}type nonrec list_pipeline_versions_request = {max_results : max_results option;The maximum number of pipeline versions to return in the response.
*)next_token : next_token option;If the result of the previous ListPipelineVersions request was truncated, the response includes a NextToken. To retrieve the next set of pipeline versions, use this token in your next request.
sort_order : sort_order option;The sort order for the results.
*)created_before : timestamp option;A filter that returns the pipeline versions that were created before a specified time.
*)created_after : timestamp option;A filter that returns the pipeline versions that were created after a specified time.
*)pipeline_name : pipeline_name_or_arn;The Amazon Resource Name (ARN) of the pipeline.
*)}type nonrec pipeline_summary = {last_execution_time : timestamp option;The last time that a pipeline execution began.
*)last_modified_time : timestamp option;The time that the pipeline was last modified.
*)creation_time : timestamp option;The creation time of the pipeline.
*)role_arn : role_arn option;The Amazon Resource Name (ARN) that the pipeline used to execute.
*)pipeline_description : pipeline_description option;The description of the pipeline.
*)pipeline_display_name : pipeline_name option;The display name of the pipeline.
*)pipeline_name : pipeline_name option;The name of the pipeline.
*)pipeline_arn : pipeline_arn option;The Amazon Resource Name (ARN) of the pipeline.
*)}A summary of a pipeline.
type nonrec pipeline_summary_list = pipeline_summary listtype nonrec list_pipelines_response = {next_token : next_token option;If the result of the previous ListPipelines request was truncated, the response includes a NextToken. To retrieve the next set of pipelines, use the token in the next request.
pipeline_summaries : pipeline_summary_list option;Contains a sorted list of PipelineSummary objects matching the specified filters. Each PipelineSummary consists of PipelineArn, PipelineName, ExperimentName, PipelineDescription, CreationTime, LastModifiedTime, LastRunTime, and RoleArn. This list can be empty.
}type nonrec list_pipelines_request = {max_results : max_results option;The maximum number of pipelines to return in the response.
*)next_token : next_token option;If the result of the previous ListPipelines request was truncated, the response includes a NextToken. To retrieve the next set of pipelines, use the token in the next request.
sort_order : sort_order option;The sort order for results.
*)sort_by : sort_pipelines_by option;The field by which to sort results. The default is CreatedTime.
created_before : timestamp option;A filter that returns the pipelines that were created before a specified time.
*)created_after : timestamp option;A filter that returns the pipelines that were created after a specified time.
*)pipeline_name_prefix : pipeline_name option;The prefix of the pipeline name.
*)}type nonrec list_pipeline_parameters_for_execution_response = {next_token : next_token option;If the result of the previous ListPipelineParametersForExecution request was truncated, the response includes a NextToken. To retrieve the next set of parameters, use the token in the next request.
pipeline_parameters : parameter_list option;Contains a list of pipeline parameters. This list can be empty.
*)}type nonrec list_pipeline_parameters_for_execution_request = {max_results : max_results option;The maximum number of parameters to return in the response.
*)next_token : next_token option;If the result of the previous ListPipelineParametersForExecution request was truncated, the response includes a NextToken. To retrieve the next set of parameters, use the token in the next request.
pipeline_execution_arn : pipeline_execution_arn;The Amazon Resource Name (ARN) of the pipeline execution.
*)}type nonrec cache_hit_result = {source_pipeline_execution_arn : pipeline_execution_arn option;The Amazon Resource Name (ARN) of the pipeline execution.
*)}Details on the cache hit of a pipeline execution step.
type nonrec processing_job_step_metadata = {arn : processing_job_arn option;The Amazon Resource Name (ARN) of the processing job.
*)}Metadata for a processing job step.
type nonrec model_step_metadata = {arn : string256 option;The Amazon Resource Name (ARN) of the created model.
*)}Metadata for Model steps.
type nonrec register_model_step_metadata = {arn : string256 option;The Amazon Resource Name (ARN) of the model package.
*)}Metadata for a register model job step.
type nonrec condition_step_metadata = {outcome : condition_outcome option;The outcome of the Condition step evaluation.
*)}Metadata for a Condition step.
type nonrec callback_step_metadata = {output_parameters : output_parameter_list option;A list of the output parameters of the callback step.
*)sqs_queue_url : string256 option;The URL of the Amazon Simple Queue Service (Amazon SQS) queue used by the callback step.
*)callback_token : callback_token option;The pipeline generated token from the Amazon SQS queue.
*)}Metadata about a callback step.
type nonrec lambda_step_metadata = {output_parameters : output_parameter_list option;A list of the output parameters of the Lambda step.
*)arn : string256 option;The Amazon Resource Name (ARN) of the Lambda function that was run by this step execution.
*)}Metadata for a Lambda step.
type nonrec emr_step_metadata = {log_file_path : string1024 option;The path to the log file where the cluster step's failure root cause is recorded.
*)step_name : string256 option;The name of the EMR cluster step.
*)step_id : string256 option;The identifier of the EMR cluster step.
*)cluster_id : string256 option;The identifier of the EMR cluster.
*)}The configurations and outcomes of an Amazon EMR step execution.
type nonrec quality_check_step_metadata = {register_new_baseline : boolean_ option;This flag indicates if a newly calculated baseline can be accessed through step properties BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics. If it is set to False, the previous baseline of the configured check type must also be available. These can be accessed through the BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics properties.
skip_check : boolean_ option;This flag indicates if the drift check against the previous baseline will be skipped or not. If it is set to False, the previous baseline of the configured check type must be available.
check_job_arn : string256 option;The Amazon Resource Name (ARN) of the Quality check processing job that was run by this step execution.
*)violation_report : string1024 option;The Amazon S3 URI of violation report if violations are detected.
*)model_package_group_name : string256 option;The model package group name.
*)calculated_baseline_constraints : string1024 option;The Amazon S3 URI of the newly calculated baseline constraints file.
*)calculated_baseline_statistics : string1024 option;The Amazon S3 URI of the newly calculated baseline statistics file.
*)baseline_used_for_drift_check_constraints : string1024 option;The Amazon S3 URI of the baseline constraints file used for the drift check.
*)baseline_used_for_drift_check_statistics : string1024 option;The Amazon S3 URI of the baseline statistics file used for the drift check.
*)check_type : string256 option;The type of the Quality check step.
*)}Container for the metadata for a Quality check step. For more information, see the topic on QualityCheck step in the Amazon SageMaker Developer Guide.
type nonrec clarify_check_step_metadata = {register_new_baseline : boolean_ option;This flag indicates if a newly calculated baseline can be accessed through step properties BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics. If it is set to False, the previous baseline of the configured check type must also be available. These can be accessed through the BaselineUsedForDriftCheckConstraints property.
skip_check : boolean_ option;This flag indicates if the drift check against the previous baseline will be skipped or not. If it is set to False, the previous baseline of the configured check type must be available.
check_job_arn : string256 option;The Amazon Resource Name (ARN) of the check processing job that was run by this step's execution.
*)violation_report : string1024 option;The Amazon S3 URI of the violation report if violations are detected.
*)model_package_group_name : string256 option;The model package group name.
*)calculated_baseline_constraints : string1024 option;The Amazon S3 URI of the newly calculated baseline constraints file.
*)baseline_used_for_drift_check_constraints : string1024 option;The Amazon S3 URI of baseline constraints file to be used for the drift check.
*)check_type : string256 option;The type of the Clarify Check step
*)}The container for the metadata for the ClarifyCheck step. For more information, see the topic on ClarifyCheck step in the Amazon SageMaker Developer Guide.
type nonrec fail_step_metadata = {error_message : string3072 option;A message that you define and then is processed and rendered by the Fail step when the error occurs.
*)}The container for the metadata for Fail step.
type nonrec auto_ml_job_step_metadata = {arn : auto_ml_job_arn option;The Amazon Resource Name (ARN) of the AutoML job.
*)}Metadata for an AutoML job step.
type nonrec endpoint_step_metadata = {arn : endpoint_arn option;The Amazon Resource Name (ARN) of the endpoint in the step.
*)}Metadata for an endpoint step.
type nonrec endpoint_config_step_metadata = {arn : endpoint_config_arn option;The Amazon Resource Name (ARN) of the endpoint configuration used in the step.
*)}Metadata for an endpoint configuration step.
type nonrec bedrock_custom_model_metadata = {arn : string1024 option;The Amazon Resource Name (ARN) of the Amazon Bedrock custom model.
*)}The metadata of the Amazon Bedrock custom model.
type nonrec bedrock_custom_model_deployment_metadata = {arn : string1024 option;The Amazon Resource Name (ARN) for the Amazon Bedrock custom model deployment.
*)}The metadata of the Amazon Bedrock custom model deployment.
type nonrec bedrock_provisioned_model_throughput_metadata = {arn : string1024 option;The Amazon Resource Name (ARN) of the Amazon Bedrock provisioned model throughput.
*)}The metadata of the Amazon Bedrock provisioned model throughput.
type nonrec bedrock_model_import_metadata = {arn : string1024 option;The Amazon Resource Name (ARN) of the Amazon Bedrock model import.
*)}The metadata of the Amazon Bedrock model import.
type nonrec inference_component_metadata = {arn : string2048 option;The Amazon Resource Name (ARN) of the inference component.
*)}The metadata of the inference component.
type nonrec map_string2048 = (string2048 * string2048) listtype nonrec association_info = {destination_arn : string2048;The Amazon Resource Name (ARN) of the AssociationInfo destination.
source_arn : string2048;The Amazon Resource Name (ARN) of the AssociationInfo source.
}The data type used to describe the relationship between different sources.
type nonrec association_info_list = association_info listtype nonrec lineage_metadata = {associations : association_info_list option;The lineage associations.
*)context_arns : map_string2048 option;The Amazon Resource Name (ARN) of the lineage context.
*)artifact_arns : map_string2048 option;The Amazon Resource Name (ARN) of the lineage artifact.
*)action_arns : map_string2048 option;The Amazon Resource Name (ARN) of the lineage action.
*)}The metadata that tracks relationships between ML artifacts, actions, and contexts.
type nonrec job_step_metadata = {arn : string1024 option;The Amazon Resource Name (ARN) of the SageMaker job that was run by this step execution.
*)}Metadata for a SageMaker job step.
type nonrec pipeline_execution_step_metadata = {job : job_step_metadata option;The metadata for a SageMaker job used in a pipeline execution step.
*)lineage : lineage_metadata option;The metadata of the lineage used in pipeline execution step.
*)inference_component : inference_component_metadata option;The metadata of the inference component used in pipeline execution step.
*)bedrock_model_import : bedrock_model_import_metadata option;The metadata of Amazon Bedrock model import used in pipeline execution step.
*)bedrock_provisioned_model_throughput : bedrock_provisioned_model_throughput_metadata
option;The metadata of the Amazon Bedrock provisioned model throughput used in the pipeline execution step.
*)bedrock_custom_model_deployment : bedrock_custom_model_deployment_metadata
option;The metadata of the Amazon Bedrock custom model deployment used in pipeline execution step.
*)bedrock_custom_model : bedrock_custom_model_metadata option;The metadata of the Amazon Bedrock custom model used in the pipeline execution step.
*)endpoint_config : endpoint_config_step_metadata option;The endpoint configuration used to create an endpoint during this step execution.
*)endpoint : endpoint_step_metadata option;The endpoint that was invoked during this step execution.
*)auto_ml_job : auto_ml_job_step_metadata option;The Amazon Resource Name (ARN) of the AutoML job that was run by this step.
*)fail : fail_step_metadata option;The configurations and outcomes of a Fail step execution.
*)clarify_check : clarify_check_step_metadata option;Container for the metadata for a Clarify check step. The configurations and outcomes of the check step execution. This includes:
BaselineUsedForDriftCheck is set the same as CalculatedBaseline.quality_check : quality_check_step_metadata option;The configurations and outcomes of the check step execution. This includes:
BaselineUsedForDriftCheck is set the same as CalculatedBaseline.em_r : emr_step_metadata option;The configurations and outcomes of an Amazon EMR step execution.
*)lambda : lambda_step_metadata option;The Amazon Resource Name (ARN) of the Lambda function that was run by this step execution and a list of output parameters.
*)callback : callback_step_metadata option;The URL of the Amazon SQS queue used by this step execution, the pipeline generated token, and a list of output parameters.
*)condition : condition_step_metadata option;The outcome of the condition evaluation that was run by this step execution.
*)register_model : register_model_step_metadata option;The Amazon Resource Name (ARN) of the model package that the model was registered to by this step execution.
*)model : model_step_metadata option;The Amazon Resource Name (ARN) of the model that was created by this step execution.
*)tuning_job : tuning_job_step_meta_data option;The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.
*)transform_job : transform_job_step_metadata option;The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
*)processing_job : processing_job_step_metadata option;The Amazon Resource Name (ARN) of the processing job that was run by this step execution.
*)training_job : training_job_step_metadata option;The Amazon Resource Name (ARN) of the training job that was run by this step execution.
*)}Metadata for a step execution.
type nonrec pipeline_execution_step = {selective_execution_result : selective_execution_result option;The ARN from an execution of the current pipeline from which results are reused for this step.
*)attempt_count : integer option;The current attempt of the execution step. For more information, see Retry Policy for SageMaker Pipelines steps.
*)metadata : pipeline_execution_step_metadata option;Metadata to run the pipeline step.
*)failure_reason : failure_reason option;The reason why the step failed execution. This is only returned if the step failed its execution.
*)cache_hit_result : cache_hit_result option;If this pipeline execution step was cached, details on the cache hit.
*)step_status : step_status option;The status of the step execution.
*)end_time : timestamp option;The time that the step stopped executing.
*)start_time : timestamp option;The time that the step started executing.
*)step_description : step_description option;The description of the step.
*)step_display_name : step_display_name option;The display name of the step.
*)step_name : step_name option;The name of the step that is executed.
*)}An execution of a step in a pipeline.
type nonrec pipeline_execution_step_list = pipeline_execution_step listtype nonrec list_pipeline_execution_steps_response = {next_token : next_token option;If the result of the previous ListPipelineExecutionSteps request was truncated, the response includes a NextToken. To retrieve the next set of pipeline execution steps, use the token in the next request.
pipeline_execution_steps : pipeline_execution_step_list option;A list of PipeLineExecutionStep objects. Each PipeLineExecutionStep consists of StepName, StartTime, EndTime, StepStatus, and Metadata. Metadata is an object with properties for each job that contains relevant information about the job created by the step.
}type nonrec list_pipeline_execution_steps_request = {sort_order : sort_order option;The field by which to sort results. The default is CreatedTime.
max_results : max_results option;The maximum number of pipeline execution steps to return in the response.
*)next_token : next_token option;If the result of the previous ListPipelineExecutionSteps request was truncated, the response includes a NextToken. To retrieve the next set of pipeline execution steps, use the token in the next request.
pipeline_execution_arn : pipeline_execution_arn option;The Amazon Resource Name (ARN) of the pipeline execution.
*)}type nonrec pipeline_execution_summary = {pipeline_execution_failure_reason : string3072 option;A message generated by SageMaker Pipelines describing why the pipeline execution failed.
*)pipeline_execution_display_name : pipeline_execution_name option;The display name of the pipeline execution.
*)pipeline_execution_description : pipeline_execution_description option;The description of the pipeline execution.
*)pipeline_execution_status : pipeline_execution_status option;The status of the pipeline execution.
*)start_time : timestamp option;The start time of the pipeline execution.
*)pipeline_execution_arn : pipeline_execution_arn option;The Amazon Resource Name (ARN) of the pipeline execution.
*)}A pipeline execution summary.
type nonrec pipeline_execution_summary_list = pipeline_execution_summary listtype nonrec list_pipeline_executions_response = {next_token : next_token option;If the result of the previous ListPipelineExecutions request was truncated, the response includes a NextToken. To retrieve the next set of pipeline executions, use the token in the next request.
pipeline_execution_summaries : pipeline_execution_summary_list option;Contains a sorted list of pipeline execution summary objects matching the specified filters. Each run summary includes the Amazon Resource Name (ARN) of the pipeline execution, the run date, and the status. This list can be empty.
*)}type nonrec list_pipeline_executions_request = {max_results : max_results option;The maximum number of pipeline executions to return in the response.
*)next_token : next_token option;If the result of the previous ListPipelineExecutions request was truncated, the response includes a NextToken. To retrieve the next set of pipeline executions, use the token in the next request.
sort_order : sort_order option;The sort order for results.
*)sort_by : sort_pipeline_executions_by option;The field by which to sort results. The default is CreatedTime.
created_before : timestamp option;A filter that returns the pipeline executions that were created before a specified time.
*)created_after : timestamp option;A filter that returns the pipeline executions that were created after a specified time.
*)pipeline_name : pipeline_name_or_arn;The name or Amazon Resource Name (ARN) of the pipeline.
*)}type nonrec partner_app_summary = {creation_time : timestamp option;The creation time of the SageMaker Partner AI App.
*)status : partner_app_status option;The status of the SageMaker Partner AI App.
*)type_ : partner_app_type option;The type of SageMaker Partner AI App to create. Must be one of the following: lakera-guard, comet, deepchecks-llm-evaluation, or fiddler.
name : partner_app_name option;The name of the SageMaker Partner AI App.
*)arn : partner_app_arn option;The ARN of the SageMaker Partner AI App.
*)}A subset of information related to a SageMaker Partner AI App. This information is used as part of the ListPartnerApps API response.
type nonrec partner_app_summaries = partner_app_summary listtype nonrec list_partner_apps_response = {next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)summaries : partner_app_summaries option;The information related to each of the SageMaker Partner AI Apps in an account.
*)}type nonrec list_partner_apps_request = {next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)max_results : max_results option;This parameter defines the maximum number of results that can be returned in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.
}type nonrec optimization_job_deployment_instance_type = | ML_TRN1N_32XLARGE| ML_TRN1_32XLARGE| ML_TRN1_2XLARGE| ML_INF2_48XLARGE| ML_INF2_24XLARGE| ML_INF2_8XLARGE| ML_INF2_XLARGE| ML_G6E_48XLARGE| ML_G6E_24XLARGE| ML_G6E_16XLARGE| ML_G6E_12XLARGE| ML_G6E_8XLARGE| ML_G6E_4XLARGE| ML_G6E_2XLARGE| ML_G6E_XLARGE| ML_G6_48XLARGE| ML_G6_24XLARGE| ML_G6_16XLARGE| ML_G6_12XLARGE| ML_G6_8XLARGE| ML_G6_4XLARGE| ML_G6_2XLARGE| ML_G6_XLARGE| ML_G5_48XLARGE| ML_G5_24XLARGE| ML_G5_16XLARGE| ML_G5_12XLARGE| ML_G5_8XLARGE| ML_G5_4XLARGE| ML_G5_2XLARGE| ML_G5_XLARGE| ML_G4DN_16XLARGE| ML_G4DN_12XLARGE| ML_G4DN_8XLARGE| ML_G4DN_4XLARGE| ML_G4DN_2XLARGE| ML_G4DN_XLARGE| ML_P5EN_48XLARGE| ML_P5E_48XLARGE| ML_P5_48XLARGE| ML_P4DE_24XLARGE| ML_P4D_24XLARGEtype nonrec optimization_types = optimization_type listtype nonrec optimization_job_summary = {optimization_types : optimization_types;The optimization techniques that are applied by the optimization job.
*)max_instance_count : optimization_job_max_instance_count option;The maximum number of instances to use for the optimization job.
*)deployment_instance_type : optimization_job_deployment_instance_type;The type of instance that hosts the optimized model that you create with the optimization job.
*)last_modified_time : last_modified_time option;The time when the optimization job was last updated.
*)optimization_end_time : timestamp option;The time when the optimization job finished processing.
*)optimization_start_time : timestamp option;The time when the optimization job started.
*)optimization_job_status : optimization_job_status;The current status of the optimization job.
*)creation_time : creation_time;The time when you created the optimization job.
*)optimization_job_arn : optimization_job_arn;The Amazon Resource Name (ARN) of the optimization job.
*)optimization_job_name : entity_name;The name that you assigned to the optimization job.
*)}Summarizes an optimization job by providing some of its key properties.
type nonrec optimization_job_summaries = optimization_job_summary listtype nonrec list_optimization_jobs_response = {next_token : next_token option;The token to use in a subsequent request to get the next set of results following a truncated response.
*)optimization_job_summaries : optimization_job_summaries;A list of optimization jobs and their properties that matches any of the filters you specified in the request.
*)}type nonrec list_optimization_jobs_request = {sort_order : sort_order option;The sort order for results. The default is Ascending
sort_by : list_optimization_jobs_sort_by option;The field by which to sort the optimization jobs in the response. The default is CreationTime
status_equals : optimization_job_status option;Filters the results to only those optimization jobs with the specified status.
*)name_contains : name_contains option;Filters the results to only those optimization jobs with a name that contains the specified string.
*)optimization_contains : name_contains option;Filters the results to only those optimization jobs that apply the specified optimization techniques. You can specify either Quantization or Compilation.
last_modified_time_before : last_modified_time option;Filters the results to only those optimization jobs that were updated before the specified time.
*)last_modified_time_after : last_modified_time option;Filters the results to only those optimization jobs that were updated after the specified time.
*)creation_time_before : creation_time option;Filters the results to only those optimization jobs that were created before the specified time.
*)creation_time_after : creation_time option;Filters the results to only those optimization jobs that were created after the specified time.
*)max_results : max_results option;The maximum number of optimization jobs to return in the response. The default is 50.
*)next_token : next_token option;A token that you use to get the next set of results following a truncated response. If the response to the previous request was truncated, that response provides the value for this token.
*)}type nonrec notebook_instance_summary = {additional_code_repositories : additional_code_repository_names_or_urls option;An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.
*)default_code_repository : code_repository_name_or_url option;The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.
*)notebook_instance_lifecycle_config_name : notebook_instance_lifecycle_config_name
option;The name of a notebook instance lifecycle configuration associated with this notebook instance.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
*)last_modified_time : last_modified_time option;A timestamp that shows when the notebook instance was last modified.
*)creation_time : creation_time option;A timestamp that shows when the notebook instance was created.
*)instance_type : instance_type option;The type of ML compute instance that the notebook instance is running on.
*)url : notebook_instance_url option;The URL that you use to connect to the Jupyter notebook running in your notebook instance.
*)notebook_instance_status : notebook_instance_status option;The status of the notebook instance.
*)notebook_instance_arn : notebook_instance_arn;The Amazon Resource Name (ARN) of the notebook instance.
*)notebook_instance_name : notebook_instance_name;The name of the notebook instance that you want a summary for.
*)}Provides summary information for an SageMaker AI notebook instance.
type nonrec notebook_instance_summary_list = notebook_instance_summary listtype nonrec list_notebook_instances_output = {notebook_instances : notebook_instance_summary_list option;An array of NotebookInstanceSummary objects, one for each notebook instance.
next_token : next_token option;If the response to the previous ListNotebookInstances request was truncated, SageMaker AI returns this token. To retrieve the next set of notebook instances, use the token in the next request.
}type nonrec list_notebook_instances_input = {additional_code_repository_equals : code_repository_name_or_url option;A filter that returns only notebook instances with associated with the specified git repository.
*)default_code_repository_contains : code_repository_contains option;A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string.
*)notebook_instance_lifecycle_config_name_contains : notebook_instance_lifecycle_config_name
option;A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string.
*)status_equals : notebook_instance_status option;A filter that returns only notebook instances with the specified status.
*)last_modified_time_after : last_modified_time option;A filter that returns only notebook instances that were modified after the specified time (timestamp).
*)last_modified_time_before : last_modified_time option;A filter that returns only notebook instances that were modified before the specified time (timestamp).
*)creation_time_after : creation_time option;A filter that returns only notebook instances that were created after the specified time (timestamp).
*)creation_time_before : creation_time option;A filter that returns only notebook instances that were created before the specified time (timestamp).
*)name_contains : notebook_instance_name_contains option;A string in the notebook instances' name. This filter returns only notebook instances whose name contains the specified string.
*)sort_order : notebook_instance_sort_order option;The sort order for results.
*)sort_by : notebook_instance_sort_key option;The field to sort results by. The default is Name.
max_results : max_results option;The maximum number of notebook instances to return.
*)next_token : next_token option;If the previous call to the ListNotebookInstances is truncated, the response includes a NextToken. You can use this token in your subsequent ListNotebookInstances request to fetch the next set of notebook instances.
You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request.
*)}type nonrec notebook_instance_lifecycle_config_summary = {last_modified_time : last_modified_time option;A timestamp that tells when the lifecycle configuration was last modified.
*)creation_time : creation_time option;A timestamp that tells when the lifecycle configuration was created.
*)notebook_instance_lifecycle_config_arn : notebook_instance_lifecycle_config_arn;The Amazon Resource Name (ARN) of the lifecycle configuration.
*)notebook_instance_lifecycle_config_name : notebook_instance_lifecycle_config_name;The name of the lifecycle configuration.
*)}Provides a summary of a notebook instance lifecycle configuration.
type nonrec notebook_instance_lifecycle_config_summary_list =
notebook_instance_lifecycle_config_summary listtype nonrec list_notebook_instance_lifecycle_configs_output = {notebook_instance_lifecycle_configs : notebook_instance_lifecycle_config_summary_list
option;An array of NotebookInstanceLifecycleConfiguration objects, each listing a lifecycle configuration.
next_token : next_token option;If the response is truncated, SageMaker AI returns this token. To get the next set of lifecycle configurations, use it in the next request.
*)}type nonrec list_notebook_instance_lifecycle_configs_input = {last_modified_time_after : last_modified_time option;A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).
*)last_modified_time_before : last_modified_time option;A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).
*)creation_time_after : creation_time option;A filter that returns only lifecycle configurations that were created after the specified time (timestamp).
*)creation_time_before : creation_time option;A filter that returns only lifecycle configurations that were created before the specified time (timestamp).
*)name_contains : notebook_instance_lifecycle_config_name_contains option;A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.
*)sort_order : notebook_instance_lifecycle_config_sort_order option;The sort order for results.
*)sort_by : notebook_instance_lifecycle_config_sort_key option;Sorts the list of results. The default is CreationTime.
max_results : max_results option;The maximum number of lifecycle configurations to return in the response.
*)next_token : next_token option;If the result of a ListNotebookInstanceLifecycleConfigs request was truncated, the response includes a NextToken. To get the next set of lifecycle configurations, use the token in the next request.
}type nonrec monitoring_schedule_summary = {monitoring_type : monitoring_type option;The type of the monitoring job definition that the schedule is for.
*)monitoring_job_definition_name : monitoring_job_definition_name option;The name of the monitoring job definition that the schedule is for.
*)endpoint_name : endpoint_name option;The name of the endpoint using the monitoring schedule.
*)monitoring_schedule_status : schedule_status;The status of the monitoring schedule.
*)last_modified_time : timestamp;The last time the monitoring schedule was modified.
*)creation_time : timestamp;The creation time of the monitoring schedule.
*)monitoring_schedule_arn : monitoring_schedule_arn;The Amazon Resource Name (ARN) of the monitoring schedule.
*)monitoring_schedule_name : monitoring_schedule_name;The name of the monitoring schedule.
*)}Summarizes the monitoring schedule.
type nonrec monitoring_schedule_summary_list = monitoring_schedule_summary listtype nonrec list_monitoring_schedules_response = {next_token : next_token option;The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
*)monitoring_schedule_summaries : monitoring_schedule_summary_list;A JSON array in which each element is a summary for a monitoring schedule.
*)}type nonrec list_monitoring_schedules_request = {monitoring_type_equals : monitoring_type option;A filter that returns only the monitoring schedules for the specified monitoring type.
*)monitoring_job_definition_name : monitoring_job_definition_name option;Gets a list of the monitoring schedules for the specified monitoring job definition.
*)status_equals : schedule_status option;A filter that returns only monitoring schedules modified before a specified time.
*)last_modified_time_after : timestamp option;A filter that returns only monitoring schedules modified after a specified time.
*)last_modified_time_before : timestamp option;A filter that returns only monitoring schedules modified before a specified time.
*)creation_time_after : timestamp option;A filter that returns only monitoring schedules created after a specified time.
*)creation_time_before : timestamp option;A filter that returns only monitoring schedules created before a specified time.
*)name_contains : name_contains option;Filter for monitoring schedules whose name contains a specified string.
*)max_results : max_results option;The maximum number of jobs to return in the response. The default value is 10.
*)next_token : next_token option;The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
*)sort_order : sort_order option;Whether to sort the results in Ascending or Descending order. The default is Descending.
sort_by : monitoring_schedule_sort_key option;Whether to sort the results by the Status, CreationTime, or ScheduledTime field. The default is CreationTime.
endpoint_name : endpoint_name option;Name of a specific endpoint to fetch schedules for.
*)}type nonrec monitoring_execution_summary_list =
monitoring_execution_summary listtype nonrec list_monitoring_executions_response = {next_token : next_token option;The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
*)monitoring_execution_summaries : monitoring_execution_summary_list;A JSON array in which each element is a summary for a monitoring execution.
*)}type nonrec list_monitoring_executions_request = {monitoring_type_equals : monitoring_type option;A filter that returns only the monitoring job runs of the specified monitoring type.
*)monitoring_job_definition_name : monitoring_job_definition_name option;Gets a list of the monitoring job runs of the specified monitoring job definitions.
*)status_equals : execution_status option;A filter that retrieves only jobs with a specific status.
*)last_modified_time_after : timestamp option;A filter that returns only jobs modified before a specified time.
*)last_modified_time_before : timestamp option;A filter that returns only jobs modified after a specified time.
*)creation_time_after : timestamp option;A filter that returns only jobs created after a specified time.
*)creation_time_before : timestamp option;A filter that returns only jobs created before a specified time.
*)scheduled_time_after : timestamp option;Filter for jobs scheduled after a specified time.
*)scheduled_time_before : timestamp option;Filter for jobs scheduled before a specified time.
*)max_results : max_results option;The maximum number of jobs to return in the response. The default value is 10.
*)next_token : next_token option;The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
*)sort_order : sort_order option;Whether to sort the results in Ascending or Descending order. The default is Descending.
sort_by : monitoring_execution_sort_key option;Whether to sort the results by the Status, CreationTime, or ScheduledTime field. The default is CreationTime.
endpoint_name : endpoint_name option;Name of a specific endpoint to fetch jobs for.
*)monitoring_schedule_name : monitoring_schedule_name option;Name of a specific schedule to fetch jobs for.
*)}type nonrec list_monitoring_alerts_response = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of alerts, use it in the subsequent request.
*)monitoring_alert_summaries : monitoring_alert_summary_list option;A JSON array where each element is a summary for a monitoring alert.
*)}type nonrec list_monitoring_alerts_request = {max_results : max_results option;The maximum number of results to display. The default is 100.
*)next_token : next_token option;If the result of the previous ListMonitoringAlerts request was truncated, the response includes a NextToken. To retrieve the next set of alerts in the history, use the token in the next request.
monitoring_schedule_name : monitoring_schedule_name;The name of a monitoring schedule.
*)}type nonrec monitoring_alert_history_summary = {alert_status : monitoring_alert_status;The current alert status of an alert.
*)creation_time : timestamp;A timestamp that indicates when the first alert transition occurred in an alert history. An alert transition can be from status InAlert to OK, or from OK to InAlert.
monitoring_alert_name : monitoring_alert_name;The name of a monitoring alert.
*)monitoring_schedule_name : monitoring_schedule_name;The name of a monitoring schedule.
*)}Provides summary information of an alert's history.
type nonrec monitoring_alert_history_list =
monitoring_alert_history_summary listtype nonrec list_monitoring_alert_history_response = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of alerts, use it in the subsequent request.
*)monitoring_alert_history : monitoring_alert_history_list option;An alert history for a model monitoring schedule.
*)}type nonrec list_monitoring_alert_history_request = {status_equals : monitoring_alert_status option;A filter that retrieves only alerts with a specific status.
*)creation_time_after : timestamp option;A filter that returns only alerts created on or after the specified time.
*)creation_time_before : timestamp option;A filter that returns only alerts created on or before the specified time.
*)max_results : max_results option;The maximum number of results to display. The default is 100.
*)next_token : next_token option;If the result of the previous ListMonitoringAlertHistory request was truncated, the response includes a NextToken. To retrieve the next set of alerts in the history, use the token in the next request.
sort_order : sort_order option;The sort order, whether Ascending or Descending, of the alert history. The default is Descending.
sort_by : monitoring_alert_history_sort_key option;The field used to sort results. The default is CreationTime.
monitoring_alert_name : monitoring_alert_name option;The name of a monitoring alert.
*)monitoring_schedule_name : monitoring_schedule_name option;The name of a monitoring schedule.
*)}type nonrec model_summary = {creation_time : timestamp;A timestamp that indicates when the model was created.
*)model_arn : model_arn;The Amazon Resource Name (ARN) of the model.
*)model_name : model_name;The name of the model that you want a summary for.
*)}Provides summary information about a model.
type nonrec model_summary_list = model_summary listtype nonrec list_models_output = {next_token : pagination_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of models, use it in the subsequent request.
*)models : model_summary_list;An array of ModelSummary objects, each of which lists a model.
}type nonrec list_models_input = {creation_time_after : timestamp option;A filter that returns only models with a creation time greater than or equal to the specified time (timestamp).
*)creation_time_before : timestamp option;A filter that returns only models created before the specified time (timestamp).
*)name_contains : model_name_contains option;A string in the model name. This filter returns only models whose name contains the specified string.
*)max_results : max_results option;The maximum number of models to return in the response.
*)next_token : pagination_token option;If the response to a previous ListModels request was truncated, the response includes a NextToken. To retrieve the next set of models, use the token in the next request.
sort_order : order_key option;The sort order for results. The default is Descending.
sort_by : model_sort_key option;Sorts the list of results. The default is CreationTime.
}type nonrec monitoring_job_definition_summary = {endpoint_name : endpoint_name;The name of the endpoint that the job monitors.
*)creation_time : timestamp;The time that the monitoring job was created.
*)monitoring_job_definition_arn : monitoring_job_definition_arn;The Amazon Resource Name (ARN) of the monitoring job.
*)monitoring_job_definition_name : monitoring_job_definition_name;The name of the monitoring job.
*)}Summary information about a monitoring job.
type nonrec monitoring_job_definition_summary_list =
monitoring_job_definition_summary listtype nonrec list_model_quality_job_definitions_response = {next_token : next_token option;If the response is truncated, Amazon SageMaker AI returns this token. To retrieve the next set of model quality monitoring job definitions, use it in the next request.
*)job_definition_summaries : monitoring_job_definition_summary_list;A list of summaries of model quality monitoring job definitions.
*)}type nonrec list_model_quality_job_definitions_request = {creation_time_after : timestamp option;A filter that returns only model quality monitoring job definitions created after the specified time.
*)creation_time_before : timestamp option;A filter that returns only model quality monitoring job definitions created before the specified time.
*)name_contains : name_contains option;A string in the transform job name. This filter returns only model quality monitoring job definitions whose name contains the specified string.
*)max_results : max_results option;The maximum number of results to return in a call to ListModelQualityJobDefinitions.
next_token : next_token option;If the result of the previous ListModelQualityJobDefinitions request was truncated, the response includes a NextToken. To retrieve the next set of model quality monitoring job definitions, use the token in the next request.
sort_order : sort_order option;Whether to sort the results in Ascending or Descending order. The default is Descending.
sort_by : monitoring_job_definition_sort_key option;The field to sort results by. The default is CreationTime.
endpoint_name : endpoint_name option;A filter that returns only model quality monitoring job definitions that are associated with the specified endpoint.
*)}type nonrec model_package_summary = {model_package_registration_type : model_package_registration_type option;The package registration type of the model package summary.
*)model_life_cycle : model_life_cycle option;model_approval_status : model_approval_status option;The approval status of the model. This can be one of the following values.
APPROVED - The model is approvedREJECTED - The model is rejected.PENDING_MANUAL_APPROVAL - The model is waiting for manual approval.model_package_status : model_package_status;The overall status of the model package.
*)creation_time : creation_time;A timestamp that shows when the model package was created.
*)model_package_description : entity_description option;A brief description of the model package.
*)model_package_arn : model_package_arn;The Amazon Resource Name (ARN) of the model package.
*)model_package_version : model_package_version option;If the model package is a versioned model, the version of the model.
*)model_package_group_name : entity_name option;If the model package is a versioned model, the model group that the versioned model belongs to.
*)model_package_name : entity_name option;The name of the model package.
*)}Provides summary information about a model package.
type nonrec model_package_summary_list = model_package_summary listtype nonrec list_model_packages_output = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of model packages, use it in the subsequent request.
*)model_package_summary_list : model_package_summary_list;An array of ModelPackageSummary objects, each of which lists a model package.
}type nonrec list_model_packages_input = {sort_order : sort_order option;The sort order for the results. The default is Ascending.
sort_by : model_package_sort_by option;The parameter by which to sort the results. The default is CreationTime.
next_token : next_token option;If the response to a previous ListModelPackages request was truncated, the response includes a NextToken. To retrieve the next set of model packages, use the token in the next request.
model_package_type : model_package_type option;A filter that returns only the model packages of the specified type. This can be one of the following values.
UNVERSIONED - List only unversioined models. This is the default value if no ModelPackageType is specified.VERSIONED - List only versioned models.BOTH - List both versioned and unversioned models.model_package_group_name : arn_or_name option;A filter that returns only model versions that belong to the specified model group.
*)model_approval_status : model_approval_status option;A filter that returns only the model packages with the specified approval status.
*)name_contains : name_contains option;A string in the model package name. This filter returns only model packages whose name contains the specified string.
*)max_results : max_results option;The maximum number of model packages to return in the response.
*)creation_time_before : creation_time option;A filter that returns only model packages created before the specified time (timestamp).
*)creation_time_after : creation_time option;A filter that returns only model packages created after the specified time (timestamp).
*)}type nonrec managed_configuration = {managed_storage_type : managed_storage_type option;The storage type of the model package.
*)}The managed configuration of a model package group.
type nonrec model_package_group_summary = {managed_configuration : managed_configuration option;The managed configuration of the model package group.
*)model_package_group_status : model_package_group_status;The status of the model group.
*)creation_time : creation_time;The time that the model group was created.
*)model_package_group_description : entity_description option;A description of the model group.
*)model_package_group_arn : model_package_group_arn;The Amazon Resource Name (ARN) of the model group.
*)model_package_group_name : entity_name;The name of the model group.
*)}Summary information about a model group.
type nonrec model_package_group_summary_list = model_package_group_summary listtype nonrec list_model_package_groups_output = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of model groups, use it in the subsequent request.
*)model_package_group_summary_list : model_package_group_summary_list;A list of summaries of the model groups in your Amazon Web Services account.
*)}type nonrec list_model_package_groups_input = {cross_account_filter_option : cross_account_filter_option option;A filter that returns either model groups shared with you or model groups in your own account. When the value is CrossAccount, the results show the resources made discoverable to you from other accounts. When the value is SameAccount or null, the results show resources from your account. The default is SameAccount.
sort_order : sort_order option;The sort order for results. The default is Ascending.
sort_by : model_package_group_sort_by option;The field to sort results by. The default is CreationTime.
next_token : next_token option;If the result of the previous ListModelPackageGroups request was truncated, the response includes a NextToken. To retrieve the next set of model groups, use the token in the next request.
name_contains : name_contains option;A string in the model group name. This filter returns only model groups whose name contains the specified string.
*)max_results : max_results option;The maximum number of results to return in the response.
*)creation_time_before : creation_time option;A filter that returns only model groups created before the specified time.
*)creation_time_after : creation_time option;A filter that returns only model groups created after the specified time.
*)}type nonrec model_metadata_summary = {framework_version : string_;The framework version of the model.
*)model : string_;The name of the model.
*)task : string_;The machine learning task of the model.
*)framework : string_;The machine learning framework of the model.
*)domain : string_;The machine learning domain of the model.
*)}A summary of the model metadata.
type nonrec model_metadata_summaries = model_metadata_summary listtype nonrec list_model_metadata_response = {next_token : next_token option;A token for getting the next set of recommendations, if there are any.
*)model_metadata_summaries : model_metadata_summaries;A structure that holds model metadata.
*)}type nonrec model_metadata_filter = {value : string256;The value to filter the model metadata.
*)name : model_metadata_filter_type;The name of the of the model to filter by.
*)}Part of the search expression. You can specify the name and value (domain, task, framework, framework version, task, and model).
type nonrec model_metadata_filters = model_metadata_filter listtype nonrec model_metadata_search_expression = {filters : model_metadata_filters option;A list of filter objects.
*)}One or more filters that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results
type nonrec list_model_metadata_request = {max_results : max_results option;The maximum number of models to return in the response.
*)next_token : next_token option;If the response to a previous ListModelMetadataResponse request was truncated, the response includes a NextToken. To retrieve the next set of model metadata, use the token in the next request.
search_expression : model_metadata_search_expression option;One or more filters that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. Specify the Framework, FrameworkVersion, Domain or Task to filter supported. Filter names and values are case-sensitive.
*)}type nonrec list_model_explainability_job_definitions_response = {next_token : next_token option;The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
*)job_definition_summaries : monitoring_job_definition_summary_list;A JSON array in which each element is a summary for a explainability bias jobs.
*)}type nonrec list_model_explainability_job_definitions_request = {creation_time_after : timestamp option;A filter that returns only model explainability jobs created after a specified time.
*)creation_time_before : timestamp option;A filter that returns only model explainability jobs created before a specified time.
*)name_contains : name_contains option;Filter for model explainability jobs whose name contains a specified string.
*)max_results : max_results option;The maximum number of jobs to return in the response. The default value is 10.
*)next_token : next_token option;The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
*)sort_order : sort_order option;Whether to sort the results in Ascending or Descending order. The default is Descending.
sort_by : monitoring_job_definition_sort_key option;Whether to sort results by the Name or CreationTime field. The default is CreationTime.
endpoint_name : endpoint_name option;Name of the endpoint to monitor for model explainability.
*)}type nonrec model_card_version_summary = {last_modified_time : timestamp option;The time date and time that the model card version was last modified.
*)creation_time : timestamp;The date and time that the model card version was created.
*)model_card_version : integer;A version of the model card.
*)model_card_status : model_card_status;The approval status of the model card version within your organization. Different organizations might have different criteria for model card review and approval.
Draft: The model card is a work in progress.PendingReview: The model card is pending review.Approved: The model card is approved.Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.model_card_arn : model_card_arn;The Amazon Resource Name (ARN) of the model card.
*)model_card_name : entity_name;The name of the model card.
*)}A summary of a specific version of the model card.
type nonrec model_card_version_summary_list = model_card_version_summary listtype nonrec list_model_card_versions_response = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of model card versions, use it in the subsequent request.
*)model_card_version_summary_list : model_card_version_summary_list;The summaries of the listed versions of the model card.
*)}type nonrec list_model_card_versions_request = {sort_order : model_card_sort_order option;Sort model card versions by ascending or descending order.
*)sort_by : model_card_version_sort_by option;Sort listed model card versions by version. Sorts by version by default.
*)next_token : next_token option;If the response to a previous ListModelCardVersions request was truncated, the response includes a NextToken. To retrieve the next set of model card versions, use the token in the next request.
model_card_status : model_card_status option;Only list model card versions with the specified approval status.
*)model_card_name : model_card_name_or_arn;List model card versions for the model card with the specified name or Amazon Resource Name (ARN).
*)max_results : max_results option;The maximum number of model card versions to list.
*)creation_time_before : timestamp option;Only list model card versions that were created before the time specified.
*)creation_time_after : timestamp option;Only list model card versions that were created after the time specified.
*)}type nonrec model_card_summary = {last_modified_time : timestamp option;The date and time that the model card was last modified.
*)creation_time : timestamp;The date and time that the model card was created.
*)model_card_status : model_card_status;The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
Draft: The model card is a work in progress.PendingReview: The model card is pending review.Approved: The model card is approved.Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.model_card_arn : model_card_arn;The Amazon Resource Name (ARN) of the model card.
*)model_card_name : entity_name;The name of the model card.
*)}A summary of the model card.
type nonrec model_card_summary_list = model_card_summary listtype nonrec list_model_cards_response = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of model cards, use it in the subsequent request.
*)model_card_summaries : model_card_summary_list;The summaries of the listed model cards.
*)}type nonrec list_model_cards_request = {sort_order : model_card_sort_order option;Sort model cards by ascending or descending order.
*)sort_by : model_card_sort_by option;Sort model cards by either name or creation time. Sorts by creation time by default.
*)next_token : next_token option;If the response to a previous ListModelCards request was truncated, the response includes a NextToken. To retrieve the next set of model cards, use the token in the next request.
model_card_status : model_card_status option;Only list model cards with the specified approval status.
*)name_contains : entity_name option;Only list model cards with names that contain the specified string.
*)max_results : max_results option;The maximum number of model cards to list.
*)creation_time_before : timestamp option;Only list model cards that were created before the time specified.
*)creation_time_after : timestamp option;Only list model cards that were created after the time specified.
*)}type nonrec model_card_export_job_summary = {last_modified_at : timestamp;The date and time that the model card export job was last modified..
*)created_at : timestamp;The date and time that the model card export job was created.
*)model_card_version : integer;The version of the model card that the export job exports.
*)model_card_name : entity_name;The name of the model card that the export job exports.
*)status : model_card_export_job_status;The completion status of the model card export job.
*)model_card_export_job_arn : model_card_export_job_arn;The Amazon Resource Name (ARN) of the model card export job.
*)model_card_export_job_name : entity_name;The name of the model card export job.
*)}The summary of the Amazon SageMaker Model Card export job.
type nonrec model_card_export_job_summary_list =
model_card_export_job_summary listtype nonrec list_model_card_export_jobs_response = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of model card export jobs, use it in the subsequent request.
*)model_card_export_job_summaries : model_card_export_job_summary_list;The summaries of the listed model card export jobs.
*)}Attribute by which to sort returned export jobs.
type nonrec list_model_card_export_jobs_request = {max_results : max_results option;The maximum number of model card export jobs to list.
*)next_token : next_token option;If the response to a previous ListModelCardExportJobs request was truncated, the response includes a NextToken. To retrieve the next set of model card export jobs, use the token in the next request.
sort_order : model_card_export_job_sort_order option;Sort model card export jobs by ascending or descending order.
*)sort_by : model_card_export_job_sort_by option;Sort model card export jobs by either name or creation time. Sorts by creation time by default.
*)status_equals : model_card_export_job_status option;Only list model card export jobs with the specified status.
*)model_card_export_job_name_contains : entity_name option;Only list model card export jobs with names that contain the specified string.
*)creation_time_before : timestamp option;Only list model card export jobs that were created before the time specified.
*)creation_time_after : timestamp option;Only list model card export jobs that were created after the time specified.
*)model_card_version : integer option;List export jobs for the model card with the specified version.
*)model_card_name : entity_name;List export jobs for the model card with the specified name.
*)}type nonrec list_model_bias_job_definitions_response = {next_token : next_token option;The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
*)job_definition_summaries : monitoring_job_definition_summary_list;A JSON array in which each element is a summary for a model bias jobs.
*)}type nonrec list_model_bias_job_definitions_request = {creation_time_after : timestamp option;A filter that returns only model bias jobs created after a specified time.
*)creation_time_before : timestamp option;A filter that returns only model bias jobs created before a specified time.
*)name_contains : name_contains option;Filter for model bias jobs whose name contains a specified string.
*)max_results : max_results option;The maximum number of model bias jobs to return in the response. The default value is 10.
*)next_token : next_token option;The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
*)sort_order : sort_order option;Whether to sort the results in Ascending or Descending order. The default is Descending.
sort_by : monitoring_job_definition_sort_key option;Whether to sort results by the Name or CreationTime field. The default is CreationTime.
endpoint_name : endpoint_name option;Name of the endpoint to monitor for model bias.
*)}type nonrec list_mlflow_tracking_servers_response = {next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)tracking_server_summaries : tracking_server_summary_list option;A list of tracking servers according to chosen filters.
*)}type nonrec list_mlflow_tracking_servers_request = {max_results : max_results option;The maximum number of tracking servers to list.
*)next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)sort_order : sort_order option;Change the order of the listed tracking servers. By default, tracking servers are listed in Descending order by creation time. To change the list order, you can specify SortOrder to be Ascending.
sort_by : sort_tracking_server_by option;Filter for trackings servers sorting by name, creation time, or creation status.
*)mlflow_version : mlflow_version option;Filter for tracking servers using the specified MLflow version.
*)tracking_server_status : tracking_server_status option;Filter for tracking servers with a specified creation status.
*)created_before : timestamp option;Use the CreatedBefore filter to only list tracking servers created before a specific date and time. Listed tracking servers are shown with a date and time such as "2024-03-16T01:46:56+00:00". The CreatedBefore parameter takes in a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.
created_after : timestamp option;Use the CreatedAfter filter to only list tracking servers created after a specific date and time. Listed tracking servers are shown with a date and time such as "2024-03-16T01:46:56+00:00". The CreatedAfter parameter takes in a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.
}type nonrec mlflow_app_summary = {mlflow_version : mlflow_version option;The version of a listed MLflow App.
*)last_modified_time : timestamp option;The last modified time of a listed MLflow App.
*)creation_time : timestamp option;The creation time of a listed MLflow App.
*)status : mlflow_app_status option;The status of the MLflow App.
*)name : mlflow_app_name option;The name of the MLflow App.
*)arn : mlflow_app_arn option;The ARN of a listed MLflow App.
*)}The summary of the Mlflow App to list.
type nonrec mlflow_app_summaries = mlflow_app_summary listtype nonrec list_mlflow_apps_response = {next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)summaries : mlflow_app_summaries option;A list of MLflow Apps according to chosen filters.
*)}type nonrec list_mlflow_apps_request = {max_results : max_results option;The maximum number of MLflow Apps to list.
*)next_token : next_token option;If the previous response was truncated, use this token in your next request to receive the next set of results.
*)sort_order : sort_order option;Change the order of the listed MLflow Apps. By default, MLflow Apps are listed in Descending order by creation time. To change the list order, specify SortOrder to be Ascending.
sort_by : sort_mlflow_app_by option;Filter for MLflow Apps sorting by name, creation time, or creation status.
*)account_default_status : account_default_status option;Filter for MLflow Apps with the specified AccountDefaultStatus.
default_for_domain_id : string_ option;Filter for MLflow Apps with the specified default SageMaker Domain ID.
*)mlflow_version : mlflow_version option;Filter for Mlflow Apps with the specified version.
*)status : mlflow_app_status option;Filter for Mlflow apps with a specific creation status.
*)created_before : timestamp option;Use the CreatedBefore filter to only list MLflow Apps created before a specific date and time. Listed MLflow Apps are shown with a date and time such as "2024-03-16T01:46:56+00:00". The CreatedAfter parameter takes in a Unix timestamp.
created_after : timestamp option;Use the CreatedAfter filter to only list MLflow Apps created after a specific date and time. Listed MLflow Apps are shown with a date and time such as "2024-03-16T01:46:56+00:00". The CreatedAfter parameter takes in a Unix timestamp.
}type nonrec lineage_group_summary = {last_modified_time : timestamp option;The last modified time of the lineage group summary.
*)creation_time : timestamp option;The creation time of the lineage group summary.
*)display_name : experiment_entity_name option;The display name of the lineage group summary.
*)lineage_group_name : experiment_entity_name option;The name or Amazon Resource Name (ARN) of the lineage group.
*)lineage_group_arn : lineage_group_arn option;The Amazon Resource Name (ARN) of the lineage group resource.
*)}Lists a summary of the properties of a lineage group. A lineage group provides a group of shareable lineage entity resources.
type nonrec lineage_group_summaries = lineage_group_summary listtype nonrec list_lineage_groups_response = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.
*)lineage_group_summaries : lineage_group_summaries option;A list of lineage groups and their properties.
*)}type nonrec list_lineage_groups_request = {max_results : max_results option;The maximum number of endpoints to return in the response. This value defaults to 10.
*)next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.
*)sort_order : sort_order option;The sort order for the results. The default is Ascending.
sort_by : sort_lineage_groups_by option;The parameter by which to sort the results. The default is CreationTime.
created_before : timestamp option;A timestamp to filter against lineage groups created before a certain point in time.
*)created_after : timestamp option;A timestamp to filter against lineage groups created after a certain point in time.
*)}type nonrec label_counters_for_workteam = {total : label_counter option;The total number of tasks in the labeling job.
*)pending_human : label_counter option;The total number of data objects that need to be labeled by a human worker.
*)human_labeled : label_counter option;The total number of data objects labeled by a human worker.
*)}Provides counts for human-labeled tasks in the labeling job.
type nonrec labeling_job_for_workteam_summary = {number_of_human_workers_per_data_object : number_of_human_workers_per_data_object
option;The configured number of workers per data object.
*)label_counters : label_counters_for_workteam option;Provides information about the progress of a labeling job.
*)creation_time : timestamp;The date and time that the labeling job was created.
*)work_requester_account_id : account_id;The Amazon Web Services account ID of the account used to start the labeling job.
*)job_reference_code : job_reference_code;A unique identifier for a labeling job. You can use this to refer to a specific labeling job.
*)labeling_job_name : labeling_job_name option;The name of the labeling job that the work team is assigned to.
*)}Provides summary information for a work team.
type nonrec labeling_job_for_workteam_summary_list =
labeling_job_for_workteam_summary listtype nonrec list_labeling_jobs_for_workteam_response = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.
*)labeling_job_summary_list : labeling_job_for_workteam_summary_list;An array of LabelingJobSummary objects, each describing a labeling job.
}type nonrec list_labeling_jobs_for_workteam_request = {sort_order : sort_order option;The sort order for results. The default is Ascending.
sort_by : list_labeling_jobs_for_workteam_sort_by_options option;The field to sort results by. The default is CreationTime.
job_reference_code_contains : job_reference_code_contains option;A filter the limits jobs to only the ones whose job reference code contains the specified string.
*)creation_time_before : timestamp option;A filter that returns only labeling jobs created before the specified time (timestamp).
*)creation_time_after : timestamp option;A filter that returns only labeling jobs created after the specified time (timestamp).
*)next_token : next_token option;If the result of the previous ListLabelingJobsForWorkteam request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
max_results : max_results option;The maximum number of labeling jobs to return in each page of the response.
*)workteam_arn : workteam_arn;The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.
*)}type nonrec label_counters = {unlabeled : label_counter option;The total number of objects not yet labeled.
*)failed_non_retryable_error : label_counter option;The total number of objects that could not be labeled due to an error.
*)machine_labeled : label_counter option;The total number of objects labeled by automated data labeling.
*)human_labeled : label_counter option;The total number of objects labeled by a human worker.
*)total_labeled : label_counter option;The total number of objects labeled.
*)}Provides a breakdown of the number of objects labeled.
type nonrec labeling_job_output = {final_active_learning_model_arn : model_arn option;The Amazon Resource Name (ARN) for the most recent SageMaker model trained as part of automated data labeling.
*)output_dataset_s3_uri : s3_uri;The Amazon S3 bucket location of the manifest file for labeled data.
*)}Specifies the location of the output produced by the labeling job.
type nonrec labeling_job_s3_data_source = {manifest_s3_uri : s3_uri;The Amazon S3 location of the manifest file that describes the input data objects.
The input manifest file referenced in ManifestS3Uri must contain one of the following keys: source-ref or source. The value of the keys are interpreted as follows:
source-ref: The source of the object is the Amazon S3 object specified in the value. Use this value when the object is a binary object, such as an image.source: The source of the object is the value. Use this value when the object is a text value.If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File in the Amazon SageMaker Developer Guide to learn how to create an input manifest file.
*)}The Amazon S3 location of the input data objects.
type nonrec labeling_job_sns_data_source = {sns_topic_arn : sns_topic_arn;The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic you will use to send new data objects to a streaming labeling job.
*)}An Amazon SNS data source used for streaming labeling jobs.
type nonrec labeling_job_data_source = {sns_data_source : labeling_job_sns_data_source option;An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job.
*)s3_data_source : labeling_job_s3_data_source option;The Amazon S3 location of the input data objects.
*)}Provides information about the location of input data.
You must specify at least one of the following: S3DataSource or SnsDataSource.
Use SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job.
Use S3DataSource to specify an input manifest file for both streaming and one-time labeling jobs. Adding an S3DataSource is optional if you use SnsDataSource to create a streaming labeling job.
type nonrec content_classifiers = content_classifier listtype nonrec labeling_job_data_attributes = {content_classifiers : content_classifiers option;Declares that your content is free of personally identifiable information or adult content. SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.
*)}Attributes of the data specified by the customer. Use these to describe the data to be labeled.
type nonrec labeling_job_input_config = {data_attributes : labeling_job_data_attributes option;Attributes of the data specified by the customer.
*)data_source : labeling_job_data_source;The location of the input data.
*)}Input configuration information for a labeling job.
type nonrec labeling_job_summary = {input_config : labeling_job_input_config option;Input configuration for the labeling job.
*)labeling_job_output : labeling_job_output option;The location of the output produced by the labeling job.
*)failure_reason : failure_reason option;If the LabelingJobStatus field is Failed, this field contains a description of the error.
annotation_consolidation_lambda_arn : lambda_function_arn option;The Amazon Resource Name (ARN) of the Lambda function used to consolidate the annotations from individual workers into a label for a data object. For more information, see Annotation Consolidation.
*)pre_human_task_lambda_arn : lambda_function_arn option;The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker.
*)workteam_arn : workteam_arn;The Amazon Resource Name (ARN) of the work team assigned to the job.
*)label_counters : label_counters;Counts showing the progress of the labeling job.
*)labeling_job_status : labeling_job_status;The current status of the labeling job.
*)last_modified_time : timestamp;The date and time that the job was last modified (timestamp).
*)creation_time : timestamp;The date and time that the job was created (timestamp).
*)labeling_job_arn : labeling_job_arn;The Amazon Resource Name (ARN) assigned to the labeling job when it was created.
*)labeling_job_name : labeling_job_name;The name of the labeling job.
*)}Provides summary information about a labeling job.
type nonrec labeling_job_summary_list = labeling_job_summary listtype nonrec list_labeling_jobs_response = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.
*)labeling_job_summary_list : labeling_job_summary_list option;An array of LabelingJobSummary objects, each describing a labeling job.
}type nonrec list_labeling_jobs_request = {status_equals : labeling_job_status option;A filter that retrieves only labeling jobs with a specific status.
*)sort_order : sort_order option;The sort order for results. The default is Ascending.
sort_by : sort_by option;The field to sort results by. The default is CreationTime.
name_contains : name_contains option;A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string.
*)next_token : next_token option;If the result of the previous ListLabelingJobs request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
max_results : max_results option;The maximum number of labeling jobs to return in each page of the response.
*)last_modified_time_before : timestamp option;A filter that returns only labeling jobs modified before the specified time (timestamp).
*)last_modified_time_after : timestamp option;A filter that returns only labeling jobs modified after the specified time (timestamp).
*)creation_time_before : timestamp option;A filter that returns only labeling jobs created before the specified time (timestamp).
*)creation_time_after : timestamp option;A filter that returns only labeling jobs created after the specified time (timestamp).
*)}type nonrec job_config_schema_version_summary = {job_config_schema_version : job_schema_version;The version of the job configuration schema.
*)}Provides summary information about a job configuration schema version.
type nonrec job_config_schemas = job_config_schema_version_summary listtype nonrec list_job_schema_versions_response = {job_config_schemas : job_config_schemas;An array of JobConfigSchemaVersionSummary objects listing the available schema versions.
next_token : next_token option;If the response is truncated, this token retrieves the next set of results.
*)}type nonrec list_job_schema_versions_request = {max_results : max_results option;The maximum number of schema versions to return in the response. The default value is 5.
*)next_token : next_token option;If the previous response was truncated, this token retrieves the next set of results.
*)job_category : job_category;The category of job schemas to list.
*)}type nonrec job_summary = {end_time : timestamp option;The date and time that the job ended.
*)last_modified_time : timestamp;The date and time that the job was last modified.
*)creation_time : timestamp;The date and time that the job was created.
*)job_secondary_status : job_secondary_status;The secondary status of the job, providing more granular information about the job's progress. Secondary statuses may change between releases.
*)job_status : job_status;The current status of the job.
*)job_category : job_category;The category of the job.
*)job_name : job_name;The name of the job.
*)job_arn : job_arn;The Amazon Resource Name (ARN) of the job.
*)}Provides summary information about a job, returned by the ListJobs operation. Use DescribeJob to get full details for a specific job.
type nonrec job_summaries = job_summary listtype nonrec list_jobs_response = {job_summaries : job_summaries;An array of JobSummary objects that provide summary information about the jobs.
next_token : next_token option;If the response is truncated, this token retrieves the next set of results.
*)}type nonrec list_jobs_request = {status_equals : job_status option;A filter that returns only jobs with the specified status.
*)sort_order : sort_order option;The sort order for results. Valid values are Ascending and Descending.
sort_by : sort_by option;The field to sort results by.
*)name_contains : name_contains option;A string in the job name to filter results. Only jobs whose name contains the specified string are returned.
*)last_modified_time_before : timestamp option;A filter that returns only jobs modified before the specified time.
*)last_modified_time_after : timestamp option;A filter that returns only jobs modified after the specified time.
*)creation_time_before : timestamp option;A filter that returns only jobs created before the specified time.
*)creation_time_after : timestamp option;A filter that returns only jobs created after the specified time.
*)max_results : max_results option;The maximum number of jobs to return in the response. The default value is 50.
*)next_token : next_token option;If the previous response was truncated, this token retrieves the next set of results.
*)job_category : job_category;The category of jobs to list.
*)}type nonrec recommendation_metrics = {model_setup_time : model_setup_time option;The time it takes to launch new compute resources for a serverless endpoint. The time can vary depending on the model size, how long it takes to download the model, and the start-up time of the container.
NaN indicates that the value is not available.
memory_utilization : utilization_metric option;The expected memory utilization at maximum invocations per minute for the instance.
NaN indicates that the value is not available.
cpu_utilization : utilization_metric option;The expected CPU utilization at maximum invocations per minute for the instance.
NaN indicates that the value is not available.
model_latency : integer option;The expected model latency at maximum invocation per minute for the instance.
*)max_invocations : integer option;The expected maximum number of requests per minute for the instance.
*)cost_per_inference : float_ option;Defines the cost per inference for the instance .
*)cost_per_hour : float_ option;Defines the cost per hour for the instance.
*)}The metrics of recommendations.
type nonrec inference_metrics = {model_latency : integer;The expected model latency at maximum invocations per minute for the instance.
*)max_invocations : integer;The expected maximum number of requests per minute for the instance.
*)}The metrics for an existing endpoint compared in an Inference Recommender job.
type nonrec endpoint_output_configuration = {serverless_config : production_variant_serverless_config option;initial_instance_count : initial_instance_count option;The number of instances recommended to launch initially.
*)instance_type : production_variant_instance_type option;The instance type recommended by Amazon SageMaker Inference Recommender.
*)variant_name : string_;The name of the production variant (deployed model) made during a recommendation job.
*)endpoint_name : string_;The name of the endpoint made during a recommendation job.
*)}The endpoint configuration made by Inference Recommender during a recommendation job.
type nonrec environment_parameter = {value : string_;The value suggested by the Amazon SageMaker Inference Recommender.
*)value_type : string_;The value type suggested by the Amazon SageMaker Inference Recommender.
*)key : string_;The environment key suggested by the Amazon SageMaker Inference Recommender.
*)}A list of environment parameters suggested by the Amazon SageMaker Inference Recommender.
type nonrec environment_parameters = environment_parameter listtype nonrec model_configuration = {compilation_job_name : recommendation_job_compilation_job_name option;The name of the compilation job used to create the recommended model artifacts.
*)environment_parameters : environment_parameters option;Defines the environment parameters that includes key, value types, and values.
*)inference_specification_name : inference_specification_name option;The inference specification name in the model package version.
*)}Defines the model configuration. Includes the specification name and environment parameters.
type nonrec recommendation_job_inference_benchmark = {invocation_start_time : invocation_start_time option;A timestamp that shows when the benchmark started.
*)invocation_end_time : invocation_end_time option;A timestamp that shows when the benchmark completed.
*)failure_reason : recommendation_failure_reason option;The reason why a benchmark failed.
*)model_configuration : model_configuration;endpoint_configuration : endpoint_output_configuration option;endpoint_metrics : inference_metrics option;metrics : recommendation_metrics option;}The details for a specific benchmark from an Inference Recommender job.
type nonrec inference_recommendations_job_step = {inference_benchmark : recommendation_job_inference_benchmark option;The details for a specific benchmark.
*)status : recommendation_job_status;The current status of the benchmark.
*)job_name : recommendation_job_name;The name of the Inference Recommender job.
*)step_type : recommendation_step_type;The type of the subtask.
BENCHMARK: Evaluate the performance of your model on different instance types.
}A returned array object for the Steps response field in the ListInferenceRecommendationsJobSteps API command.
type nonrec inference_recommendations_job_steps =
inference_recommendations_job_step listtype nonrec list_inference_recommendations_job_steps_response = {next_token : next_token option;A token that you can specify in your next request to return more results from the list.
*)steps : inference_recommendations_job_steps option;A list of all subtask details in Inference Recommender.
*)}type nonrec list_inference_recommendations_job_steps_request = {next_token : next_token option;A token that you can specify to return more results from the list. Specify this field if you have a token that was returned from a previous request.
*)max_results : max_results option;The maximum number of results to return.
*)step_type : recommendation_step_type option;A filter to return details about the specified type of subtask.
BENCHMARK: Evaluate the performance of your model on different instance types.
status : recommendation_job_status option;A filter to return benchmarks of a specified status. If this field is left empty, then all benchmarks are returned.
*)job_name : recommendation_job_name;The name for the Inference Recommender job.
*)}type nonrec inference_recommendations_job = {model_package_version_arn : model_package_arn option;The Amazon Resource Name (ARN) of a versioned model package.
*)sample_payload_url : s3_uri option;The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
*)model_name : model_name option;The name of the created model.
*)failure_reason : failure_reason option;If the job fails, provides information why the job failed.
*)last_modified_time : last_modified_time;A timestamp that shows when the job was last modified.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
*)completion_time : timestamp option;A timestamp that shows when the job completed.
*)creation_time : creation_time;A timestamp that shows when the job was created.
*)status : recommendation_job_status;The status of the job.
*)job_arn : recommendation_job_arn;The Amazon Resource Name (ARN) of the recommendation job.
*)job_type : recommendation_job_type;The recommendation job type.
*)job_description : recommendation_job_description;The job description.
*)job_name : recommendation_job_name;The name of the job.
*)}A structure that contains a list of recommendation jobs.
type nonrec inference_recommendations_jobs = inference_recommendations_job listtype nonrec list_inference_recommendations_jobs_response = {next_token : next_token option;A token for getting the next set of recommendations, if there are any.
*)inference_recommendations_jobs : inference_recommendations_jobs;The recommendations created from the Amazon SageMaker Inference Recommender job.
*)}type nonrec list_inference_recommendations_jobs_request = {model_package_version_arn_equals : model_package_arn option;A filter that returns only jobs that were created for this versioned model package.
*)model_name_equals : model_name option;A filter that returns only jobs that were created for this model.
*)max_results : max_results option;The maximum number of recommendations to return in the response.
*)next_token : next_token option;If the response to a previous ListInferenceRecommendationsJobsRequest request was truncated, the response includes a NextToken. To retrieve the next set of recommendations, use the token in the next request.
sort_order : sort_order option;The sort order for the results.
*)sort_by : list_inference_recommendations_jobs_sort_by option;The parameter by which to sort the results.
*)status_equals : recommendation_job_status option;A filter that retrieves only inference recommendations jobs with a specific status.
*)name_contains : name_contains option;A string in the job name. This filter returns only recommendations whose name contains the specified string.
*)last_modified_time_before : last_modified_time option;A filter that returns only jobs that were last modified before the specified time (timestamp).
*)last_modified_time_after : last_modified_time option;A filter that returns only jobs that were last modified after the specified time (timestamp).
*)creation_time_before : creation_time option;A filter that returns only jobs created before the specified time (timestamp).
*)creation_time_after : creation_time option;A filter that returns only jobs created after the specified time (timestamp).
*)}type nonrec inference_experiment_summary = {role_arn : role_arn option;The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
*)last_modified_time : timestamp;The timestamp when you last modified the inference experiment.
*)completion_time : timestamp option;The timestamp at which the inference experiment was completed.
*)creation_time : timestamp;The timestamp at which the inference experiment was created.
*)description : inference_experiment_description option;The description of the inference experiment.
*)status_reason : inference_experiment_status_reason option;The error message for the inference experiment status result.
*)status : inference_experiment_status;The status of the inference experiment.
*)schedule : inference_experiment_schedule option;The duration for which the inference experiment ran or will run.
The maximum duration that you can set for an inference experiment is 30 days.
*)type_ : inference_experiment_type;The type of the inference experiment.
*)name : inference_experiment_name;The name of the inference experiment.
*)}Lists a summary of properties of an inference experiment.
type nonrec inference_experiment_list = inference_experiment_summary listtype nonrec list_inference_experiments_response = {next_token : next_token option;The token to use when calling the next page of results.
*)inference_experiments : inference_experiment_list option;List of inference experiments.
*)}type nonrec list_inference_experiments_request = {max_results : max_results option;The maximum number of results to select.
*)next_token : next_token option;The response from the last list when returning a list large enough to need tokening.
*)sort_order : sort_order option;The direction of sorting (ascending or descending).
*)sort_by : sort_inference_experiments_by option;The column by which to sort the listed inference experiments.
*)last_modified_time_before : timestamp option;Selects inference experiments which were last modified before this timestamp.
*)last_modified_time_after : timestamp option;Selects inference experiments which were last modified after this timestamp.
*)creation_time_before : timestamp option;Selects inference experiments which were created before this timestamp.
*)creation_time_after : timestamp option;Selects inference experiments which were created after this timestamp.
*)status_equals : inference_experiment_status option;Selects inference experiments which are in this status. For the possible statuses, see DescribeInferenceExperiment.
*)type_ : inference_experiment_type option;Selects inference experiments of this type. For the possible types of inference experiments, see CreateInferenceExperiment.
*)name_contains : name_contains option;Selects inference experiments whose names contain this name.
*)}type nonrec inference_component_summary = {last_modified_time : timestamp;The time when the inference component was last updated.
*)inference_component_status : inference_component_status option;The status of the inference component.
*)variant_name : variant_name;The name of the production variant that hosts the inference component.
*)endpoint_name : endpoint_name;The name of the endpoint that hosts the inference component.
*)endpoint_arn : endpoint_arn;The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.
*)inference_component_name : inference_component_name;The name of the inference component.
*)inference_component_arn : inference_component_arn;The Amazon Resource Name (ARN) of the inference component.
*)creation_time : timestamp;The time when the inference component was created.
*)}A summary of the properties of an inference component.
type nonrec inference_component_summary_list = inference_component_summary listtype nonrec list_inference_components_output = {next_token : pagination_token option;The token to use in a subsequent request to get the next set of results following a truncated response.
*)inference_components : inference_component_summary_list;A list of inference components and their properties that matches any of the filters you specified in the request.
*)}type nonrec list_inference_components_input = {variant_name_equals : variant_name option;A production variant name to filter the listed inference components. The response includes only those inference components that are hosted at the specified variant.
*)endpoint_name_equals : endpoint_name option;An endpoint name to filter the listed inference components. The response includes only those inference components that are hosted at the specified endpoint.
*)status_equals : inference_component_status option;Filters the results to only those inference components with the specified status.
*)last_modified_time_after : timestamp option;Filters the results to only those inference components that were updated after the specified time.
*)last_modified_time_before : timestamp option;Filters the results to only those inference components that were updated before the specified time.
*)creation_time_after : timestamp option;Filters the results to only those inference components that were created after the specified time.
*)creation_time_before : timestamp option;Filters the results to only those inference components that were created before the specified time.
*)name_contains : inference_component_name_contains option;Filters the results to only those inference components with a name that contains the specified string.
*)max_results : max_results option;The maximum number of inference components to return in the response. This value defaults to 10.
*)next_token : pagination_token option;A token that you use to get the next set of results following a truncated response. If the response to the previous request was truncated, that response provides the value for this token.
*)sort_order : order_key option;The sort order for results. The default is Descending.
sort_by : inference_component_sort_key option;The field by which to sort the inference components in the response. The default is CreationTime.
}type nonrec image_version = {version : image_version_number;The version number.
*)last_modified_time : timestamp;When the version was last modified.
*)image_version_status : image_version_status;The status of the version.
*)image_version_arn : image_version_arn;The ARN of the version.
*)image_arn : image_arn;The ARN of the image the version is based on.
*)failure_reason : failure_reason option;When a create or delete operation fails, the reason for the failure.
*)creation_time : timestamp;When the version was created.
*)}A version of a SageMaker AI Image. A version represents an existing container image.
type nonrec image_versions = image_version listtype nonrec list_image_versions_response = {next_token : next_token option;A token for getting the next set of versions, if there are any.
*)image_versions : image_versions option;A list of versions and their properties.
*)}type nonrec list_image_versions_request = {sort_order : image_version_sort_order option;The sort order. The default value is DESCENDING.
sort_by : image_version_sort_by option;The property used to sort results. The default value is CREATION_TIME.
next_token : next_token option;If the previous call to ListImageVersions didn't return the full set of versions, the call returns a token for getting the next set of versions.
max_results : max_results option;The maximum number of versions to return in the response. The default value is 10.
*)last_modified_time_before : timestamp option;A filter that returns only versions modified on or before the specified time.
*)last_modified_time_after : timestamp option;A filter that returns only versions modified on or after the specified time.
*)image_name : image_name;The name of the image to list the versions of.
*)creation_time_before : timestamp option;A filter that returns only versions created on or before the specified time.
*)creation_time_after : timestamp option;A filter that returns only versions created on or after the specified time.
*)}type nonrec image = {last_modified_time : timestamp;When the image was last modified.
*)image_status : image_status;The status of the image.
*)image_name : image_name;The name of the image.
*)image_arn : image_arn;The ARN of the image.
*)failure_reason : failure_reason option;When a create, update, or delete operation fails, the reason for the failure.
*)display_name : image_display_name option;The name of the image as displayed.
*)description : image_description option;The description of the image.
*)creation_time : timestamp;When the image was created.
*)}A SageMaker AI image. A SageMaker AI image represents a set of container images that are derived from a common base container image. Each of these container images is represented by a SageMaker AI ImageVersion.
type nonrec images = image listtype nonrec list_images_response = {next_token : next_token option;A token for getting the next set of images, if there are any.
*)images : images option;A list of images and their properties.
*)}type nonrec list_images_request = {sort_order : image_sort_order option;The sort order. The default value is DESCENDING.
sort_by : image_sort_by option;The property used to sort results. The default value is CREATION_TIME.
next_token : next_token option;If the previous call to ListImages didn't return the full set of images, the call returns a token for getting the next set of images.
name_contains : image_name_contains option;A filter that returns only images whose name contains the specified string.
*)max_results : max_results option;The maximum number of images to return in the response. The default value is 10.
*)last_modified_time_before : timestamp option;A filter that returns only images modified on or before the specified time.
*)last_modified_time_after : timestamp option;A filter that returns only images modified on or after the specified time.
*)creation_time_before : timestamp option;A filter that returns only images created on or before the specified time.
*)creation_time_after : timestamp option;A filter that returns only images created on or after the specified time.
*)}type nonrec hyper_parameter_tuning_job_summary = {resource_limits : resource_limits option;The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.
*)objective_status_counters : objective_status_counters;The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.
*)training_job_status_counters : training_job_status_counters;The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.
*)last_modified_time : timestamp option;The date and time that the tuning job was modified.
*)hyper_parameter_tuning_end_time : timestamp option;The date and time that the tuning job ended.
*)creation_time : timestamp;The date and time that the tuning job was created.
*)strategy : hyper_parameter_tuning_job_strategy_type;Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to evaluate at each iteration.
*)hyper_parameter_tuning_job_status : hyper_parameter_tuning_job_status;The status of the tuning job.
*)hyper_parameter_tuning_job_arn : hyper_parameter_tuning_job_arn;The Amazon Resource Name (ARN) of the tuning job.
*)hyper_parameter_tuning_job_name : hyper_parameter_tuning_job_name;The name of the tuning job.
*)}Provides summary information about a hyperparameter tuning job.
type nonrec hyper_parameter_tuning_job_summaries =
hyper_parameter_tuning_job_summary listtype nonrec list_hyper_parameter_tuning_jobs_response = {next_token : next_token option;If the result of this ListHyperParameterTuningJobs request was truncated, the response includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.
hyper_parameter_tuning_job_summaries : hyper_parameter_tuning_job_summaries;A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the ListHyperParameterTuningJobs request returned.
}type nonrec list_hyper_parameter_tuning_jobs_request = {status_equals : hyper_parameter_tuning_job_status option;A filter that returns only tuning jobs with the specified status.
*)last_modified_time_before : timestamp option;A filter that returns only tuning jobs that were modified before the specified time.
*)last_modified_time_after : timestamp option;A filter that returns only tuning jobs that were modified after the specified time.
*)creation_time_before : timestamp option;A filter that returns only tuning jobs that were created before the specified time.
*)creation_time_after : timestamp option;A filter that returns only tuning jobs that were created after the specified time.
*)name_contains : name_contains option;A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.
*)sort_order : sort_order option;The sort order for results. The default is Ascending.
sort_by : hyper_parameter_tuning_job_sort_by_options option;The field to sort results by. The default is Name.
max_results : max_results option;The maximum number of tuning jobs to return. The default value is 10.
*)next_token : next_token option;If the result of the previous ListHyperParameterTuningJobs request was truncated, the response includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.
}type nonrec human_task_ui_summary = {creation_time : timestamp;A timestamp when SageMaker created the human task user interface.
*)human_task_ui_arn : human_task_ui_arn;The Amazon Resource Name (ARN) of the human task user interface.
*)human_task_ui_name : human_task_ui_name;The name of the human task user interface.
*)}Container for human task user interface information.
type nonrec human_task_ui_summaries = human_task_ui_summary listtype nonrec list_human_task_uis_response = {next_token : next_token option;A token to resume pagination.
*)human_task_ui_summaries : human_task_ui_summaries;An array of objects describing the human task user interfaces.
*)}type nonrec list_human_task_uis_request = {max_results : max_results option;The total number of items to return. If the total number of available items is more than the value specified in MaxResults, then a NextToken will be provided in the output that you can use to resume pagination.
next_token : next_token option;A token to resume pagination.
*)sort_order : sort_order option;An optional value that specifies whether you want the results sorted in Ascending or Descending order.
creation_time_before : timestamp option;A filter that returns only human task user interfaces that were created before the specified timestamp.
*)creation_time_after : timestamp option;A filter that returns only human task user interfaces with a creation time greater than or equal to the specified timestamp.
*)}type nonrec hub_info = {last_modified_time : timestamp;The date and time that the hub was last modified.
*)creation_time : timestamp;The date and time that the hub was created.
*)hub_status : hub_status;The status of the hub.
*)hub_search_keywords : hub_search_keyword_list option;The searchable keywords for the hub.
*)hub_description : hub_description option;A description of the hub.
*)hub_display_name : hub_display_name option;The display name of the hub.
*)hub_arn : hub_arn;The Amazon Resource Name (ARN) of the hub.
*)hub_name : hub_name;The name of the hub.
*)}Information about a hub.
type nonrec hub_info_list = hub_info listtype nonrec list_hubs_response = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of hubs, use it in the subsequent request.
*)hub_summaries : hub_info_list;The summaries of the listed hubs.
*)}type nonrec list_hubs_request = {next_token : next_token option;If the response to a previous ListHubs request was truncated, the response includes a NextToken. To retrieve the next set of hubs, use the token in the next request.
max_results : max_results option;The maximum number of hubs to list.
*)sort_order : sort_order option;Sort hubs by ascending or descending order.
*)sort_by : hub_sort_by option;Sort hubs by either name or creation time.
*)last_modified_time_after : timestamp option;Only list hubs that were last modified after the time specified.
*)last_modified_time_before : timestamp option;Only list hubs that were last modified before the time specified.
*)creation_time_after : timestamp option;Only list hubs that were created after the time specified.
*)creation_time_before : timestamp option;Only list hubs that were created before the time specified.
*)name_contains : name_contains option;Only list hubs with names that contain the specified string.
*)}type nonrec hub_content_info = {original_creation_time : timestamp option;The date and time when the hub content was originally created, before any updates or revisions.
*)creation_time : timestamp;The date and time that the hub content was created.
*)hub_content_status : hub_content_status;The status of the hub content.
*)hub_content_search_keywords : hub_content_search_keyword_list option;The searchable keywords for the hub content.
*)support_status : hub_content_support_status option;The support status of the hub content.
*)hub_content_description : hub_content_description option;A description of the hub content.
*)hub_content_display_name : hub_content_display_name option;The display name of the hub content.
*)document_schema_version : document_schema_version;The version of the hub content document schema.
*)hub_content_type : hub_content_type;The type of hub content.
*)hub_content_version : hub_content_version;The version of the hub content.
*)sage_maker_public_hub_content_arn : sage_maker_public_hub_content_arn option;The ARN of the public hub content.
*)hub_content_arn : hub_content_arn;The Amazon Resource Name (ARN) of the hub content.
*)hub_content_name : hub_content_name;The name of the hub content.
*)}Information about hub content.
type nonrec hub_content_info_list = hub_content_info listtype nonrec list_hub_content_versions_response = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of hub content versions, use it in the subsequent request.
*)hub_content_summaries : hub_content_info_list;The summaries of the listed hub content versions.
*)}type nonrec list_hub_content_versions_request = {next_token : next_token option;If the response to a previous ListHubContentVersions request was truncated, the response includes a NextToken. To retrieve the next set of hub content versions, use the token in the next request.
max_results : max_results option;The maximum number of hub content versions to list.
*)sort_order : sort_order option;Sort hub content versions by ascending or descending order.
*)sort_by : hub_content_sort_by option;Sort hub content versions by either name or creation time.
*)creation_time_after : timestamp option;Only list hub content versions that were created after the time specified.
*)creation_time_before : timestamp option;Only list hub content versions that were created before the time specified.
*)max_schema_version : document_schema_version option;The upper bound of the hub content schema version.
*)min_version : hub_content_version option;The lower bound of the hub content versions to list.
*)hub_content_name : hub_content_name;The name of the hub content.
*)hub_content_type : hub_content_type;The type of hub content to list versions of.
*)hub_name : hub_name_or_arn;The name of the hub to list the content versions of.
*)}type nonrec list_hub_contents_response = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of hub content, use it in the subsequent request.
*)hub_content_summaries : hub_content_info_list;The summaries of the listed hub content.
*)}type nonrec list_hub_contents_request = {next_token : next_token option;If the response to a previous ListHubContents request was truncated, the response includes a NextToken. To retrieve the next set of hub content, use the token in the next request.
max_results : max_results option;The maximum amount of hub content to list.
*)sort_order : sort_order option;Sort hubs by ascending or descending order.
*)sort_by : hub_content_sort_by option;Sort hub content versions by either name or creation time.
*)creation_time_after : timestamp option;Only list hub content that was created after the time specified.
*)creation_time_before : timestamp option;Only list hub content that was created before the time specified.
*)max_schema_version : document_schema_version option;The upper bound of the hub content schema verion.
*)name_contains : name_contains option;Only list hub content if the name contains the specified string.
*)hub_content_type : hub_content_type;The type of hub content to list.
*)hub_name : hub_name_or_arn;The name of the hub to list the contents of.
*)}type nonrec flow_definition_summary = {failure_reason : failure_reason option;The reason why the flow definition creation failed. A failure reason is returned only when the flow definition status is Failed.
creation_time : timestamp;The timestamp when SageMaker created the flow definition.
*)flow_definition_status : flow_definition_status;The status of the flow definition. Valid values:
*)flow_definition_arn : flow_definition_arn;The Amazon Resource Name (ARN) of the flow definition.
*)flow_definition_name : flow_definition_name;The name of the flow definition.
*)}Contains summary information about the flow definition.
type nonrec flow_definition_summaries = flow_definition_summary listtype nonrec list_flow_definitions_response = {next_token : next_token option;A token to resume pagination.
*)flow_definition_summaries : flow_definition_summaries;An array of objects describing the flow definitions.
*)}type nonrec list_flow_definitions_request = {max_results : max_results option;The total number of items to return. If the total number of available items is more than the value specified in MaxResults, then a NextToken will be provided in the output that you can use to resume pagination.
next_token : next_token option;A token to resume pagination.
*)sort_order : sort_order option;An optional value that specifies whether you want the results sorted in Ascending or Descending order.
creation_time_before : timestamp option;A filter that returns only flow definitions that were created before the specified timestamp.
*)creation_time_after : timestamp option;A filter that returns only flow definitions with a creation time greater than or equal to the specified timestamp.
*)}type nonrec feature_group_summary = {offline_store_status : offline_store_status option;Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked.
feature_group_status : feature_group_status option;The status of a FeatureGroup. The status can be any of the following: Creating, Created, CreateFail, Deleting or DetailFail.
creation_time : timestamp;A timestamp indicating the time of creation time of the FeatureGroup.
feature_group_arn : feature_group_arn;Unique identifier for the FeatureGroup.
feature_group_name : feature_group_name;The name of FeatureGroup.
}The name, ARN, CreationTime, FeatureGroup values, LastUpdatedTime and EnableOnlineStorage status of a FeatureGroup.
type nonrec feature_group_summaries = feature_group_summary listtype nonrec list_feature_groups_response = {next_token : next_token option;A token to resume pagination of ListFeatureGroups results.
feature_group_summaries : feature_group_summaries;A summary of feature groups.
*)}type nonrec list_feature_groups_request = {next_token : next_token option;A token to resume pagination of ListFeatureGroups results.
max_results : feature_group_max_results option;The maximum number of results returned by ListFeatureGroups.
sort_by : feature_group_sort_by option;The value on which the feature group list is sorted.
*)sort_order : feature_group_sort_order option;The order in which feature groups are listed.
*)creation_time_before : creation_time option;Use this parameter to search for FeatureGroupss created before a specific date and time.
creation_time_after : creation_time option;Use this parameter to search for FeatureGroupss created after a specific date and time.
offline_store_status_equals : offline_store_status_value option;An OfflineStore status. Filters by OfflineStore status.
feature_group_status_equals : feature_group_status option;A FeatureGroup status. Filters by FeatureGroup status.
name_contains : feature_group_name_contains option;A string that partially matches one or more FeatureGroups names. Filters FeatureGroups by name.
}type nonrec experiment_summary = {last_modified_time : timestamp option;When the experiment was last modified.
*)creation_time : timestamp option;When the experiment was created.
*)experiment_source : experiment_source option;display_name : experiment_entity_name option;The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
experiment_name : experiment_entity_name option;The name of the experiment.
*)experiment_arn : experiment_arn option;The Amazon Resource Name (ARN) of the experiment.
*)}A summary of the properties of an experiment. To get the complete set of properties, call the DescribeExperiment API and provide the ExperimentName.
type nonrec experiment_summaries = experiment_summary listtype nonrec list_experiments_response = {next_token : next_token option;A token for getting the next set of experiments, if there are any.
*)experiment_summaries : experiment_summaries option;A list of the summaries of your experiments.
*)}type nonrec list_experiments_request = {max_results : max_results option;The maximum number of experiments to return in the response. The default value is 10.
*)next_token : next_token option;If the previous call to ListExperiments didn't return the full set of experiments, the call returns a token for getting the next set of experiments.
sort_order : sort_order option;The sort order. The default value is Descending.
sort_by : sort_experiments_by option;The property used to sort results. The default value is CreationTime.
created_before : timestamp option;A filter that returns only experiments created before the specified time.
*)created_after : timestamp option;A filter that returns only experiments created after the specified time.
*)}type nonrec endpoint_summary = {endpoint_status : endpoint_status;The status of the endpoint.
OutOfService: Endpoint is not available to take incoming requests.Creating: CreateEndpoint is executing.Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count.RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.InService: Endpoint is available to process incoming requests.Deleting: DeleteEndpoint is executing.Failed: Endpoint could not be created, updated, or re-scaled. Use DescribeEndpointOutput$FailureReason for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.To get a list of endpoints with a specified status, use the StatusEquals filter with a call to ListEndpoints.
last_modified_time : timestamp;A timestamp that shows when the endpoint was last modified.
*)creation_time : timestamp;A timestamp that shows when the endpoint was created.
*)endpoint_arn : endpoint_arn;The Amazon Resource Name (ARN) of the endpoint.
*)endpoint_name : endpoint_name;The name of the endpoint.
*)}Provides summary information for an endpoint.
type nonrec endpoint_summary_list = endpoint_summary listtype nonrec list_endpoints_output = {next_token : pagination_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.
*)endpoints : endpoint_summary_list;An array or endpoint objects.
*)}type nonrec list_endpoints_input = {status_equals : endpoint_status option;A filter that returns only endpoints with the specified status.
*)last_modified_time_after : timestamp option;A filter that returns only endpoints that were modified after the specified timestamp.
*)last_modified_time_before : timestamp option;A filter that returns only endpoints that were modified before the specified timestamp.
*)creation_time_after : timestamp option;A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp).
*)creation_time_before : timestamp option;A filter that returns only endpoints that were created before the specified time (timestamp).
*)name_contains : endpoint_name_contains option;A string in endpoint names. This filter returns only endpoints whose name contains the specified string.
*)max_results : max_results option;The maximum number of endpoints to return in the response. This value defaults to 10.
*)next_token : pagination_token option;If the result of a ListEndpoints request was truncated, the response includes a NextToken. To retrieve the next set of endpoints, use the token in the next request.
sort_order : order_key option;The sort order for results. The default is Descending.
sort_by : endpoint_sort_key option;Sorts the list of results. The default is CreationTime.
}type nonrec endpoint_config_summary = {creation_time : timestamp;A timestamp that shows when the endpoint configuration was created.
*)endpoint_config_arn : endpoint_config_arn;The Amazon Resource Name (ARN) of the endpoint configuration.
*)endpoint_config_name : endpoint_config_name;The name of the endpoint configuration.
*)}Provides summary information for an endpoint configuration.
type nonrec endpoint_config_summary_list = endpoint_config_summary listtype nonrec list_endpoint_configs_output = {next_token : pagination_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of endpoint configurations, use it in the subsequent request
*)endpoint_configs : endpoint_config_summary_list;An array of endpoint configurations.
*)}type nonrec list_endpoint_configs_input = {creation_time_after : timestamp option;A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp).
*)creation_time_before : timestamp option;A filter that returns only endpoint configurations created before the specified time (timestamp).
*)name_contains : endpoint_config_name_contains option;A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string.
*)max_results : max_results option;The maximum number of training jobs to return in the response.
*)next_token : pagination_token option;If the result of the previous ListEndpointConfig request was truncated, the response includes a NextToken. To retrieve the next set of endpoint configurations, use the token in the next request.
sort_order : order_key option;The sort order for results. The default is Descending.
sort_by : endpoint_config_sort_key option;The field to sort results by. The default is CreationTime.
}type nonrec edge_packaging_job_summary = {last_modified_time : timestamp option;The timestamp of when the edge packaging job was last updated.
*)creation_time : timestamp option;The timestamp of when the job was created.
*)model_version : edge_version option;The version of the model.
*)model_name : entity_name option;The name of the model.
*)compilation_job_name : entity_name option;The name of the SageMaker Neo compilation job.
*)edge_packaging_job_status : edge_packaging_job_status;The status of the edge packaging job.
*)edge_packaging_job_name : entity_name;The name of the edge packaging job.
*)edge_packaging_job_arn : edge_packaging_job_arn;The Amazon Resource Name (ARN) of the edge packaging job.
*)}Summary of edge packaging job.
type nonrec edge_packaging_job_summaries = edge_packaging_job_summary listtype nonrec list_edge_packaging_jobs_response = {next_token : next_token option;Token to use when calling the next page of results.
*)edge_packaging_job_summaries : edge_packaging_job_summaries;Summaries of edge packaging jobs.
*)}type nonrec list_edge_packaging_jobs_request = {sort_order : sort_order option;What direction to sort by.
*)sort_by : list_edge_packaging_jobs_sort_by option;Use to specify what column to sort by.
*)status_equals : edge_packaging_job_status option;The job status to filter for.
*)model_name_contains : name_contains option;Filter for jobs where the model name contains this string.
*)name_contains : name_contains option;Filter for jobs containing this name in their packaging job name.
*)last_modified_time_before : timestamp option;Select jobs where the job was updated before specified time.
*)last_modified_time_after : timestamp option;Select jobs where the job was updated after specified time.
*)creation_time_before : timestamp option;Select jobs where the job was created before specified time.
*)creation_time_after : timestamp option;Select jobs where the job was created after specified time.
*)max_results : list_max_results option;Maximum number of results to select.
*)next_token : next_token option;The response from the last list when returning a list large enough to need tokening.
*)}type nonrec edge_deployment_plan_summary = {last_modified_time : timestamp option;The time when the edge deployment plan was last updated.
*)creation_time : timestamp option;The time when the edge deployment plan was created.
*)edge_deployment_failed : integer;The number of edge devices that failed the deployment.
*)edge_deployment_pending : integer;The number of edge devices yet to pick up the deployment, or in progress.
*)edge_deployment_success : integer;The number of edge devices with the successful deployment.
*)device_fleet_name : entity_name;The name of the device fleet used for the deployment.
*)edge_deployment_plan_name : entity_name;The name of the edge deployment plan.
*)edge_deployment_plan_arn : edge_deployment_plan_arn;The ARN of the edge deployment plan.
*)}Contains information summarizing an edge deployment plan.
type nonrec edge_deployment_plan_summaries = edge_deployment_plan_summary listtype nonrec list_edge_deployment_plans_response = {next_token : next_token option;The token to use when calling the next page of results.
*)edge_deployment_plan_summaries : edge_deployment_plan_summaries;List of summaries of edge deployment plans.
*)}type nonrec list_edge_deployment_plans_request = {sort_order : sort_order option;The direction of the sorting (ascending or descending).
*)sort_by : list_edge_deployment_plans_sort_by option;The column by which to sort the edge deployment plans. Can be one of NAME, DEVICEFLEETNAME, CREATIONTIME, LASTMODIFIEDTIME.
device_fleet_name_contains : name_contains option;Selects edge deployment plans with a device fleet name containing this name.
*)name_contains : name_contains option;Selects edge deployment plans with names containing this name.
*)last_modified_time_before : timestamp option;Selects edge deployment plans that were last updated before this time.
*)last_modified_time_after : timestamp option;Selects edge deployment plans that were last updated after this time.
*)creation_time_before : timestamp option;Selects edge deployment plans created before this time.
*)creation_time_after : timestamp option;Selects edge deployment plans created after this time.
*)max_results : list_max_results option;The maximum number of results to select (50 by default).
*)next_token : next_token option;The response from the last list when returning a list large enough to need tokening.
*)}type nonrec domain_details = {url : string1024 option;The domain's URL.
*)last_modified_time : last_modified_time option;The last modified time.
*)creation_time : creation_time option;The creation time.
*)status : domain_status option;The status.
*)domain_name : domain_name option;The domain name.
*)domain_id : domain_id option;The domain ID.
*)domain_arn : domain_arn option;The domain's Amazon Resource Name (ARN).
*)}The domain's details.
type nonrec domain_list = domain_details listtype nonrec list_domains_response = {next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)domains : domain_list option;The list of domains.
*)}type nonrec list_domains_request = {max_results : max_results option;This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.
next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)}type nonrec edge_model_summary = {model_version : edge_version;The version model.
*)model_name : entity_name;The name of the model.
*)}Summary of model on edge device.
type nonrec edge_model_summaries = edge_model_summary listtype nonrec device_summary = {agent_version : edge_version option;Edge Manager agent version.
*)models : edge_model_summaries option;Models on the device.
*)latest_heartbeat : timestamp option;The last heartbeat received from the device.
*)registration_time : timestamp option;The timestamp of the last registration or de-reregistration.
*)iot_thing_name : thing_name option;The Amazon Web Services Internet of Things (IoT) object thing name associated with the device..
*)device_fleet_name : entity_name option;The name of the fleet the device belongs to.
*)description : device_description option;A description of the device.
*)device_arn : device_arn;Amazon Resource Name (ARN) of the device.
*)device_name : entity_name;The unique identifier of the device.
*)}Summary of the device.
type nonrec device_summaries = device_summary listtype nonrec list_devices_response = {next_token : next_token option;The response from the last list when returning a list large enough to need tokening.
*)device_summaries : device_summaries;Summary of devices.
*)}type nonrec list_devices_request = {device_fleet_name : entity_name option;Filter for fleets containing this name in their device fleet name.
*)model_name : entity_name option;A filter that searches devices that contains this name in any of their models.
*)latest_heartbeat_after : timestamp option;Select fleets where the job was updated after X
*)max_results : list_max_results option;Maximum number of results to select.
*)next_token : next_token option;The response from the last list when returning a list large enough to need tokening.
*)}type nonrec device_fleet_summary = {last_modified_time : timestamp option;Timestamp of when the device fleet was last updated.
*)creation_time : timestamp option;Timestamp of when the device fleet was created.
*)device_fleet_name : entity_name;Name of the device fleet.
*)device_fleet_arn : device_fleet_arn;Amazon Resource Name (ARN) of the device fleet.
*)}Summary of the device fleet.
type nonrec device_fleet_summaries = device_fleet_summary listtype nonrec list_device_fleets_response = {next_token : next_token option;The response from the last list when returning a list large enough to need tokening.
*)device_fleet_summaries : device_fleet_summaries;Summary of the device fleet.
*)}type nonrec list_device_fleets_request = {sort_order : sort_order option;What direction to sort in.
*)sort_by : list_device_fleets_sort_by option;The column to sort by.
*)name_contains : name_contains option;Filter for fleets containing this name in their fleet device name.
*)last_modified_time_before : timestamp option;Select fleets where the job was updated before X
*)last_modified_time_after : timestamp option;Select fleets where the job was updated after X
*)creation_time_before : timestamp option;Filter fleets where the edge packaging job was created before specified time.
*)creation_time_after : timestamp option;Filter fleets where packaging job was created after specified time.
*)max_results : list_max_results option;The maximum number of results to select.
*)next_token : next_token option;The response from the last list when returning a list large enough to need tokening.
*)}type nonrec list_data_quality_job_definitions_response = {next_token : next_token option;If the result of the previous ListDataQualityJobDefinitions request was truncated, the response includes a NextToken. To retrieve the next set of data quality monitoring job definitions, use the token in the next request.
job_definition_summaries : monitoring_job_definition_summary_list;A list of data quality monitoring job definitions.
*)}type nonrec list_data_quality_job_definitions_request = {creation_time_after : timestamp option;A filter that returns only data quality monitoring job definitions created after the specified time.
*)creation_time_before : timestamp option;A filter that returns only data quality monitoring job definitions created before the specified time.
*)name_contains : name_contains option;A string in the data quality monitoring job definition name. This filter returns only data quality monitoring job definitions whose name contains the specified string.
*)max_results : max_results option;The maximum number of data quality monitoring job definitions to return in the response.
*)next_token : next_token option;If the result of the previous ListDataQualityJobDefinitions request was truncated, the response includes a NextToken. To retrieve the next set of transform jobs, use the token in the next request.>
sort_order : sort_order option;Whether to sort the results in Ascending or Descending order. The default is Descending.
sort_by : monitoring_job_definition_sort_key option;The field to sort results by. The default is CreationTime.
endpoint_name : endpoint_name option;A filter that lists the data quality job definitions associated with the specified endpoint.
*)}type nonrec context_source = {source_id : string256 option;The ID of the source.
*)source_type : string256 option;The type of the source.
*)source_uri : source_uri;The URI of the source.
*)}A structure describing the source of a context.
type nonrec context_summary = {last_modified_time : timestamp option;When the context was last modified.
*)creation_time : timestamp option;When the context was created.
*)context_type : string256 option;The type of the context.
*)source : context_source option;The source of the context.
*)context_name : context_name option;The name of the context.
*)context_arn : context_arn option;The Amazon Resource Name (ARN) of the context.
*)}Lists a summary of the properties of a context. A context provides a logical grouping of other entities.
type nonrec context_summaries = context_summary listtype nonrec list_contexts_response = {next_token : next_token option;A token for getting the next set of contexts, if there are any.
*)context_summaries : context_summaries option;A list of contexts and their properties.
*)}type nonrec list_contexts_request = {max_results : max_results option;The maximum number of contexts to return in the response. The default value is 10.
*)next_token : next_token option;If the previous call to ListContexts didn't return the full set of contexts, the call returns a token for getting the next set of contexts.
sort_order : sort_order option;The sort order. The default value is Descending.
sort_by : sort_contexts_by option;The property used to sort results. The default value is CreationTime.
created_before : timestamp option;A filter that returns only contexts created on or before the specified time.
*)created_after : timestamp option;A filter that returns only contexts created on or after the specified time.
*)context_type : string256 option;A filter that returns only contexts of the specified type.
*)source_uri : source_uri option;A filter that returns only contexts with the specified source URI.
*)}type nonrec compute_quota_summary = {last_modified_time : timestamp option;Last modified time of the compute allocation definition.
*)creation_time : timestamp;Creation time of the compute allocation definition.
*)activation_state : activation_state option;The state of the compute allocation being described. Use to enable or disable compute allocation.
Default is Enabled.
compute_quota_target : compute_quota_target;The target entity to allocate compute resources to.
*)compute_quota_config : compute_quota_config option;Configuration of the compute allocation definition. This includes the resource sharing option, and the setting to preempt low priority tasks.
*)cluster_arn : cluster_arn option;ARN of the cluster.
*)status : scheduler_resource_status;Status of the compute allocation definition.
*)compute_quota_version : integer option;Version of the compute allocation definition.
*)name : entity_name;Name of the compute allocation definition.
*)compute_quota_id : compute_quota_id;ID of the compute allocation definition.
*)compute_quota_arn : compute_quota_arn;ARN of the compute allocation definition.
*)}Summary of the compute allocation definition.
type nonrec compute_quota_summary_list = compute_quota_summary listtype nonrec list_compute_quotas_response = {next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)compute_quota_summaries : compute_quota_summary_list option;Summaries of the compute allocation definitions.
*)}type nonrec list_compute_quotas_request = {max_results : max_results option;The maximum number of compute allocation definitions to list.
*)next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)sort_order : sort_order option;The order of the list. By default, listed in Descending order according to by SortBy. To change the list order, you can specify SortOrder to be Ascending.
sort_by : sort_quota_by option;Filter for sorting the list by a given value. For example, sort by name, creation time, or status.
*)cluster_arn : cluster_arn option;Filter for ARN of the cluster.
*)status : scheduler_resource_status option;Filter for status.
*)name_contains : entity_name option;Filter for name containing this string.
*)created_before : timestamp option;Filter for before this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.
*)created_after : timestamp option;Filter for after this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.
*)}type nonrec compilation_job_summary = {compilation_job_status : compilation_job_status;The status of the model compilation job.
*)last_modified_time : last_modified_time option;The time when the model compilation job was last modified.
*)compilation_target_platform_accelerator : target_platform_accelerator option;The type of accelerator that the model will run on after the compilation job has completed.
*)compilation_target_platform_arch : target_platform_arch option;The type of architecture that the model will run on after the compilation job has completed.
*)compilation_target_platform_os : target_platform_os option;The type of OS that the model will run on after the compilation job has completed.
*)compilation_target_device : target_device option;The type of device that the model will run on after the compilation job has completed.
*)compilation_end_time : timestamp option;The time when the model compilation job completed.
*)compilation_start_time : timestamp option;The time when the model compilation job started.
*)creation_time : creation_time;The time when the model compilation job was created.
*)compilation_job_arn : compilation_job_arn;The Amazon Resource Name (ARN) of the model compilation job.
*)compilation_job_name : entity_name;The name of the model compilation job that you want a summary for.
*)}A summary of a model compilation job.
type nonrec compilation_job_summaries = compilation_job_summary listtype nonrec list_compilation_jobs_response = {next_token : next_token option;If the response is truncated, Amazon SageMaker AI returns this NextToken. To retrieve the next set of model compilation jobs, use this token in the next request.
compilation_job_summaries : compilation_job_summaries;}type nonrec list_compilation_jobs_request = {sort_order : sort_order option;The sort order for results. The default is Ascending.
sort_by : list_compilation_jobs_sort_by option;The field by which to sort results. The default is CreationTime.
status_equals : compilation_job_status option;A filter that retrieves model compilation jobs with a specific CompilationJobStatus status.
name_contains : name_contains option;A filter that returns the model compilation jobs whose name contains a specified string.
*)last_modified_time_before : last_modified_time option;A filter that returns the model compilation jobs that were modified before a specified time.
*)last_modified_time_after : last_modified_time option;A filter that returns the model compilation jobs that were modified after a specified time.
*)creation_time_before : creation_time option;A filter that returns the model compilation jobs that were created before a specified time.
*)creation_time_after : creation_time option;A filter that returns the model compilation jobs that were created after a specified time.
*)max_results : max_results option;The maximum number of model compilation jobs to return in the response.
*)next_token : next_token option;If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken. To retrieve the next set of model compilation jobs, use the token in the next request.
}type nonrec git_config = {secret_arn : secret_arn option;The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:
\{"username": {i UserName}, "password": {i Password}\}branch : branch option;The default branch for the Git repository.
*)repository_url : git_config_url;The URL where the Git repository is located.
*)}Specifies configuration details for a Git repository in your Amazon Web Services account.
type nonrec code_repository_summary = {git_config : git_config option;Configuration details for the Git repository, including the URL where it is located and the ARN of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.
*)last_modified_time : last_modified_time;The date and time that the Git repository was last modified.
*)creation_time : creation_time;The date and time that the Git repository was created.
*)code_repository_arn : code_repository_arn;The Amazon Resource Name (ARN) of the Git repository.
*)code_repository_name : entity_name;The name of the Git repository.
*)}Specifies summary information about a Git repository.
type nonrec code_repository_summary_list = code_repository_summary listtype nonrec list_code_repositories_output = {next_token : next_token option;If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a NextToken. To get the next set of Git repositories, use the token in the next request.
code_repository_summary_list : code_repository_summary_list;Gets a list of summaries of the Git repositories. Each summary specifies the following values for the repository:
}type nonrec list_code_repositories_input = {sort_order : code_repository_sort_order option;The sort order for results. The default is Ascending.
sort_by : code_repository_sort_by option;The field to sort results by. The default is Name.
next_token : next_token option;If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a NextToken. To get the next set of Git repositories, use the token in the next request.
name_contains : code_repository_name_contains option;A string in the Git repositories name. This filter returns only repositories whose name contains the specified string.
*)max_results : max_results option;The maximum number of Git repositories to return in the response.
*)last_modified_time_before : timestamp option;A filter that returns only Git repositories that were last modified before the specified time.
*)last_modified_time_after : timestamp option;A filter that returns only Git repositories that were last modified after the specified time.
*)creation_time_before : creation_time option;A filter that returns only Git repositories that were created before the specified time.
*)creation_time_after : creation_time option;A filter that returns only Git repositories that were created after the specified time.
*)}type nonrec cluster_scheduler_config_summary = {cluster_arn : cluster_arn option;ARN of the cluster.
*)status : scheduler_resource_status;Status of the cluster policy.
*)last_modified_time : timestamp option;Last modified time of the cluster policy.
*)creation_time : timestamp;Creation time of the cluster policy.
*)name : entity_name;Name of the cluster policy.
*)cluster_scheduler_config_version : integer option;Version of the cluster policy.
*)cluster_scheduler_config_id : cluster_scheduler_config_id;ID of the cluster policy.
*)cluster_scheduler_config_arn : cluster_scheduler_config_arn;ARN of the cluster policy.
*)}Summary of the cluster policy.
type nonrec cluster_scheduler_config_summary_list =
cluster_scheduler_config_summary listtype nonrec list_cluster_scheduler_configs_response = {next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)cluster_scheduler_config_summaries : cluster_scheduler_config_summary_list
option;Summaries of the cluster policies.
*)}type nonrec list_cluster_scheduler_configs_request = {max_results : max_results option;The maximum number of cluster policies to list.
*)next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)sort_order : sort_order option;The order of the list. By default, listed in Descending order according to by SortBy. To change the list order, you can specify SortOrder to be Ascending.
sort_by : sort_cluster_scheduler_config_by option;Filter for sorting the list by a given value. For example, sort by name, creation time, or status.
*)status : scheduler_resource_status option;Filter for status.
*)cluster_arn : cluster_arn option;Filter for ARN of the cluster.
*)name_contains : entity_name option;Filter for name containing this string.
*)created_before : timestamp option;Filter for before this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.
*)created_after : timestamp option;Filter for after this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.
*)}The status of the Amazon Machine Image (AMI) version for the HyperPod cluster instance group, node, or cluster. The AMI version is determined at the instance group level, and all nodes within an instance group run the same AMI. The cluster-level status is aggregated across all instance groups.
UpToDate: The resource is running the latest available AMI version.UpdateAvailable: A newer AMI version is available for the resource.SecurityUpdateRequired: The current AMI has known security vulnerabilities, and a patched version is available.EndOfLife: The AMI variant has reached end of support and an upgrade is required.type nonrec cluster_summary = {image_version_status : cluster_image_version_status option;The aggregate status of the image version across the cluster's instance groups.
*)training_plan_arns : training_plan_arns option;A list of Amazon Resource Names (ARNs) of the training plans associated with this cluster.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingPlan.html}CreateTrainingPlan} .
*)cluster_status : cluster_status;The status of the SageMaker HyperPod cluster.
*)creation_time : timestamp;The time when the SageMaker HyperPod cluster is created.
*)cluster_name : cluster_name;The name of the SageMaker HyperPod cluster.
*)cluster_arn : cluster_arn;The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.
*)}Lists a summary of the properties of a SageMaker HyperPod cluster.
type nonrec cluster_summaries = cluster_summary listtype nonrec list_clusters_response = {cluster_summaries : cluster_summaries;The summaries of listed SageMaker HyperPod clusters.
*)next_token : next_token option;If the result of the previous ListClusters request was truncated, the response includes a NextToken. To retrieve the next set of clusters, use the token in the next request.
}type nonrec list_clusters_request = {training_plan_arn : training_plan_arn option;The Amazon Resource Name (ARN); of the training plan to filter clusters by. For more information about reserving GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingPlan.html}CreateTrainingPlan} .
*)sort_order : sort_order option;The sort order for results. The default value is Ascending.
sort_by : cluster_sort_by option;The field by which to sort results. The default value is CREATION_TIME.
next_token : next_token option;Set the next token to retrieve the list of SageMaker HyperPod clusters.
*)name_contains : name_contains option;Set the maximum number of instances to print in the list.
*)max_results : max_results option;Specifies the maximum number of clusters to evaluate for the operation (not necessarily the number of matching items). After SageMaker processes the number of clusters up to MaxResults, it stops the operation and returns the matching clusters up to that point. If all the matching clusters are desired, SageMaker will go through all the clusters until NextToken is empty.
creation_time_before : timestamp option;Set an end time for the time range during which you want to list SageMaker HyperPod clusters. A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for CreationTimeAfter. For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.
creation_time_after : timestamp option;Set a start time for the time range during which you want to list SageMaker HyperPod clusters. Timestamps are formatted according to the ISO 8601 standard.
Acceptable formats include:
YYYY-MM-DDThh:mm:ss.sssTZD (UTC), for example, 2014-10-01T20:30:00.000ZYYYY-MM-DDThh:mm:ss.sssTZD (with offset), for example, 2014-10-01T12:30:00.000-08:00YYYY-MM-DD, for example, 2014-10-011412195400. This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC.For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.
*)}type nonrec cluster_instance_status_details = {message : Smaws_Lib.Smithy_api.Types.string_ option;The message from an instance in a SageMaker HyperPod cluster.
*)status : cluster_instance_status;The status of an instance in a SageMaker HyperPod cluster.
*)}Details of an instance in a SageMaker HyperPod cluster.
type nonrec cluster_node_summary = {image_version_status : cluster_image_version_status option;The status of the image version for the cluster node.
*)current_image_release_version : image_release_version option;The version of the HyperPod-managed AMI currently running on the node.
*)private_dns_hostname : cluster_private_dns_hostname option;The private DNS hostname of the SageMaker HyperPod cluster node.
*)ultra_server_info : ultra_server_info option;Contains information about the UltraServer.
*)instance_status : cluster_instance_status_details;The status of the instance.
*)last_software_update_time : timestamp option;The time when SageMaker last updated the software of the instances in the cluster.
*)launch_time : timestamp;The time when the instance is launched.
*)instance_type : cluster_instance_type;The type of the instance.
*)node_logical_id : Smaws_Lib.Smithy_api.Types.string_ option;A unique identifier for the node that persists throughout its lifecycle, from provisioning request to termination. This identifier can be used to track the node even before it has an assigned InstanceId. This field is only included when IncludeNodeLogicalIds is set to True in the ListClusterNodes request.
instance_id : Smaws_Lib.Smithy_api.Types.string_;The ID of the instance.
*)instance_group_name : cluster_instance_group_name;The name of the instance group in which the instance is.
*)}Lists a summary of the properties of an instance (also called a node interchangeably) of a SageMaker HyperPod cluster.
type nonrec cluster_node_summaries = cluster_node_summary listtype nonrec list_cluster_nodes_response = {cluster_node_summaries : cluster_node_summaries;The summaries of listed instances in a SageMaker HyperPod cluster
*)next_token : next_token option;The next token specified for listing instances in a SageMaker HyperPod cluster.
*)}type nonrec list_cluster_nodes_request = {include_node_logical_ids : include_node_logical_ids_boolean option;Specifies whether to include nodes that are still being provisioned in the response. When set to true, the response includes all nodes regardless of their provisioning status. When set to False (default), only nodes with assigned InstanceIds are returned.
sort_order : sort_order option;The sort order for results. The default value is Ascending.
sort_by : cluster_sort_by option;The field by which to sort results. The default value is CREATION_TIME.
next_token : next_token option;If the result of the previous ListClusterNodes request was truncated, the response includes a NextToken. To retrieve the next set of cluster nodes, use the token in the next request.
max_results : max_results option;The maximum number of nodes to return in the response.
*)instance_group_name_contains : cluster_instance_group_name option;A filter that returns the instance groups whose name contain a specified string.
*)creation_time_before : timestamp option;A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for CreationTimeAfter. For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.
creation_time_after : timestamp option;A filter that returns nodes in a SageMaker HyperPod cluster created after the specified time. Timestamps are formatted according to the ISO 8601 standard.
Acceptable formats include:
YYYY-MM-DDThh:mm:ss.sssTZD (UTC), for example, 2014-10-01T20:30:00.000ZYYYY-MM-DDThh:mm:ss.sssTZD (with offset), for example, 2014-10-01T12:30:00.000-08:00YYYY-MM-DD, for example, 2014-10-011412195400. This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC.For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.
*)cluster_name : cluster_name_or_arn;The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster in which you want to retrieve the list of nodes.
*)}The severity level for a HyperPod cluster event.
type nonrec cluster_event_summary = {event_level : cluster_event_level option;The severity level of the event. Valid values are Info, Warn, and Error.
description : Smaws_Lib.Smithy_api.Types.string_ option;A brief, human-readable description of the event.
*)event_time : timestamp;The timestamp when the event occurred.
*)resource_type : cluster_event_resource_type;The type of resource associated with the event. Valid values are Cluster, InstanceGroup, or Instance.
instance_id : Smaws_Lib.Smithy_api.Types.string_ option;The Amazon Elastic Compute Cloud (EC2) instance ID associated with the event, if applicable.
*)instance_group_name : cluster_instance_group_name option;The name of the instance group associated with the event, if applicable.
*)cluster_name : cluster_name;The name of the HyperPod cluster associated with the event.
*)cluster_arn : cluster_arn;The Amazon Resource Name (ARN) of the HyperPod cluster associated with the event.
*)event_id : event_id;The unique identifier (UUID) of the event.
*)}A summary of an event in a HyperPod cluster.
type nonrec cluster_event_summaries = cluster_event_summary listtype nonrec list_cluster_events_response = {events : cluster_event_summaries option;A list of event summaries matching the specified criteria.
*)next_token : next_token option;A token to retrieve the next set of results. Include this token in subsequent ListClusterEvents calls to fetch more events.
}type nonrec list_cluster_events_request = {next_token : next_token option;A token to retrieve the next set of results. This token is obtained from the output of a previous ListClusterEvents call.
max_results : cluster_event_max_results option;The maximum number of events to return in the response. Valid range is 1 to 100.
*)resource_type : cluster_event_resource_type option;The type of resource for which to filter events. Valid values are Cluster, InstanceGroup, or Instance.
sort_order : sort_order option;The order in which to sort the results. Valid values are Ascending or Descending (the default is Descending).
sort_by : event_sort_by option;The field to use for sorting the event list. Currently, the only supported value is EventTime.
event_time_before : timestamp option;The end of the time range for filtering events. Only events that occurred before this time are included in the results.
*)event_time_after : timestamp option;The start of the time range for filtering events. Only events that occurred after this time are included in the results.
*)node_id : cluster_node_id option;The EC2 instance ID to filter events. If specified, only events related to this instance are returned.
*)instance_group_name : cluster_instance_group_name option;The name of the instance group to filter events. If specified, only events related to this instance group are returned.
*)cluster_name : cluster_name_or_arn;The name or Amazon Resource Name (ARN) of the HyperPod cluster for which to list events.
*)}type nonrec final_auto_ml_job_objective_metric = {standard_metric_name : auto_ml_metric_enum option;The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics.
*)value : metric_value;The value of the metric with the best result.
*)metric_name : auto_ml_metric_enum;The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName.
*)type_ : auto_ml_job_objective_type option;The type of metric with the best result.
*)}The best candidate result from an AutoML training job.
type nonrec auto_ml_candidate_step = {candidate_step_name : candidate_step_name;The name for the candidate's step.
*)candidate_step_arn : candidate_step_arn;The ARN for the candidate's step.
*)candidate_step_type : candidate_step_type;Whether the candidate is at the transform, training, or processing step.
*)}Information about the steps for a candidate and what step it is working on.
type nonrec candidate_steps = auto_ml_candidate_step listtype nonrec auto_ml_container_definition = {environment : environment_map option;The environment variables to set in the container. For more information, see ContainerDefinition.
*)model_data_url : url;image : container_image;The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition.
*)}A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition.
type nonrec auto_ml_container_definitions = auto_ml_container_definition listtype nonrec candidate_artifact_locations = {backtest_results : backtest_results_location option;The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type.
*)model_insights : model_insights_location option;The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.
*)explainability : explainability_location;The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.
*)}The location of artifacts for an AutoML candidate job.
type nonrec metric_datum = {set : metric_set_source option;The dataset split from which the AutoML job produced the metric.
*)value : float_ option;The value of the metric.
*)standard_metric_name : auto_ml_metric_extended_enum option;The name of the standard metric.
For definitions of the standard metrics, see Autopilot candidate metrics.
metric_name : auto_ml_metric_enum option;The name of the metric.
*)}Information about the metric for a candidate produced by an AutoML job.
type nonrec metric_data_list = metric_datum listtype nonrec candidate_properties = {candidate_metrics : metric_data_list option;Information about the candidate metrics for an AutoML job.
*)candidate_artifact_locations : candidate_artifact_locations option;The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
*)}The properties of an AutoML candidate job.
type nonrec auto_ml_inference_container_definitions =
(auto_ml_processing_unit * auto_ml_container_definitions) listThe mapping of all supported processing unit (CPU, GPU, etc...) to inference container definitions for the candidate. This field is populated for the V2 API only (for example, for jobs created by calling CreateAutoMLJobV2).
type nonrec auto_ml_candidate = {inference_container_definitions : auto_ml_inference_container_definitions
option;The mapping of all supported processing unit (CPU, GPU, etc...) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling CreateAutoMLJobV2) related to image or text classification problem types only.
candidate_properties : candidate_properties option;The properties of an AutoML candidate job.
*)failure_reason : auto_ml_failure_reason option;The failure reason.
*)last_modified_time : timestamp;The last modified time.
*)end_time : timestamp option;The end time.
*)creation_time : timestamp;The creation time.
*)inference_containers : auto_ml_container_definitions option;Information about the recommended inference container definitions.
*)candidate_status : candidate_status;The candidate's status.
*)candidate_steps : candidate_steps;Information about the candidate's steps.
*)objective_status : objective_status;The objective's status.
*)final_auto_ml_job_objective_metric : final_auto_ml_job_objective_metric option;candidate_name : candidate_name;The name of the candidate.
*)}Information about a candidate produced by an AutoML training job, including its status, steps, and other properties.
type nonrec auto_ml_candidates = auto_ml_candidate listtype nonrec list_candidates_for_auto_ml_job_response = {next_token : next_token option;If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
*)candidates : auto_ml_candidates;Summaries about the AutoMLCandidates.
}type nonrec list_candidates_for_auto_ml_job_request = {next_token : next_token option;If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
*)max_results : auto_ml_max_results_for_trials option;List the job's candidates up to a specified limit.
*)sort_by : candidate_sort_by option;The parameter by which to sort the results. The default is Descending.
sort_order : auto_ml_sort_order option;The sort order for the results. The default is Ascending.
candidate_name_equals : candidate_name option;List the candidates for the job and filter by candidate name.
*)status_equals : candidate_status option;List the candidates for the job and filter by status.
*)auto_ml_job_name : auto_ml_job_name;List the candidates created for the job by providing the job's name.
*)}type nonrec auto_ml_job_secondary_status = | PRE_TRAINING| TRAINING_MODELS| GENERATING_EXPLAINABILITY_REPORT| MODEL_TUNING| FEATURE_ENGINEERING| ANALYZING_DATA| MODEL_INSIGHTS_ERROR| GENERATING_MODEL_INSIGHTS_REPORT| MODEL_DEPLOYMENT_ERROR| DEPLOYING_MODEL| EXPLAINABILITY_ERROR| COMPLETED| CANDIDATE_DEFINITIONS_GENERATED| STOPPING| MAX_AUTO_ML_JOB_RUNTIME_REACHED| STOPPED| FAILED| MAX_CANDIDATES_REACHED| STARTINGtype nonrec auto_ml_partial_failure_reason = {partial_failure_message : auto_ml_failure_reason option;The message containing the reason for a partial failure of an AutoML job.
*)}The reason for a partial failure of an AutoML job.
type nonrec auto_ml_partial_failure_reasons =
auto_ml_partial_failure_reason listtype nonrec auto_ml_job_summary = {partial_failure_reasons : auto_ml_partial_failure_reasons option;The list of reasons for partial failures within an AutoML job.
*)failure_reason : auto_ml_failure_reason option;The failure reason of an AutoML job.
*)last_modified_time : timestamp;When the AutoML job was last modified.
*)end_time : timestamp option;The end time of an AutoML job.
*)creation_time : timestamp;When the AutoML job was created.
*)auto_ml_job_secondary_status : auto_ml_job_secondary_status;The secondary status of the AutoML job.
*)auto_ml_job_status : auto_ml_job_status;The status of the AutoML job.
*)auto_ml_job_arn : auto_ml_job_arn;The ARN of the AutoML job.
*)auto_ml_job_name : auto_ml_job_name;The name of the AutoML job you are requesting.
*)}Provides a summary about an AutoML job.
type nonrec auto_ml_job_summaries = auto_ml_job_summary listtype nonrec list_auto_ml_jobs_response = {next_token : next_token option;If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
*)auto_ml_job_summaries : auto_ml_job_summaries;Returns a summary list of jobs.
*)}type nonrec list_auto_ml_jobs_request = {next_token : next_token option;If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
*)max_results : auto_ml_max_results option;Request a list of jobs up to a specified limit.
*)sort_by : auto_ml_sort_by option;The parameter by which to sort the results. The default is Name.
sort_order : auto_ml_sort_order option;The sort order for the results. The default is Descending.
status_equals : auto_ml_job_status option;Request a list of jobs, using a filter for status.
*)name_contains : auto_ml_name_contains option;Request a list of jobs, using a search filter for name.
*)last_modified_time_before : timestamp option;Request a list of jobs, using a filter for time.
*)last_modified_time_after : timestamp option;Request a list of jobs, using a filter for time.
*)creation_time_before : timestamp option;Request a list of jobs, using a filter for time.
*)creation_time_after : timestamp option;Request a list of jobs, using a filter for time.
*)}type nonrec association_summary = {created_by : user_context option;creation_time : timestamp option;When the association was created.
*)destination_name : experiment_entity_name option;The name of the destination.
*)source_name : experiment_entity_name option;The name of the source.
*)association_type : association_edge_type option;The type of the association.
*)destination_type : string256 option;The destination type.
*)source_type : string256 option;The source type.
*)destination_arn : association_entity_arn option;The Amazon Resource Name (ARN) of the destination.
*)source_arn : association_entity_arn option;The ARN of the source.
*)}Lists a summary of the properties of an association. An association is an entity that links other lineage or experiment entities. An example would be an association between a training job and a model.
type nonrec association_summaries = association_summary listtype nonrec list_associations_response = {next_token : next_token option;A token for getting the next set of associations, if there are any.
*)association_summaries : association_summaries option;A list of associations and their properties.
*)}type nonrec list_associations_request = {max_results : max_results option;The maximum number of associations to return in the response. The default value is 10.
*)next_token : next_token option;If the previous call to ListAssociations didn't return the full set of associations, the call returns a token for getting the next set of associations.
sort_order : sort_order option;The sort order. The default value is Descending.
sort_by : sort_associations_by option;The property used to sort results. The default value is CreationTime.
created_before : timestamp option;A filter that returns only associations created on or before the specified time.
*)created_after : timestamp option;A filter that returns only associations created on or after the specified time.
*)association_type : association_edge_type option;A filter that returns only associations of the specified type.
*)destination_type : string256 option;A filter that returns only associations with the specified destination type.
*)source_type : string256 option;A filter that returns only associations with the specified source type.
*)destination_arn : association_entity_arn option;A filter that returns only associations with the specified destination Amazon Resource Name (ARN).
*)source_arn : association_entity_arn option;A filter that returns only associations with the specified source ARN.
*)}type nonrec artifact_source_type = {value : string256;The ID.
*)source_id_type : artifact_source_id_type;The type of ID.
*)}The ID and ID type of an artifact source.
type nonrec artifact_source_types = artifact_source_type listtype nonrec artifact_source = {source_types : artifact_source_types option;A list of source types.
*)source_uri : source_uri;The URI of the source.
*)}A structure describing the source of an artifact.
type nonrec artifact_summary = {last_modified_time : timestamp option;When the artifact was last modified.
*)creation_time : timestamp option;When the artifact was created.
*)artifact_type : string256 option;The type of the artifact.
*)source : artifact_source option;The source of the artifact.
*)artifact_name : experiment_entity_name option;The name of the artifact.
*)artifact_arn : artifact_arn option;The Amazon Resource Name (ARN) of the artifact.
*)}Lists a summary of the properties of an artifact. An artifact represents a URI addressable object or data. Some examples are a dataset and a model.
type nonrec artifact_summaries = artifact_summary listtype nonrec list_artifacts_response = {next_token : next_token option;A token for getting the next set of artifacts, if there are any.
*)artifact_summaries : artifact_summaries option;A list of artifacts and their properties.
*)}type nonrec list_artifacts_request = {max_results : max_results option;The maximum number of artifacts to return in the response. The default value is 10.
*)next_token : next_token option;If the previous call to ListArtifacts didn't return the full set of artifacts, the call returns a token for getting the next set of artifacts.
sort_order : sort_order option;The sort order. The default value is Descending.
sort_by : sort_artifacts_by option;The property used to sort results. The default value is CreationTime.
created_before : timestamp option;A filter that returns only artifacts created on or before the specified time.
*)created_after : timestamp option;A filter that returns only artifacts created on or after the specified time.
*)artifact_type : string256 option;A filter that returns only artifacts of the specified type.
*)source_uri : source_uri option;A filter that returns only artifacts with the specified source URI.
*)}type nonrec app_details = {resource_spec : resource_spec option;creation_time : creation_time option;The creation time.
*)status : app_status option;The status.
*)app_name : app_name option;The name of the app.
*)app_type : app_type option;The type of app.
*)space_name : space_name option;The name of the space.
*)user_profile_name : user_profile_name option;The user profile name.
*)domain_id : domain_id option;The domain ID.
*)}Details about an Amazon SageMaker AI app.
type nonrec app_list = app_details listtype nonrec list_apps_response = {next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)apps : app_list option;The list of apps.
*)}type nonrec list_apps_request = {space_name_equals : space_name option;A parameter to search by space name. If UserProfileNameEquals is set, then this value cannot be set.
user_profile_name_equals : user_profile_name option;A parameter to search by user profile name. If SpaceNameEquals is set, then this value cannot be set.
domain_id_equals : domain_id option;A parameter to search for the domain ID.
*)sort_by : app_sort_key option;The parameter by which to sort the results. The default is CreationTime.
*)sort_order : sort_order option;The sort order for the results. The default is Ascending.
*)max_results : max_results option;This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.
next_token : next_token option;If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
*)}type nonrec app_image_config_details = {code_editor_app_image_config : code_editor_app_image_config option;The configuration for the file system and the runtime, such as the environment variables and entry point.
*)jupyter_lab_app_image_config : jupyter_lab_app_image_config option;The configuration for the file system and the runtime, such as the environment variables and entry point.
*)kernel_gateway_image_config : kernel_gateway_image_config option;The configuration for the file system and kernels in the SageMaker AI image.
*)last_modified_time : timestamp option;When the AppImageConfig was last modified.
*)creation_time : timestamp option;When the AppImageConfig was created.
*)app_image_config_name : app_image_config_name option;The name of the AppImageConfig. Must be unique to your account.
*)app_image_config_arn : app_image_config_arn option;The ARN of the AppImageConfig.
*)}The configuration for running a SageMaker AI image as a KernelGateway app.
type nonrec app_image_config_list = app_image_config_details listtype nonrec list_app_image_configs_response = {app_image_configs : app_image_config_list option;A list of AppImageConfigs and their properties.
*)next_token : next_token option;A token for getting the next set of AppImageConfigs, if there are any.
*)}type nonrec list_app_image_configs_request = {sort_order : sort_order option;The sort order. The default value is Descending.
sort_by : app_image_config_sort_key option;The property used to sort results. The default value is CreationTime.
modified_time_after : timestamp option;A filter that returns only AppImageConfigs modified on or after the specified time.
*)modified_time_before : timestamp option;A filter that returns only AppImageConfigs modified on or before the specified time.
*)creation_time_after : timestamp option;A filter that returns only AppImageConfigs created on or after the specified time.
*)creation_time_before : timestamp option;A filter that returns only AppImageConfigs created on or before the specified time.
*)name_contains : app_image_config_name option;A filter that returns only AppImageConfigs whose name contains the specified string.
*)next_token : next_token option;If the previous call to ListImages didn't return the full set of AppImageConfigs, the call returns a token for getting the next set of AppImageConfigs.
max_results : max_results option;The total number of items to return in the response. If the total number of items available is more than the value specified, a NextToken is provided in the response. To resume pagination, provide the NextToken value in the as part of a subsequent call. The default value is 10.
}type nonrec list_aliases_response = {next_token : next_token option;A token for getting the next set of aliases, if more aliases exist.
*)sage_maker_image_version_aliases : sage_maker_image_version_aliases option;A list of SageMaker AI image version aliases.
*)}type nonrec list_aliases_request = {next_token : next_token option;If the previous call to ListAliases didn't return the full set of aliases, the call returns a token for retrieving the next set of aliases.
max_results : max_results option;The maximum number of aliases to return.
*)version : image_version_number option;The version of the image. If image version is not specified, the aliases of all versions of the image are listed.
*)alias : sage_maker_image_version_alias option;The alias of the image version.
*)image_name : image_name;The name of the image.
*)}type nonrec algorithm_summary = {algorithm_status : algorithm_status;The overall status of the algorithm.
*)creation_time : creation_time;A timestamp that shows when the algorithm was created.
*)algorithm_description : entity_description option;A brief description of the algorithm.
*)algorithm_arn : algorithm_arn;The Amazon Resource Name (ARN) of the algorithm.
*)algorithm_name : entity_name;The name of the algorithm that is described by the summary.
*)}Provides summary information about an algorithm.
type nonrec algorithm_summary_list = algorithm_summary listtype nonrec list_algorithms_output = {next_token : next_token option;If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.
*)algorithm_summary_list : algorithm_summary_list;>An array of AlgorithmSummary objects, each of which lists an algorithm.
}type nonrec list_algorithms_input = {sort_order : sort_order option;The sort order for the results. The default is Ascending.
sort_by : algorithm_sort_by option;The parameter by which to sort the results. The default is CreationTime.
next_token : next_token option;If the response to a previous ListAlgorithms request was truncated, the response includes a NextToken. To retrieve the next set of algorithms, use the token in the next request.
name_contains : name_contains option;A string in the algorithm name. This filter returns only algorithms whose name contains the specified string.
*)max_results : max_results option;The maximum number of algorithms to return in the response.
*)creation_time_before : creation_time option;A filter that returns only algorithms created before the specified time (timestamp).
*)creation_time_after : creation_time option;A filter that returns only algorithms created after the specified time (timestamp).
*)}type nonrec ai_workload_config_summary = {creation_time : timestamp;A timestamp that indicates when the configuration was created.
*)ai_workload_config_arn : ai_workload_config_arn;The Amazon Resource Name (ARN) of the AI workload configuration.
*)ai_workload_config_name : ai_entity_name;The name of the AI workload configuration.
*)}Summary information about an AI workload configuration.
type nonrec ai_workload_config_summary_list = ai_workload_config_summary listtype nonrec list_ai_workload_configs_response = {next_token : next_token option;If the response is truncated, Amazon SageMaker AI returns this token. To retrieve the next set of configurations, use it in the subsequent request.
*)ai_workload_configs : ai_workload_config_summary_list;An array of AIWorkloadConfigSummary objects, one for each AI workload configuration that matches the specified filters.
}type nonrec list_ai_workload_configs_request = {sort_order : sort_order option;The sort order for results. The default is Descending.
sort_by : list_ai_workload_configs_sort_by option;The field to sort results by. The default is CreationTime.
creation_time_before : timestamp option;A filter that returns only configurations created before the specified time.
*)creation_time_after : timestamp option;A filter that returns only configurations created after the specified time.
*)name_contains : name_contains option;A string in the configuration name. This filter returns only configurations whose name contains the specified string.
*)next_token : next_token option;If the previous call to ListAIWorkloadConfigs didn't return the full set of configurations, the call returns a token for getting the next set of configurations.
max_results : max_results option;The maximum number of AI workload configurations to return in the response.
*)}type nonrec ai_recommendation_job_summary = {end_time : timestamp option;A timestamp that indicates when the recommendation job completed.
*)creation_time : timestamp;A timestamp that indicates when the recommendation job was created.
*)ai_recommendation_job_status : ai_recommendation_job_status;The status of the recommendation job.
*)ai_recommendation_job_arn : ai_recommendation_job_arn;The Amazon Resource Name (ARN) of the recommendation job.
*)ai_recommendation_job_name : ai_entity_name;The name of the recommendation job.
*)}Summary information about an AI recommendation job.
type nonrec ai_recommendation_job_summary_list =
ai_recommendation_job_summary listtype nonrec list_ai_recommendation_jobs_response = {next_token : next_token option;If the response is truncated, Amazon SageMaker AI returns this token. To retrieve the next set of jobs, use it in the subsequent request.
*)ai_recommendation_jobs : ai_recommendation_job_summary_list;An array of AIRecommendationJobSummary objects, one for each recommendation job that matches the specified filters.
}type nonrec list_ai_recommendation_jobs_request = {sort_order : sort_order option;The sort order for results. The default is Descending.
sort_by : list_ai_recommendation_jobs_sort_by option;The field to sort results by. The default is CreationTime.
creation_time_before : timestamp option;A filter that returns only jobs created before the specified time.
*)creation_time_after : timestamp option;A filter that returns only jobs created after the specified time.
*)status_equals : ai_recommendation_job_status option;A filter that returns only recommendation jobs with the specified status.
*)name_contains : name_contains option;A string in the job name. This filter returns only jobs whose name contains the specified string.
*)next_token : next_token option;If the previous call to ListAIRecommendationJobs didn't return the full set of jobs, the call returns a token for getting the next set.
max_results : max_results option;The maximum number of recommendation jobs to return in the response.
*)}type nonrec ai_benchmark_job_summary = {ai_workload_config_name : ai_entity_name option;The name of the AI workload configuration used by the benchmark job.
*)end_time : timestamp option;A timestamp that indicates when the benchmark job completed.
*)creation_time : timestamp;A timestamp that indicates when the benchmark job was created.
*)ai_benchmark_job_status : ai_benchmark_job_status;The status of the benchmark job.
*)ai_benchmark_job_arn : ai_benchmark_job_arn;The Amazon Resource Name (ARN) of the benchmark job.
*)ai_benchmark_job_name : ai_entity_name;The name of the benchmark job.
*)}Summary information about an AI benchmark job.
type nonrec ai_benchmark_job_summary_list = ai_benchmark_job_summary listtype nonrec list_ai_benchmark_jobs_response = {next_token : next_token option;If the response is truncated, Amazon SageMaker AI returns this token. To retrieve the next set of jobs, use it in the subsequent request.
*)ai_benchmark_jobs : ai_benchmark_job_summary_list;An array of AIBenchmarkJobSummary objects, one for each benchmark job that matches the specified filters.
}type nonrec list_ai_benchmark_jobs_request = {sort_order : sort_order option;The sort order for results. The default is Descending.
sort_by : list_ai_benchmark_jobs_sort_by option;The field to sort results by. The default is CreationTime.
creation_time_before : timestamp option;A filter that returns only jobs created before the specified time.
*)creation_time_after : timestamp option;A filter that returns only jobs created after the specified time.
*)status_equals : ai_benchmark_job_status option;A filter that returns only benchmark jobs with the specified status.
*)name_contains : name_contains option;A string in the job name. This filter returns only jobs whose name contains the specified string.
*)next_token : next_token option;If the previous call to ListAIBenchmarkJobs didn't return the full set of jobs, the call returns a token for getting the next set.
max_results : max_results option;The maximum number of benchmark jobs to return in the response.
*)}type nonrec action_source = {source_id : string256 option;The ID of the source.
*)source_type : string256 option;The type of the source.
*)source_uri : source_uri;The URI of the source.
*)}A structure describing the source of an action.
type nonrec action_summary = {last_modified_time : timestamp option;When the action was last modified.
*)creation_time : timestamp option;When the action was created.
*)status : action_status option;The status of the action.
*)action_type : string64 option;The type of the action.
*)source : action_source option;The source of the action.
*)action_name : experiment_entity_name option;The name of the action.
*)action_arn : action_arn option;The Amazon Resource Name (ARN) of the action.
*)}Lists the properties of an action. An action represents an action or activity. Some examples are a workflow step and a model deployment. Generally, an action involves at least one input artifact or output artifact.
type nonrec action_summaries = action_summary listtype nonrec list_actions_response = {next_token : next_token option;A token for getting the next set of actions, if there are any.
*)action_summaries : action_summaries option;A list of actions and their properties.
*)}type nonrec list_actions_request = {max_results : max_results option;The maximum number of actions to return in the response. The default value is 10.
*)next_token : next_token option;If the previous call to ListActions didn't return the full set of actions, the call returns a token for getting the next set of actions.
sort_order : sort_order option;The sort order. The default value is Descending.
sort_by : sort_actions_by option;The property used to sort results. The default value is CreationTime.
created_before : timestamp option;A filter that returns only actions created on or before the specified time.
*)created_after : timestamp option;A filter that returns only actions created on or after the specified time.
*)action_type : string256 option;A filter that returns only actions of the specified type.
*)source_uri : source_uri option;A filter that returns only actions with the specified source URI.
*)}type nonrec import_hub_content_response = {hub_content_arn : hub_content_arn;The ARN of the hub content that was imported.
*)hub_arn : hub_arn;The ARN of the hub that the content was imported into.
*)}type nonrec import_hub_content_request = {hub_content_search_keywords : hub_content_search_keyword_list option;The searchable keywords of the hub content.
*)support_status : hub_content_support_status option;The status of the hub content resource.
*)hub_content_document : hub_content_document;The hub content document that describes information about the hub content such as type, associated containers, scripts, and more.
*)hub_content_markdown : hub_content_markdown option;A string that provides a description of the hub content. This string can include links, tables, and standard markdown formating.
*)hub_content_description : hub_content_description option;A description of the hub content to import.
*)hub_content_display_name : hub_content_display_name option;The display name of the hub content to import.
*)hub_name : hub_name_or_arn;The name of the hub to import content into.
*)document_schema_version : document_schema_version;The version of the hub content schema to import.
*)hub_content_type : hub_content_type;The type of hub content to import.
*)hub_content_version : hub_content_version option;The version of the hub content to import.
*)hub_content_name : hub_content_name;The name of the hub content to import.
*)}type nonrec property_name_suggestion = {property_name : resource_property_name option;A suggested property name based on what you entered in the search textbox in the SageMaker console.
*)}A property name returned from a GetSearchSuggestions call that specifies a value in the PropertyNameQuery field.
type nonrec property_name_suggestion_list = property_name_suggestion listtype nonrec get_search_suggestions_response = {property_name_suggestions : property_name_suggestion_list option;A list of property names for a Resource that match a SuggestionQuery.
}type nonrec get_search_suggestions_request = {suggestion_query : suggestion_query option;Limits the property names that are included in the response.
*)resource : resource_type;The name of the SageMaker resource to search for.
*)}type nonrec dynamic_scaling_configuration = {scaling_policies : scaling_policies option;An object of the scaling policies for each metric.
*)scale_out_cooldown : integer option;The recommended scale out cooldown time for your autoscaling policy.
*)scale_in_cooldown : integer option;The recommended scale in cooldown time for your autoscaling policy.
*)max_capacity : integer option;The recommended maximum capacity to specify for your autoscaling policy.
*)min_capacity : integer option;The recommended minimum capacity to specify for your autoscaling policy.
*)}An object with the recommended values for you to specify when creating an autoscaling policy.
type nonrec get_scaling_configuration_recommendation_response = {dynamic_scaling_configuration : dynamic_scaling_configuration option;An object with the recommended values for you to specify when creating an autoscaling policy.
*)metric : scaling_policy_metric option;An object with a list of metrics that were benchmarked during the previously completed Inference Recommender job.
*)scaling_policy_objective : scaling_policy_objective option;An object representing the anticipated traffic pattern for an endpoint that you specified in the request.
*)target_cpu_utilization_per_core : utilization_percentage_per_core option;The percentage of how much utilization you want an instance to use before autoscaling, which you specified in the request. The default value is 50%.
*)endpoint_name : endpoint_name option;The name of an endpoint benchmarked during a previously completed Inference Recommender job.
*)recommendation_id : string_ option;The recommendation ID of a previously completed inference recommendation.
*)inference_recommendations_job_name : recommendation_job_name option;The name of a previously completed Inference Recommender job.
*)}type nonrec get_scaling_configuration_recommendation_request = {scaling_policy_objective : scaling_policy_objective option;An object where you specify the anticipated traffic pattern for an endpoint.
*)target_cpu_utilization_per_core : utilization_percentage_per_core option;The percentage of how much utilization you want an instance to use before autoscaling. The default value is 50%.
*)endpoint_name : endpoint_name option;The name of an endpoint benchmarked during a previously completed inference recommendation job. This name should come from one of the recommendations returned by the job specified in the InferenceRecommendationsJobName field.
Specify either this field or the RecommendationId field.
recommendation_id : string_ option;The recommendation ID of a previously completed inference recommendation. This ID should come from one of the recommendations returned by the job specified in the InferenceRecommendationsJobName field.
Specify either this field or the EndpointName field.
inference_recommendations_job_name : recommendation_job_name;The name of a previously completed Inference Recommender job.
*)}type nonrec get_sagemaker_servicecatalog_portfolio_status_output = {status : sagemaker_servicecatalog_status option;Whether Service Catalog is enabled or disabled in SageMaker.
*)}type nonrec get_model_package_group_policy_output = {resource_policy : policy_string;The resource policy for the model group.
*)}type nonrec get_model_package_group_policy_input = {model_package_group_name : entity_name;The name of the model group for which to get the resource policy.
*)}type nonrec get_lineage_group_policy_response = {resource_policy : resource_policy_string option;The resource policy that gives access to the lineage group in another account.
*)lineage_group_arn : lineage_group_arn option;The Amazon Resource Name (ARN) of the lineage group.
*)}type nonrec get_lineage_group_policy_request = {lineage_group_name : lineage_group_name_or_arn;The name or Amazon Resource Name (ARN) of the lineage group.
*)}type nonrec device_stats = {registered_device_count : long;The number of registered devices.
*)connected_device_count : long;The number of devices connected with a heartbeat.
*)}Status of devices.
type nonrec agent_version = {agent_count : long;The number of Edge Manager agents.
*)version : edge_version;Version of the agent.
*)}Edge Manager agent version.
type nonrec agent_versions = agent_version listtype nonrec edge_model_stat = {sampling_device_count : long;The number of devices with this model version and are producing sample data.
*)active_device_count : long;The number of devices that have this model version, a heart beat, and are currently running.
*)connected_device_count : long;The number of devices that have this model version and have a heart beat.
*)offline_device_count : long;The number of devices that have this model version and do not have a heart beat.
*)model_version : edge_version;The model version.
*)model_name : entity_name;The name of the model.
*)}Status of edge devices with this model.
type nonrec edge_model_stats = edge_model_stat listtype nonrec get_device_fleet_report_response = {model_stats : edge_model_stats option;Status of model on device.
*)agent_versions : agent_versions option;The versions of Edge Manager agent deployed on the fleet.
*)device_stats : device_stats option;Status of devices.
*)report_generated : timestamp option;Timestamp of when the report was generated.
*)description : device_fleet_description option;Description of the fleet.
*)output_config : edge_output_config option;The output configuration for storing sample data collected by the fleet.
*)device_fleet_name : entity_name;The name of the fleet.
*)device_fleet_arn : device_fleet_arn;The Amazon Resource Name (ARN) of the device.
*)}type nonrec get_device_fleet_report_request = {device_fleet_name : entity_name;The name of the fleet.
*)}type nonrec extend_training_plan_response = {training_plan_extensions : training_plan_extensions;The list of extensions for the training plan, including the newly created extension.
*)}type nonrec extend_training_plan_request = {training_plan_extension_offering_id : training_plan_extension_offering_id;The unique identifier of the extension offering to purchase. You can retrieve this ID from the TrainingPlanExtensionOfferings in the response of the SearchTrainingPlanOfferings API.
}type nonrec disassociate_trial_component_response = {trial_arn : trial_arn option;The Amazon Resource Name (ARN) of the trial.
*)trial_component_arn : trial_component_arn option;The Amazon Resource Name (ARN) of the trial component.
*)}type nonrec disassociate_trial_component_request = {trial_name : experiment_entity_name;The name of the trial to disassociate from.
*)trial_component_name : experiment_entity_name;The name of the component to disassociate from the trial.
*)}type nonrec detach_cluster_node_volume_response = {device_name : volume_device_name;The device name assigned to your attached volume on the target instance.
*)status : volume_attachment_status;The current status of your volume detachment operation.
*)attach_time : timestamp;The original timestamp when your volume was initially attached to the node.
*)volume_id : volume_id;The unique identifier of your EBS volume that was detached.
*)node_id : cluster_node_id;The unique identifier of the cluster node from which your volume was detached.
*)cluster_arn : cluster_arn;The Amazon Resource Name (ARN) of your SageMaker HyperPod cluster where the volume detachment operation was performed.
*)}type nonrec detach_cluster_node_volume_request = {volume_id : volume_id;The unique identifier of your EBS volume that you want to detach. Your volume must be currently attached to the specified node.
*)node_id : cluster_node_id;The unique identifier of the cluster node from which you want to detach the volume.
*)cluster_arn : cluster_arn;The Amazon Resource Name (ARN) of your SageMaker HyperPod cluster containing the target node. Your cluster must use EKS as the orchestration and be in the InService state.
}type nonrec describe_workteam_response = {workteam : workteam;A Workteam instance that contains information about the work team.
}type nonrec describe_workteam_request = {workteam_name : workteam_name;The name of the work team to return a description of.
*)}type nonrec describe_workforce_response = {workforce : workforce;A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.
*)}type nonrec describe_workforce_request = {workforce_name : workforce_name;The name of the private workforce whose access you want to restrict. WorkforceName is automatically set to default when a workforce is created and cannot be modified.
}type nonrec describe_user_profile_response = {user_settings : user_settings option;A collection of settings.
*)single_sign_on_user_value : string256 option;The IAM Identity Center user value.
*)single_sign_on_user_identifier : single_sign_on_user_identifier option;The IAM Identity Center user identifier.
*)failure_reason : failure_reason option;The failure reason.
*)creation_time : creation_time option;The creation time.
*)last_modified_time : last_modified_time option;The last modified time.
*)status : user_profile_status option;The status.
*)home_efs_file_system_uid : efs_uid option;The ID of the user's profile in the Amazon Elastic File System volume.
*)user_profile_name : user_profile_name option;The user profile name.
*)user_profile_arn : user_profile_arn option;The user profile Amazon Resource Name (ARN).
*)domain_id : domain_id option;The ID of the domain that contains the profile.
*)}type nonrec describe_user_profile_request = {user_profile_name : user_profile_name;The user profile name. This value is not case sensitive.
*)domain_id : domain_id;The domain ID.
*)}type nonrec describe_trial_component_response = {sources : trial_component_sources option;A list of ARNs and, if applicable, job types for multiple sources of an experiment run.
*)lineage_group_arn : lineage_group_arn option;The Amazon Resource Name (ARN) of the lineage group.
*)metrics : trial_component_metric_summaries option;The metrics for the component.
*)metadata_properties : metadata_properties option;output_artifacts : trial_component_artifacts option;The output artifacts of the component.
*)input_artifacts : trial_component_artifacts option;The input artifacts of the component.
*)parameters : trial_component_parameters option;The hyperparameters of the component.
*)last_modified_by : user_context option;Who last modified the component.
*)last_modified_time : timestamp option;When the component was last modified.
*)created_by : user_context option;Who created the trial component.
*)creation_time : timestamp option;When the component was created.
*)end_time : timestamp option;When the component ended.
*)start_time : timestamp option;When the component started.
*)status : trial_component_status option;The status of the component. States include:
source : trial_component_source option;The Amazon Resource Name (ARN) of the source and, optionally, the job type.
*)display_name : experiment_entity_name option;The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
trial_component_arn : trial_component_arn option;The Amazon Resource Name (ARN) of the trial component.
*)trial_component_name : experiment_entity_name option;The name of the trial component.
*)}type nonrec describe_trial_component_request = {trial_component_name : experiment_entity_name_or_arn;The name of the trial component to describe.
*)}type nonrec describe_trial_response = {metadata_properties : metadata_properties option;last_modified_by : user_context option;Who last modified the trial.
*)last_modified_time : timestamp option;When the trial was last modified.
*)created_by : user_context option;Who created the trial.
*)creation_time : timestamp option;When the trial was created.
*)source : trial_source option;The Amazon Resource Name (ARN) of the source and, optionally, the job type.
*)experiment_name : experiment_entity_name option;The name of the experiment the trial is part of.
*)display_name : experiment_entity_name option;The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
trial_arn : trial_arn option;The Amazon Resource Name (ARN) of the trial.
*)trial_name : experiment_entity_name option;The name of the trial.
*)}type nonrec describe_trial_request = {trial_name : experiment_entity_name;The name of the trial to describe.
*)}type nonrec describe_transform_job_response = {experiment_config : experiment_config option;data_processing : data_processing option;auto_ml_job_arn : auto_ml_job_arn option;The Amazon Resource Name (ARN) of the AutoML transform job.
*)labeling_job_arn : labeling_job_arn option;The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
*)transform_end_time : timestamp option;Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime.
transform_start_time : timestamp option;Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.
creation_time : timestamp;A timestamp that shows when the transform Job was created.
*)transform_resources : transform_resources;Describes the resources, including ML instance types and ML instance count, to use for the transform job.
*)data_capture_config : batch_data_capture_config option;Configuration to control how SageMaker captures inference data.
*)transform_output : transform_output option;Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
*)transform_input : transform_input;Describes the dataset to be transformed and the Amazon S3 location where it is stored.
*)environment : transform_environment_map option;The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
*)batch_strategy : batch_strategy option;Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
To enable the batch strategy, you must set SplitType to Line, RecordIO, or TFRecord.
max_payload_in_m_b : max_payload_in_m_b option;The maximum payload size, in MB, used in the transform job.
*)model_client_config : model_client_config option;The timeout and maximum number of retries for processing a transform job invocation.
*)max_concurrent_transforms : max_concurrent_transforms option;The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.
*)model_name : model_name;The name of the model used in the transform job.
*)failure_reason : failure_reason option;If the transform job failed, FailureReason describes why it failed. A transform job creates a log file, which includes error messages, and stores it as an Amazon S3 object. For more information, see Log Amazon SageMaker Events with Amazon CloudWatch.
transform_job_status : transform_job_status;The status of the transform job. If the transform job failed, the reason is returned in the FailureReason field.
transform_job_arn : transform_job_arn;The Amazon Resource Name (ARN) of the transform job.
*)transform_job_name : transform_job_name;The name of the transform job.
*)}type nonrec describe_transform_job_request = {transform_job_name : transform_job_name;The name of the transform job that you want to view details of.
*)}type nonrec describe_training_plan_extension_history_response = {next_token : next_token option;A token to continue pagination if more results are available.
*)training_plan_extensions : training_plan_extensions;A list of extensions for the specified training plan.
*)}type nonrec describe_training_plan_extension_history_request = {max_results : max_results option;The maximum number of extensions to return in the response.
*)next_token : next_token option;A token to continue pagination if more results are available.
*)training_plan_arn : training_plan_arn;The Amazon Resource Name (ARN); of the training plan to retrieve extension history for.
*)}type nonrec describe_training_plan_response = {reserved_capacity_summaries : reserved_capacity_summaries option;The list of Reserved Capacity providing the underlying compute resources of the plan.
*)target_resources : sage_maker_resource_names option;The target resources (e.g., SageMaker Training Jobs, SageMaker HyperPod, SageMaker Endpoints, Studio apps) that can use this training plan.
Training plans are specific to their target resource.
total_ultra_server_count : ultra_server_count option;The total number of UltraServers reserved to this training plan.
*)available_spare_instance_count : available_spare_instance_count option;The number of available spare instances in the training plan.
*)unhealthy_instance_count : unhealthy_instance_count option;The number of instances in the training plan that are currently in an unhealthy state.
*)in_use_instance_count : in_use_instance_count option;The number of instances currently in use from this training plan.
*)available_instance_count : available_instance_count option;The number of instances currently available for use in this training plan.
*)total_instance_count : total_instance_count option;The total number of instances reserved in this training plan.
*)currency_code : currency_code option;The currency code for the upfront fee (e.g., USD).
*)upfront_fee : string256 option;The upfront fee for the training plan.
*)end_time : timestamp option;The end time of the training plan.
*)start_time : timestamp option;The start time of the training plan.
*)duration_minutes : training_plan_duration_minutes option;The additional minutes beyond whole hours in the total duration for this training plan.
*)duration_hours : training_plan_duration_hours option;The number of whole hours in the total duration for this training plan.
*)status_message : training_plan_status_message option;A message providing additional information about the current status of the training plan.
*)status : training_plan_status;The current status of the training plan (e.g., Pending, Active, Expired). To see the complete list of status values available for a training plan, refer to the Status attribute within the
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TrainingPlanSummary.html}TrainingPlanSummary} object.
*)training_plan_name : training_plan_name;The name of the training plan.
*)training_plan_arn : training_plan_arn;The Amazon Resource Name (ARN); of the training plan.
*)}type nonrec describe_training_plan_request = {training_plan_name : training_plan_name;The name of the training plan to describe.
*)}type nonrec billable_token_count = Smaws_Lib.CoreTypes.Int64.ttype nonrec profiler_rule_evaluation_status = {last_modified_time : timestamp option;Timestamp when the rule evaluation status was last modified.
*)status_details : status_details option;Details from the rule evaluation.
*)rule_evaluation_status : rule_evaluation_status option;Status of the rule evaluation.
*)rule_evaluation_job_arn : processing_job_arn option;The Amazon Resource Name (ARN) of the rule evaluation job.
*)rule_configuration_name : rule_configuration_name option;The name of the rule configuration.
*)}Information about the status of the rule evaluation.
type nonrec profiler_rule_evaluation_statuses =
profiler_rule_evaluation_status listtype nonrec remote_debug_config = {enable_remote_debug : enable_remote_debug option;If set to True, enables remote debugging.
*)}Configuration for remote debugging for the CreateTrainingJob API. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
type nonrec infra_check_config = {enable_infra_check : enable_infra_check option;Enables an infrastructure health check.
*)}Configuration information for the infrastructure health check of a training job. A SageMaker-provided health check tests the health of instance hardware and cluster network connectivity.
type nonrec mlflow_config = {mlflow_run_name : mlflow_run_name option;The MLflow run name used for this job.
*)mlflow_experiment_name : mlflow_experiment_name option;The MLflow experiment name used for this job.
*)mlflow_resource_arn : ml_flow_resource_arn;The Amazon Resource Name (ARN) of the MLflow resource.
*)}The MLflow configuration using SageMaker managed MLflow.
type nonrec mlflow_details = {mlflow_run_id : mlflow_run_id option;The MLflow run ID used for this job.
*)mlflow_experiment_id : mlflow_experiment_id option;The MLflow experiment ID used for this job.
*)}The MLflow details of this job.
type nonrec describe_training_job_response = {output_model_package_arn : model_package_arn option;The Amazon Resource Name (ARN) of the output model package containing model weights or checkpoints.
*)progress_info : training_progress_info option;The Serverless training job progress information.
*)mlflow_details : mlflow_details option;The MLflow details of this job.
*)model_package_config : model_package_config option;The configuration for the model package.
*)mlflow_config : mlflow_config option;The MLflow configuration using SageMaker managed MLflow.
*)serverless_job_config : serverless_job_config option;The configuration for serverless training jobs.
*)infra_check_config : infra_check_config option;Contains information about the infrastructure health check configuration for the training job.
*)remote_debug_config : remote_debug_config option;Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
*)retry_strategy : retry_strategy option;The number of times to retry the job when the job fails due to an InternalServerError.
environment : training_environment_map option;The environment variables to set in the Docker container.
Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.
*)profiling_status : profiling_status option;Profiling status of a training job.
*)profiler_rule_evaluation_statuses : profiler_rule_evaluation_statuses option;Evaluation status of Amazon SageMaker Debugger rules for profiling on a training job.
*)profiler_rule_configurations : profiler_rule_configurations option;Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
*)profiler_config : profiler_config option;debug_rule_evaluation_statuses : debug_rule_evaluation_statuses option;Evaluation status of Amazon SageMaker Debugger rules for debugging on a training job.
*)tensor_board_output_config : tensor_board_output_config option;debug_rule_configurations : debug_rule_configurations option;Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
*)experiment_config : experiment_config option;debug_hook_config : debug_hook_config option;billable_token_count : billable_token_count option;The billable token count for eligible serverless training jobs.
*)billable_time_in_seconds : billable_time_in_seconds option;The billable time in seconds. Billable time refers to the absolute wall-clock time.
Multiply BillableTimeInSeconds by the number of instances (InstanceCount) in your training cluster to get the total compute time SageMaker bills you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount .
You can calculate the savings from using managed spot training using the formula (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example, if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.
training_time_in_seconds : training_time_in_seconds option;The training time in seconds.
*)checkpoint_config : checkpoint_config option;enable_managed_spot_training : boolean_ option;A Boolean indicating whether managed spot training is enabled (True) or not (False).
enable_inter_container_traffic_encryption : boolean_ option;To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithms in distributed training.
enable_network_isolation : boolean_ option;If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
final_metric_data_list : final_metric_data_list option;A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
secondary_status_transitions : secondary_status_transitions option;A history of all of the secondary statuses that the training job has transitioned through.
*)last_modified_time : timestamp option;A timestamp that indicates when the status of the training job was last modified.
*)training_end_time : timestamp option;Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
training_start_time : timestamp option;Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
creation_time : timestamp;A timestamp that indicates when the training job was created.
*)stopping_condition : stopping_condition;Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
vpc_config : vpc_config option;A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
*)warm_pool_status : warm_pool_status option;The status of the warm pool associated with the training job.
*)resource_config : resource_config option;Resources, including ML compute instances and ML storage volumes, that are configured for model training.
*)output_data_config : output_data_config option;The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.
*)input_data_config : input_data_config option;An array of Channel objects that describes each data input channel.
role_arn : role_arn option;The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
*)algorithm_specification : algorithm_specification option;Information about the algorithm used for training, and algorithm metadata.
*)hyper_parameters : hyper_parameters option;Algorithm-specific parameters.
*)failure_reason : failure_reason option;If the training job failed, the reason it failed.
*)secondary_status : secondary_status;Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see StatusMessage under SecondaryStatusTransition.
SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.Pending - The training job is waiting for compute capacity or compute resource provision.Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.Training - Training is in progress.Interrupted - The job stopped because the managed spot training instances were interrupted.Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.Completed
Completed - The training job has completed.Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.MaxWaitTimeExceeded - The job stopped because it exceeded the maximum allowed wait time.Stopped - The training job has stopped.Stopping
Stopping - Stopping the training job.Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstancesPreparingTrainingDownloadingTrainingImagetraining_job_status : training_job_status;The status of the training job.
SageMaker provides the following training job statuses:
InProgress - The training is in progress.Completed - The training job has completed.Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.Stopping - The training job is stopping.Stopped - The training job has stopped.For more detailed information, see SecondaryStatus.
model_artifacts : model_artifacts;Information about the Amazon S3 location that is configured for storing model artifacts.
*)auto_ml_job_arn : auto_ml_job_arn option;The Amazon Resource Name (ARN) of an AutoML job.
*)labeling_job_arn : labeling_job_arn option;The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling job that created the transform or training job.
*)tuning_job_arn : hyper_parameter_tuning_job_arn option;The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
*)training_job_arn : training_job_arn;The Amazon Resource Name (ARN) of the training job.
*)training_job_name : training_job_name;Name of the model training job.
*)}type nonrec describe_training_job_request = {training_job_name : training_job_name;The name of the training job.
*)}type nonrec describe_subscribed_workteam_response = {subscribed_workteam : subscribed_workteam;A Workteam instance that contains information about the work team.
}type nonrec describe_subscribed_workteam_request = {workteam_arn : workteam_arn;The Amazon Resource Name (ARN) of the subscribed work team to describe.
*)}type nonrec describe_studio_lifecycle_config_response = {studio_lifecycle_config_app_type : studio_lifecycle_config_app_type option;The App type that the Lifecycle Configuration is attached to.
*)studio_lifecycle_config_content : studio_lifecycle_config_content option;The content of your Amazon SageMaker AI Studio Lifecycle Configuration script.
*)last_modified_time : timestamp option;This value is equivalent to CreationTime because Amazon SageMaker AI Studio Lifecycle Configurations are immutable.
*)creation_time : timestamp option;The creation time of the Amazon SageMaker AI Studio Lifecycle Configuration.
*)studio_lifecycle_config_name : studio_lifecycle_config_name option;The name of the Amazon SageMaker AI Studio Lifecycle Configuration that is described.
*)studio_lifecycle_config_arn : studio_lifecycle_config_arn option;The ARN of the Lifecycle Configuration to describe.
*)}type nonrec describe_studio_lifecycle_config_request = {studio_lifecycle_config_name : studio_lifecycle_config_name;The name of the Amazon SageMaker AI Studio Lifecycle Configuration to describe.
*)}type nonrec ownership_settings = {owner_user_profile_name : user_profile_name;The user profile who is the owner of the space.
*)}The collection of ownership settings for a space.
type nonrec describe_space_response = {url : string1024 option;Returns the URL of the space. If the space is created with Amazon Web Services IAM Identity Center (Successor to Amazon Web Services Single Sign-On) authentication, users can navigate to the URL after appending the respective redirect parameter for the application type to be federated through Amazon Web Services IAM Identity Center.
The following application types are supported:
&redirect=JupyterServer&redirect=JupyterLab&redirect=CodeEditorspace_display_name : non_empty_string64 option;The name of the space that appears in the Amazon SageMaker Studio UI.
*)space_sharing_settings : space_sharing_settings option;The collection of space sharing settings for a space.
*)ownership_settings : ownership_settings option;The collection of ownership settings for a space.
*)space_settings : space_settings option;A collection of space settings.
*)failure_reason : failure_reason option;The failure reason.
*)creation_time : creation_time option;The creation time.
*)last_modified_time : last_modified_time option;The last modified time.
*)status : space_status option;The status.
*)home_efs_file_system_uid : efs_uid option;The ID of the space's profile in the Amazon EFS volume.
*)space_name : space_name option;The name of the space.
*)space_arn : space_arn option;The space's Amazon Resource Name (ARN).
*)domain_id : domain_id option;The ID of the associated domain.
*)}type nonrec describe_space_request = {space_name : space_name;The name of the space.
*)domain_id : domain_id;The ID of the associated domain.
*)}type nonrec describe_reserved_capacity_response = {ultra_server_summary : ultra_server_summary option;A summary of the UltraServer associated with this reserved capacity.
*)in_use_instance_count : in_use_instance_count option;The number of instances currently in use from this reserved capacity.
*)available_instance_count : available_instance_count option;The number of instances currently available for use in this reserved capacity.
*)total_instance_count : total_instance_count;The total number of instances allocated to this reserved capacity.
*)instance_type : reserved_capacity_instance_type;The Amazon EC2 instance type used in the reserved capacity.
*)end_time : timestamp option;The timestamp when the reserved capacity expires.
*)start_time : timestamp option;The timestamp when the reserved capacity becomes active.
*)duration_minutes : reserved_capacity_duration_minutes option;The number of minutes for the duration of the reserved capacity. For example, if a reserved capacity starts at 08:55 and ends at 11:30, the minutes field would be 35.
*)duration_hours : reserved_capacity_duration_hours option;The total duration of the reserved capacity in hours.
*)availability_zone : availability_zone option;The Availability Zone where the reserved capacity is provisioned.
*)status : reserved_capacity_status option;The current status of the reserved capacity.
*)reserved_capacity_type : reserved_capacity_type option;The type of reserved capacity.
*)reserved_capacity_arn : reserved_capacity_arn;ARN of the reserved capacity.
*)}type nonrec describe_reserved_capacity_request = {reserved_capacity_arn : reserved_capacity_arn;ARN of the reserved capacity to describe.
*)}type nonrec describe_project_output = {last_modified_by : user_context option;last_modified_time : timestamp option;The timestamp when project was last modified.
*)creation_time : timestamp;The time when the project was created.
*)created_by : user_context option;template_provider_details : template_provider_detail_list option;An array of template providers associated with the project.
*)project_status : project_status;The status of the project.
*)service_catalog_provisioned_product_details : service_catalog_provisioned_product_details
option;Information about a provisioned service catalog product.
*)service_catalog_provisioning_details : service_catalog_provisioning_details
option;Information used to provision a service catalog product. For information, see What is Amazon Web Services Service Catalog.
*)project_description : entity_description option;The description of the project.
*)project_id : project_id;The ID of the project.
*)project_name : project_entity_name;The name of the project.
*)project_arn : project_arn;The Amazon Resource Name (ARN) of the project.
*)}type nonrec describe_project_input = {project_name : project_entity_name;The name of the project to describe.
*)}type nonrec describe_processing_job_response = {training_job_arn : training_job_arn option;The ARN of a training job associated with this processing job.
*)auto_ml_job_arn : auto_ml_job_arn option;The ARN of an AutoML job associated with this processing job.
*)monitoring_schedule_arn : monitoring_schedule_arn option;The ARN of a monitoring schedule for an endpoint associated with this processing job.
*)creation_time : timestamp;The time at which the processing job was created.
*)last_modified_time : timestamp option;The time at which the processing job was last modified.
*)processing_start_time : timestamp option;The time at which the processing job started.
*)processing_end_time : timestamp option;The time at which the processing job completed.
*)failure_reason : failure_reason option;A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
*)exit_message : exit_message option;An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
*)processing_job_status : processing_job_status;Provides the status of a processing job.
*)processing_job_arn : processing_job_arn;The Amazon Resource Name (ARN) of the processing job.
*)experiment_config : experiment_config option;The configuration information used to create an experiment.
*)role_arn : role_arn option;The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
*)network_config : network_config option;Networking options for a processing job.
*)environment : processing_environment_map option;The environment variables set in the Docker container.
*)app_specification : app_specification;Configures the processing job to run a specified container image.
*)stopping_condition : processing_stopping_condition option;The time limit for how long the processing job is allowed to run.
*)processing_resources : processing_resources;Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
*)processing_job_name : processing_job_name;The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
*)processing_output_config : processing_output_config option;Output configuration for the processing job.
*)processing_inputs : processing_inputs option;The inputs for a processing job.
*)}type nonrec describe_processing_job_request = {processing_job_name : processing_job_name;The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
*)}type nonrec m_lflow_configuration = {mlflow_experiment_name : mlflow_experiment_entity_name option;The name of the MLflow configuration.
*)mlflow_resource_arn : m_lflow_arn option;The Amazon Resource Name (ARN) of MLflow configuration resource.
*)}The MLflow configuration.
type nonrec describe_pipeline_execution_response = {m_lflow_config : m_lflow_configuration option;The MLflow configuration of the pipeline execution.
*)pipeline_version_id : pipeline_version_id option;The ID of the pipeline version.
*)selective_execution_config : selective_execution_config option;The selective execution configuration applied to the pipeline run.
*)parallelism_configuration : parallelism_configuration option;The parallelism configuration applied to the pipeline.
*)last_modified_by : user_context option;created_by : user_context option;last_modified_time : timestamp option;The time when the pipeline execution was modified last.
*)creation_time : timestamp option;The time when the pipeline execution was created.
*)failure_reason : pipeline_execution_failure_reason option;If the execution failed, a message describing why.
*)pipeline_experiment_config : pipeline_experiment_config option;pipeline_execution_description : pipeline_execution_description option;The description of the pipeline execution.
*)pipeline_execution_status : pipeline_execution_status option;The status of the pipeline execution.
*)pipeline_execution_display_name : pipeline_execution_name option;The display name of the pipeline execution.
*)pipeline_execution_arn : pipeline_execution_arn option;The Amazon Resource Name (ARN) of the pipeline execution.
*)pipeline_arn : pipeline_arn option;The Amazon Resource Name (ARN) of the pipeline.
*)}type nonrec describe_pipeline_execution_request = {pipeline_execution_arn : pipeline_execution_arn;The Amazon Resource Name (ARN) of the pipeline execution.
*)}type nonrec describe_pipeline_definition_for_execution_response = {creation_time : timestamp option;The time when the pipeline was created.
*)pipeline_definition : pipeline_definition option;The JSON pipeline definition.
*)}type nonrec describe_pipeline_definition_for_execution_request = {pipeline_execution_arn : pipeline_execution_arn;The Amazon Resource Name (ARN) of the pipeline execution.
*)}type nonrec describe_pipeline_response = {pipeline_version_description : pipeline_version_description option;The description of the pipeline version.
*)pipeline_version_display_name : pipeline_version_name option;The display name of the pipeline version.
*)parallelism_configuration : parallelism_configuration option;Lists the parallelism configuration applied to the pipeline.
*)last_modified_by : user_context option;created_by : user_context option;last_run_time : timestamp option;The time when the pipeline was last run.
*)last_modified_time : timestamp option;The time when the pipeline was last modified.
*)creation_time : timestamp option;The time when the pipeline was created.
*)pipeline_status : pipeline_status option;The status of the pipeline execution.
*)role_arn : role_arn option;The Amazon Resource Name (ARN) that the pipeline uses to execute.
*)pipeline_description : pipeline_description option;The description of the pipeline.
*)pipeline_definition : pipeline_definition option;The JSON pipeline definition.
*)pipeline_display_name : pipeline_name option;The display name of the pipeline.
*)pipeline_name : pipeline_name option;The name of the pipeline.
*)pipeline_arn : pipeline_arn option;The Amazon Resource Name (ARN) of the pipeline.
*)}type nonrec describe_pipeline_request = {pipeline_version_id : pipeline_version_id option;The ID of the pipeline version to describe.
*)pipeline_name : pipeline_name_or_arn;The name or Amazon Resource Name (ARN) of the pipeline to describe.
*)}type nonrec error_info = {reason : non_empty_string256 option;The failure reason for the operation.
*)code : non_empty_string64 option;The error code for an invalid or failed operation.
*)}This is an error field object that contains the error code and the reason for an operation failure.
type nonrec release_notes_list = string1024 listtype nonrec available_upgrade = {release_notes : release_notes_list option;A list of release notes describing the changes and improvements included in the available upgrade version.
*)version : major_minor_version option;The semantic version number of the available upgrade for the SageMaker Partner AI App.
*)}Contains information about an available upgrade for a SageMaker Partner AI App, including the version number and release notes.
type nonrec describe_partner_app_response = {available_upgrade : available_upgrade option;A map of available minor version upgrades for the SageMaker Partner AI App. The key is the semantic version number, and the value is a list of release notes for that version. A null value indicates no upgrades are available.
*)current_version_eol_date : timestamp option;The end-of-life date for the current version of the SageMaker Partner AI App.
*)enable_auto_minor_version_upgrade : boolean_ option;Indicates whether the SageMaker Partner AI App is configured for automatic minor version upgrades during scheduled maintenance windows.
*)error : error_info option;This is an error field object that contains the error code and the reason for an operation failure.
*)enable_iam_session_based_identity : boolean_ option;When set to TRUE, the SageMaker Partner AI App sets the Amazon Web Services IAM session name or the authenticated IAM user as the identity of the SageMaker Partner AI App user.
auth_type : partner_app_auth_type option;The authorization type that users use to access the SageMaker Partner AI App.
*)application_config : partner_app_config option;Configuration settings for the SageMaker Partner AI App.
*)version : non_empty_string64 option;The version of the SageMaker Partner AI App.
*)tier : non_empty_string64 option;The instance type and size of the cluster attached to the SageMaker Partner AI App.
*)maintenance_config : partner_app_maintenance_config option;Maintenance configuration settings for the SageMaker Partner AI App.
*)base_url : string2048 option;The URL of the SageMaker Partner AI App that the Application SDK uses to support in-app calls for the user.
*)kms_key_id : kms_key_id option;The Amazon Web Services KMS customer managed key used to encrypt the data at rest associated with SageMaker Partner AI Apps.
*)execution_role_arn : role_arn option;The ARN of the IAM role associated with the SageMaker Partner AI App.
*)last_modified_time : timestamp option;The time that the SageMaker Partner AI App was last modified.
*)creation_time : timestamp option;The time that the SageMaker Partner AI App was created.
*)status : partner_app_status option;The status of the SageMaker Partner AI App.
type_ : partner_app_type option;The type of SageMaker Partner AI App. Must be one of the following: lakera-guard, comet, deepchecks-llm-evaluation, or fiddler.
name : partner_app_name option;The name of the SageMaker Partner AI App.
*)arn : partner_app_arn option;The ARN of the SageMaker Partner AI App that was described.
*)}type nonrec describe_partner_app_request = {include_available_upgrade : boolean_ option;When set to TRUE, the response includes available upgrade information for the SageMaker Partner AI App. Default is FALSE.
arn : partner_app_arn;The ARN of the SageMaker Partner AI App to describe.
*)}type nonrec optimization_model_access_config = {accept_eula : optimization_model_accept_eula;Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
}The access configuration settings for the source ML model for an optimization job, where you can accept the model end-user license agreement (EULA).
type nonrec optimization_job_model_source_s3 = {model_access_config : optimization_model_access_config option;The access configuration settings for the source ML model for an optimization job, where you can accept the model end-user license agreement (EULA).
*)s3_uri : s3_uri option;An Amazon S3 URI that locates a source model to optimize with an optimization job.
*)}The Amazon S3 location of a source model to optimize with an optimization job.
type nonrec optimization_sage_maker_model = {model_name : model_name option;The name of a SageMaker model.
*)}A SageMaker model to use as the source or destination for an optimization job.
type nonrec optimization_job_model_source = {sage_maker_model : optimization_sage_maker_model option;The name of an existing SageMaker model to optimize with an optimization job.
*)s3 : optimization_job_model_source_s3 option;The Amazon S3 location of a source model to optimize with an optimization job.
*)}The location of the source model to optimize with an optimization job.
type nonrec optimization_job_environment_variables =
(non_empty_string256 * string256) listtype nonrec model_quantization_config = {override_environment : optimization_job_environment_variables option;Environment variables that override the default ones in the model container.
*)image : optimization_container_image option;The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.
*)}Settings for the model quantization technique that's applied by a model optimization job.
type nonrec model_compilation_config = {override_environment : optimization_job_environment_variables option;Environment variables that override the default ones in the model container.
*)image : optimization_container_image option;The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.
*)}Settings for the model compilation technique that's applied by a model optimization job.
type nonrec model_sharding_config = {override_environment : optimization_job_environment_variables option;Environment variables that override the default ones in the model container.
*)image : optimization_container_image option;The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.
*)}Settings for the model sharding technique that's applied by a model optimization job.
type nonrec model_speculative_decoding_training_data_source = {s3_data_type : model_speculative_decoding_s3_data_type;The type of data stored in the Amazon S3 location. Valid values are S3Prefix or ManifestFile.
s3_uri : s3_uri;The Amazon S3 URI that points to the training data for speculative decoding.
*)}Contains information about the training data source for speculative decoding.
type nonrec model_speculative_decoding_config = {training_data_source : model_speculative_decoding_training_data_source option;The location of the training data to use for speculative decoding. The data must be formatted as ShareGPT, OpenAI Completions or OpenAI Chat Completions. The input can also be unencrypted captured data from a SageMaker endpoint as long as the endpoint uses one of the above formats.
*)technique : model_speculative_decoding_technique;The speculative decoding technique to apply during model optimization.
*)}Settings for the model speculative decoding technique that's applied by a model optimization job.
type nonrec optimization_config = | ModelSpeculativeDecodingConfig of model_speculative_decoding_configSettings for the model speculative decoding technique that's applied by a model optimization job.
*)| ModelShardingConfig of model_sharding_configSettings for the model sharding technique that's applied by a model optimization job.
*)| ModelCompilationConfig of model_compilation_configSettings for the model compilation technique that's applied by a model optimization job.
*)| ModelQuantizationConfig of model_quantization_configSettings for the model quantization technique that's applied by a model optimization job.
*)Settings for an optimization technique that you apply with a model optimization job.
type nonrec optimization_configs = optimization_config listtype nonrec optimization_job_output_config = {sage_maker_model : optimization_sage_maker_model option;The name of a SageMaker model to use as the output destination for an optimization job.
*)s3_output_location : s3_uri;The Amazon S3 URI for where to store the optimized model that you create with an optimization job.
*)kms_key_id : kms_key_id option;The Amazon Resource Name (ARN) of a key in Amazon Web Services KMS. SageMaker uses they key to encrypt the artifacts of the optimized model when SageMaker uploads the model to Amazon S3.
*)}Details for where to store the optimized model that you create with the optimization job.
type nonrec optimization_output = {recommended_inference_image : optimization_container_image option;The image that SageMaker recommends that you use to host the optimized model that you created with an optimization job.
*)}Output values produced by an optimization job.
type nonrec optimization_vpc_security_group_ids =
optimization_vpc_security_group_id listtype nonrec optimization_vpc_subnets = optimization_vpc_subnet_id listtype nonrec optimization_vpc_config = {subnets : optimization_vpc_subnets;The ID of the subnets in the VPC to which you want to connect your optimized model.
*)security_group_ids : optimization_vpc_security_group_ids;The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
}A VPC in Amazon VPC that's accessible to an optimized that you create with an optimization job. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
type nonrec describe_optimization_job_response = {vpc_config : optimization_vpc_config option;A VPC in Amazon VPC that your optimized model has access to.
*)stopping_condition : stopping_condition;role_arn : role_arn;The ARN of the IAM role that you assigned to the optimization job.
*)optimization_output : optimization_output option;Output values produced by an optimization job.
*)output_config : optimization_job_output_config;Details for where to store the optimized model that you create with the optimization job.
*)optimization_configs : optimization_configs;Settings for each of the optimization techniques that the job applies.
*)max_instance_count : optimization_job_max_instance_count option;The maximum number of instances to use for the optimization job.
*)deployment_instance_type : optimization_job_deployment_instance_type;The type of instance that hosts the optimized model that you create with the optimization job.
*)optimization_environment : optimization_job_environment_variables option;The environment variables to set in the model container.
*)model_source : optimization_job_model_source;The location of the source model to optimize with an optimization job.
*)optimization_job_name : entity_name;The name that you assigned to the optimization job.
*)failure_reason : failure_reason option;If the optimization job status is FAILED, the reason for the failure.
last_modified_time : last_modified_time;The time when the optimization job was last updated.
*)creation_time : creation_time;The time when you created the optimization job.
*)optimization_end_time : timestamp option;The time when the optimization job finished processing.
*)optimization_start_time : timestamp option;The time when the optimization job started.
*)optimization_job_status : optimization_job_status;The current status of the optimization job.
*)optimization_job_arn : optimization_job_arn;The Amazon Resource Name (ARN) of the optimization job.
*)}type nonrec describe_optimization_job_request = {optimization_job_name : entity_name;The name that you assigned to the optimization job.
*)}type nonrec describe_notebook_instance_lifecycle_config_output = {creation_time : creation_time option;A timestamp that tells when the lifecycle configuration was created.
*)last_modified_time : last_modified_time option;A timestamp that tells when the lifecycle configuration was last modified.
*)on_start : notebook_instance_lifecycle_config_list option;The shell script that runs every time you start a notebook instance, including when you create the notebook instance.
*)on_create : notebook_instance_lifecycle_config_list option;The shell script that runs only once, when you create a notebook instance.
*)notebook_instance_lifecycle_config_name : notebook_instance_lifecycle_config_name
option;The name of the lifecycle configuration.
*)notebook_instance_lifecycle_config_arn : notebook_instance_lifecycle_config_arn
option;The Amazon Resource Name (ARN) of the lifecycle configuration.
*)}type nonrec describe_notebook_instance_lifecycle_config_input = {notebook_instance_lifecycle_config_name : notebook_instance_lifecycle_config_name;The name of the lifecycle configuration to describe.
*)}type nonrec describe_notebook_instance_output = {instance_metadata_service_configuration : instance_metadata_service_configuration
option;Information on the IMDS configuration of the notebook instance
*)platform_identifier : platform_identifier option;The platform identifier of the notebook instance runtime environment.
*)root_access : root_access option;Whether root access is enabled or disabled for users of the notebook instance.
Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.
*)additional_code_repositories : additional_code_repository_names_or_urls option;An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.
*)default_code_repository : code_repository_name_or_url option;The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.
*)accelerator_types : notebook_instance_accelerator_types option;This parameter is no longer supported. Elastic Inference (EI) is no longer available.
This parameter was used to specify a list of the EI instance types associated with this notebook instance.
*)volume_size_in_g_b : notebook_instance_volume_size_in_g_b option;The size, in GB, of the ML storage volume attached to the notebook instance.
*)direct_internet_access : direct_internet_access option;Describes whether SageMaker AI provides internet access to the notebook instance. If this value is set to Disabled, the notebook instance does not have internet access, and cannot connect to SageMaker AI training and endpoint services.
For more information, see Notebook Instances Are Internet-Enabled by Default.
*)notebook_instance_lifecycle_config_name : notebook_instance_lifecycle_config_name
option;Returns the name of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance
*)creation_time : creation_time option;A timestamp. Use this parameter to return the time when the notebook instance was created
*)last_modified_time : last_modified_time option;A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified.
*)network_interface_id : network_interface_id option;The network interface IDs that SageMaker AI created at the time of creating the instance.
*)kms_key_id : kms_key_id option;The Amazon Web Services KMS key ID SageMaker AI uses to encrypt data when storing it on the ML storage volume attached to the instance.
*)role_arn : role_arn option;The Amazon Resource Name (ARN) of the IAM role associated with the instance.
*)security_groups : security_group_ids option;The IDs of the VPC security groups.
*)subnet_id : subnet_id option;The ID of the VPC subnet.
*)ip_address_type : ip_address_type option;The IP address type configured for the notebook instance. Returns ipv4 for IPv4-only connectivity or dualstack for both IPv4 and IPv6 connectivity.
instance_type : instance_type option;The type of ML compute instance running on the notebook instance.
*)url : notebook_instance_url option;The URL that you use to connect to the Jupyter notebook that is running in your notebook instance.
*)failure_reason : failure_reason option;If status is Failed, the reason it failed.
notebook_instance_status : notebook_instance_status option;The status of the notebook instance.
*)notebook_instance_name : notebook_instance_name option;The name of the SageMaker AI notebook instance.
*)notebook_instance_arn : notebook_instance_arn option;The Amazon Resource Name (ARN) of the notebook instance.
*)}type nonrec describe_notebook_instance_input = {notebook_instance_name : notebook_instance_name;The name of the notebook instance that you want information about.
*)}type nonrec describe_monitoring_schedule_response = {last_monitoring_execution_summary : monitoring_execution_summary option;Describes metadata on the last execution to run, if there was one.
*)endpoint_name : endpoint_name option;The name of the endpoint for the monitoring job.
*)monitoring_schedule_config : monitoring_schedule_config;The configuration object that specifies the monitoring schedule and defines the monitoring job.
*)last_modified_time : timestamp;The time at which the monitoring job was last modified.
*)creation_time : timestamp;The time at which the monitoring job was created.
*)failure_reason : failure_reason option;A string, up to one KB in size, that contains the reason a monitoring job failed, if it failed.
*)monitoring_type : monitoring_type option;The type of the monitoring job that this schedule runs. This is one of the following values.
DATA_QUALITY - The schedule is for a data quality monitoring job.MODEL_QUALITY - The schedule is for a model quality monitoring job.MODEL_BIAS - The schedule is for a bias monitoring job.MODEL_EXPLAINABILITY - The schedule is for an explainability monitoring job.monitoring_schedule_status : schedule_status;The status of an monitoring job.
*)monitoring_schedule_name : monitoring_schedule_name;Name of the monitoring schedule.
*)monitoring_schedule_arn : monitoring_schedule_arn;The Amazon Resource Name (ARN) of the monitoring schedule.
*)}type nonrec describe_monitoring_schedule_request = {monitoring_schedule_name : monitoring_schedule_name;Name of a previously created monitoring schedule.
*)}type nonrec model_quality_baseline_config = {constraints_resource : monitoring_constraints_resource option;baselining_job_name : processing_job_name option;The name of the job that performs baselining for the monitoring job.
*)}Configuration for monitoring constraints and monitoring statistics. These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.
type nonrec model_quality_app_specification = {environment : monitoring_environment_map option;Sets the environment variables in the container that the monitoring job runs.
*)problem_type : monitoring_problem_type option;The machine learning problem type of the model that the monitoring job monitors.
*)post_analytics_processor_source_uri : s3_uri option;An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
*)record_preprocessor_source_uri : s3_uri option;An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
*)container_arguments : monitoring_container_arguments option;An array of arguments for the container used to run the monitoring job.
*)container_entrypoint : container_entrypoint option;Specifies the entrypoint for a container that the monitoring job runs.
*)image_uri : image_uri;The address of the container image that the monitoring job runs.
*)}Container image configuration object for the monitoring job.
type nonrec monitoring_ground_truth_s3_input = {s3_uri : monitoring_s3_uri option;The address of the Amazon S3 location of the ground truth labels.
*)}The ground truth labels for the dataset used for the monitoring job.
type nonrec model_quality_job_input = {ground_truth_s3_input : monitoring_ground_truth_s3_input;The ground truth label provided for the model.
*)batch_transform_input : batch_transform_input option;Input object for the batch transform job.
*)endpoint_input : endpoint_input option;}The input for the model quality monitoring job. Currently endpoints are supported for input for model quality monitoring jobs.
type nonrec monitoring_network_config = {vpc_config : vpc_config option;enable_network_isolation : boolean_ option;Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
*)enable_inter_container_traffic_encryption : boolean_ option;Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.
}The networking configuration for the monitoring job.
type nonrec describe_model_quality_job_definition_response = {stopping_condition : monitoring_stopping_condition option;role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.
*)network_config : monitoring_network_config option;Networking options for a model quality job.
*)job_resources : monitoring_resources;model_quality_job_output_config : monitoring_output_config;model_quality_job_input : model_quality_job_input;Inputs for the model quality job.
*)model_quality_app_specification : model_quality_app_specification;Configures the model quality job to run a specified Docker container image.
*)model_quality_baseline_config : model_quality_baseline_config option;The baseline configuration for a model quality job.
*)creation_time : timestamp;The time at which the model quality job was created.
*)job_definition_name : monitoring_job_definition_name;The name of the quality job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
*)job_definition_arn : monitoring_job_definition_arn;The Amazon Resource Name (ARN) of the model quality job.
*)}type nonrec describe_model_quality_job_definition_request = {job_definition_name : monitoring_job_definition_name;The name of the model quality job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
*)}type nonrec describe_model_package_group_output = {managed_configuration : managed_configuration option;The managed configuration of the model package group.
*)model_package_group_status : model_package_group_status;The status of the model group.
*)created_by : user_context;creation_time : creation_time;The time that the model group was created.
*)model_package_group_description : entity_description option;A description of the model group.
*)model_package_group_arn : model_package_group_arn;The Amazon Resource Name (ARN) of the model group.
*)model_package_group_name : entity_name;The name of the model group.
*)}type nonrec describe_model_package_group_input = {model_package_group_name : arn_or_name;The name of the model group to describe.
*)}type nonrec describe_model_package_output = {managed_storage_type : managed_storage_type option;The storage type of the model package.
*)model_life_cycle : model_life_cycle option;A structure describing the current state of the model in its life cycle.
*)model_card : model_package_model_card option;The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.
When you set IncludedData to MetadataOnly in the request, ModelCardStatus is preserved and ModelCardContent is sanitized to include only the following JSON paths, when present in the model card:
model_overview.model_idmodel_overview.model_nameintended_uses.risk_ratingmodel_package_details.model_package_group_namemodel_package_details.model_package_arnBecause the ModelPackageModelCard schema does not include model_package_details and limits model_overview to model_creator and model_artifact, the sanitized ModelCardContent for a model package typically contains only intended_uses.risk_rating if it was provided when the model card was created. To retrieve the complete ModelCardContent, set IncludedData to AllData or omit the parameter.
security_config : model_package_security_config option;The KMS Key ID (KMSKeyId) used for encryption of model package information.
source_uri : model_package_source_uri option;The URI of the source for the model package.
*)skip_model_validation : skip_model_validation option;Indicates if you want to skip model validation.
*)additional_inference_specifications : additional_inference_specifications
option;An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
*)drift_check_baselines : drift_check_baselines option;Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.
*)customer_metadata_properties : customer_metadata_map option;The metadata properties associated with the model package versions.
*)sample_payload_url : string_ option;The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path points to a single gzip compressed tar archive (.tar.gz suffix).
*)task : string_ option;The machine learning task you specified that your model package accomplishes. Common machine learning tasks include object detection and image classification.
*)domain : string_ option;The machine learning domain of the model package you specified. Common machine learning domains include computer vision and natural language processing.
*)approval_description : approval_description option;A description provided for the model approval.
*)last_modified_by : user_context option;last_modified_time : timestamp option;The last time that the model package was modified.
*)model_metrics : model_metrics option;Metrics for the model.
*)metadata_properties : metadata_properties option;created_by : user_context option;model_approval_status : model_approval_status option;The approval status of the model package.
*)certify_for_marketplace : certify_for_marketplace option;Whether the model package is certified for listing on Amazon Web Services Marketplace.
*)model_package_status_details : model_package_status_details;Details about the current status of the model package.
*)model_package_status : model_package_status;The current status of the model package.
*)validation_specification : model_package_validation_specification option;Configurations for one or more transform jobs that SageMaker runs to test the model package.
*)source_algorithm_specification : source_algorithm_specification option;Details about the algorithm that was used to create the model package.
*)inference_specification : inference_specification option;Details about inference jobs that you can run with models based on this model package.
*)creation_time : creation_time;A timestamp specifying when the model package was created.
*)model_package_description : entity_description option;A brief summary of the model package.
*)model_package_arn : model_package_arn;The Amazon Resource Name (ARN) of the model package.
*)model_package_registration_type : model_package_registration_type option;The package registration type of the model package output.
*)model_package_version : model_package_version option;The version of the model package.
*)model_package_group_name : entity_name option;If the model is a versioned model, the name of the model group that the versioned model belongs to.
*)model_package_name : entity_name;The name of the model package being described.
*)}type nonrec describe_model_package_input = {included_data : included_data option;Specifies the level of model package data to include in the response. Use this parameter to call DescribeModelPackage on a model package that has an associated model card without requiring kms:Decrypt permission on the customer-managed KMS key associated with the embedded model card.
AllData: Returns the full model package response, including the unredacted ModelCard.ModelCardContent. This option requires kms:Decrypt permission on the customer-managed key, if one is associated with the embedded model card. This is the default.MetadataOnly: Returns the full model package response, but with the embedded ModelCard.ModelCardContent sanitized to include only a small set of unencrypted metadata fields. This option does not require kms:Decrypt permission. All other top-level response fields, including InferenceSpecification, ModelMetrics, DriftCheckBaselines, and SecurityConfig, are returned unchanged. For the list of fields preserved within ModelCardContent, see ModelCard.If you don't specify a value, SageMaker returns AllData.
model_package_name : versioned_arn_or_name;The name or Amazon Resource Name (ARN) of the model package to describe.
When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
*)}type nonrec model_explainability_baseline_config = {constraints_resource : monitoring_constraints_resource option;baselining_job_name : processing_job_name option;The name of the baseline model explainability job.
*)}The configuration for a baseline model explainability job.
type nonrec model_explainability_app_specification = {environment : monitoring_environment_map option;Sets the environment variables in the Docker container.
*)config_uri : s3_uri;JSON formatted Amazon S3 file that defines explainability parameters. For more information on this JSON configuration file, see Configure model explainability parameters.
*)image_uri : image_uri;The container image to be run by the model explainability job.
*)}Docker container image configuration object for the model explainability job.
type nonrec model_explainability_job_input = {batch_transform_input : batch_transform_input option;Input object for the batch transform job.
*)endpoint_input : endpoint_input option;}Inputs for the model explainability job.
type nonrec describe_model_explainability_job_definition_response = {stopping_condition : monitoring_stopping_condition option;role_arn : role_arn;The Amazon Resource Name (ARN) of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.
*)network_config : monitoring_network_config option;Networking options for a model explainability job.
*)job_resources : monitoring_resources;model_explainability_job_output_config : monitoring_output_config;model_explainability_job_input : model_explainability_job_input;Inputs for the model explainability job.
*)model_explainability_app_specification : model_explainability_app_specification;Configures the model explainability job to run a specified Docker container image.
*)model_explainability_baseline_config : model_explainability_baseline_config
option;The baseline configuration for a model explainability job.
*)creation_time : timestamp;The time at which the model explainability job was created.
*)job_definition_name : monitoring_job_definition_name;The name of the explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
*)job_definition_arn : monitoring_job_definition_arn;The Amazon Resource Name (ARN) of the model explainability job.
*)}type nonrec describe_model_explainability_job_definition_request = {job_definition_name : monitoring_job_definition_name;The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
*)}type nonrec model_card_export_output_config = {s3_output_path : s3_uri;The Amazon S3 output path to export your model card PDF.
*)}Configure the export output details for an Amazon SageMaker Model Card.
type nonrec model_card_export_artifacts = {s3_export_artifacts : s3_uri;The Amazon S3 URI of the exported model artifacts.
*)}The artifacts of the model card export job.
type nonrec describe_model_card_export_job_response = {export_artifacts : model_card_export_artifacts option;The exported model card artifacts.
*)failure_reason : failure_reason option;The failure reason if the model export job fails.
*)last_modified_at : timestamp;The date and time that the model export job was last modified.
*)created_at : timestamp;The date and time that the model export job was created.
*)output_config : model_card_export_output_config;The export output details for the model card.
*)model_card_version : integer;The version of the model card that the model export job exports.
*)model_card_name : entity_name;The name or Amazon Resource Name (ARN) of the model card that the model export job exports.
*)status : model_card_export_job_status;The completion status of the model card export job.
InProgress: The model card export job is in progress.Completed: The model card export job is complete.Failed: The model card export job failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeModelCardExportJob call.model_card_export_job_arn : model_card_export_job_arn;The Amazon Resource Name (ARN) of the model card export job.
*)model_card_export_job_name : entity_name;The name of the model card export job to describe.
*)}type nonrec describe_model_card_export_job_request = {model_card_export_job_arn : model_card_export_job_arn;The Amazon Resource Name (ARN) of the model card export job to describe.
*)}type nonrec describe_model_card_response = {model_card_processing_status : model_card_processing_status option;The processing status of model card deletion. The ModelCardProcessingStatus updates throughout the different deletion steps.
DeletePending: Model card deletion request received.DeleteInProgress: Model card deletion is in progress.ContentDeleted: Deleted model card content.ExportJobsDeleted: Deleted all export jobs associated with the model card.DeleteCompleted: Successfully deleted the model card.DeleteFailed: The model card failed to delete.last_modified_by : user_context option;last_modified_time : timestamp option;The date and time the model card was last modified.
*)created_by : user_context;creation_time : timestamp;The date and time the model card was created.
*)security_config : model_card_security_config option;The security configuration used to protect model card content.
*)model_card_status : model_card_status;The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
Draft: The model card is a work in progress.PendingReview: The model card is pending review.Approved: The model card is approved.Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.content : model_card_content;The content of the model card. Content is provided as a string in the model card JSON schema.
When you set IncludedData to MetadataOnly in the request, SageMaker returns a sanitized version of Content that includes only the following JSON paths, when present in the model card:
model_overview.model_idmodel_overview.model_nameintended_uses.risk_ratingmodel_package_details.model_package_group_namemodel_package_details.model_package_arnAll other fields are removed from Content when IncludedData is MetadataOnly, including model description, training details, evaluation details, business details, and additional information. To retrieve the complete Content, set IncludedData to AllData or omit the parameter.
model_card_version : integer;The version of the model card.
*)model_card_name : entity_name;The name of the model card.
*)model_card_arn : model_card_arn;The Amazon Resource Name (ARN) of the model card.
*)}type nonrec describe_model_card_request = {included_data : included_data option;Specifies the level of model card data to include in the response. Use this parameter to call DescribeModelCard without requiring kms:Decrypt permission on the customer-managed Amazon Web Services KMS key.
AllData: Returns the full model card Content. This option requires kms:Decrypt permission on the customer-managed key, if one is associated with the model card. This is the default.MetadataOnly: Returns the model card with sanitized Content that includes only a small set of unencrypted metadata fields. This option does not require kms:Decrypt permission. For the list of fields preserved in the response, see Content.If you don't specify a value, SageMaker returns AllData.
model_card_version : integer option;The version of the model card to describe. If a version is not provided, then the latest version of the model card is described.
*)model_card_name : model_card_name_or_arn;The name or Amazon Resource Name (ARN) of the model card to describe.
*)}type nonrec model_bias_baseline_config = {constraints_resource : monitoring_constraints_resource option;baselining_job_name : processing_job_name option;The name of the baseline model bias job.
*)}The configuration for a baseline model bias job.
type nonrec model_bias_app_specification = {environment : monitoring_environment_map option;Sets the environment variables in the Docker container.
*)config_uri : s3_uri;JSON formatted S3 file that defines bias parameters. For more information on this JSON configuration file, see Configure bias parameters.
*)image_uri : image_uri;The container image to be run by the model bias job.
*)}Docker container image configuration object for the model bias job.
type nonrec model_bias_job_input = {ground_truth_s3_input : monitoring_ground_truth_s3_input;Location of ground truth labels to use in model bias job.
*)batch_transform_input : batch_transform_input option;Input object for the batch transform job.
*)endpoint_input : endpoint_input option;}Inputs for the model bias job.
type nonrec describe_model_bias_job_definition_response = {stopping_condition : monitoring_stopping_condition option;role_arn : role_arn;The Amazon Resource Name (ARN) of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.
*)network_config : monitoring_network_config option;Networking options for a model bias job.
*)job_resources : monitoring_resources;model_bias_job_output_config : monitoring_output_config;model_bias_job_input : model_bias_job_input;Inputs for the model bias job.
*)model_bias_app_specification : model_bias_app_specification;Configures the model bias job to run a specified Docker container image.
*)model_bias_baseline_config : model_bias_baseline_config option;The baseline configuration for a model bias job.
*)creation_time : timestamp;The time at which the model bias job was created.
*)job_definition_name : monitoring_job_definition_name;The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
*)job_definition_arn : monitoring_job_definition_arn;The Amazon Resource Name (ARN) of the model bias job.
*)}type nonrec describe_model_bias_job_definition_request = {job_definition_name : monitoring_job_definition_name;The name of the model bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
*)}type nonrec describe_model_output = {deployment_recommendation : deployment_recommendation option;A set of recommended deployment configurations for the model.
*)enable_network_isolation : boolean_ option;If True, no inbound or outbound network calls can be made to or from the model container.
model_arn : model_arn;The Amazon Resource Name (ARN) of the model.
*)creation_time : timestamp;A timestamp that shows when the model was created.
*)vpc_config : vpc_config option;A VpcConfig object that specifies the VPC that this model has access to. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud
*)execution_role_arn : role_arn option;The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
*)inference_execution_config : inference_execution_config option;Specifies details of how containers in a multi-container endpoint are called.
*)containers : container_definition_list option;The containers in the inference pipeline.
*)primary_container : container_definition option;The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.
*)model_name : model_name;Name of the SageMaker model.
*)}type nonrec describe_mlflow_tracking_server_response = {s3_bucket_owner_verification : boolean_ option;Whether Amazon S3 Bucket Ownership checks are enabled whenever the tracking server interacts with Amazon Amazon S3.
*)s3_bucket_owner_account_id : account_id option;Expected Amazon Web Services account ID that owns the Amazon S3 bucket for artifact storage.
*)last_modified_by : user_context option;last_modified_time : timestamp option;The timestamp of when the described MLflow Tracking Server was last modified.
*)created_by : user_context option;creation_time : timestamp option;The timestamp of when the described MLflow Tracking Server was created.
*)automatic_model_registration : boolean_ option;Whether automatic registration of new MLflow models to the SageMaker Model Registry is enabled.
*)weekly_maintenance_window_start : weekly_maintenance_window_start option;The day and time of the week when weekly maintenance occurs on the described tracking server.
*)tracking_server_url : tracking_server_url option;The URL to connect to the MLflow user interface for the described tracking server.
*)is_active : is_tracking_server_active option;Whether the described MLflow Tracking Server is currently active.
*)tracking_server_maintenance_status : tracking_server_maintenance_status option;The current maintenance status of the described MLflow Tracking Server.
*)tracking_server_status : tracking_server_status option;The current creation status of the described MLflow Tracking Server.
*)role_arn : role_arn option;The Amazon Resource Name (ARN) for an IAM role in your account that the described MLflow Tracking Server uses to access the artifact store in Amazon S3.
*)mlflow_version : mlflow_version option;The MLflow version used for the described tracking server.
*)tracking_server_size : tracking_server_size option;The size of the described tracking server.
*)artifact_store_uri : s3_uri option;The S3 URI of the general purpose bucket used as the MLflow Tracking Server artifact store.
*)tracking_server_name : tracking_server_name option;The name of the described tracking server.
*)tracking_server_arn : tracking_server_arn option;The ARN of the described tracking server.
*)}type nonrec describe_mlflow_tracking_server_request = {tracking_server_name : tracking_server_name;The name of the MLflow Tracking Server to describe.
*)}type nonrec describe_mlflow_app_response = {maintenance_status : maintenance_status option;Current maintenance status of the MLflow App.
*)weekly_maintenance_window_start : weekly_maintenance_window_start option;The day and time of the week when weekly maintenance occurs.
*)last_modified_by : user_context option;last_modified_time : timestamp option;The timestamp when the MLflow App was last modified.
*)created_by : user_context option;creation_time : timestamp option;The timestamp when the MLflow App was created.
*)default_domain_id_list : default_domain_id_list option;List of SageMaker Domain IDs for which this MLflow App is the default.
*)account_default_status : account_default_status option;Indicates whether this MLflow app is the default for the entire account.
*)model_registration_mode : model_registration_mode option;Whether automatic registration of new MLflow models to the SageMaker Model Registry is enabled.
*)status : mlflow_app_status option;The current creation status of the described MLflow App.
*)role_arn : role_arn option;The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow App uses to access the artifact store in Amazon S3.
*)mlflow_version : mlflow_version option;The MLflow version used.
*)artifact_store_uri : s3_uri option;The S3 URI of the general purpose bucket used as the MLflow App artifact store.
*)name : mlflow_app_name option;The name of the MLflow App.
*)arn : mlflow_app_arn option;The ARN of the MLflow App.
*)}type nonrec describe_mlflow_app_request = {arn : mlflow_app_arn;The ARN of the MLflow App for which to get information.
*)}type nonrec describe_lineage_group_response = {last_modified_by : user_context option;last_modified_time : timestamp option;The last modified time of the lineage group.
*)created_by : user_context option;creation_time : timestamp option;The creation time of lineage group.
*)description : experiment_description option;The description of the lineage group.
*)display_name : experiment_entity_name option;The display name of the lineage group.
*)lineage_group_arn : lineage_group_arn option;The Amazon Resource Name (ARN) of the lineage group.
*)lineage_group_name : experiment_entity_name option;The name of the lineage group.
*)}type nonrec describe_lineage_group_request = {lineage_group_name : experiment_entity_name;The name of the lineage group.
*)}type nonrec labeling_job_output_config = {sns_topic_arn : sns_topic_arn option;An Amazon Simple Notification Service (Amazon SNS) output topic ARN. Provide a SnsTopicArn if you want to do real time chaining to another streaming job and receive an Amazon SNS notifications each time a data object is submitted by a worker.
If you provide an SnsTopicArn in OutputConfig, when workers complete labeling tasks, Ground Truth will send labeling task output data to the SNS output topic you specify here.
To learn more, see Receive Output Data from a Streaming Labeling Job.
*)kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service ID of the key used to encrypt the output data, if any.
If you provide your own KMS key ID, you must add the required permissions to your KMS key described in Encrypt Output Data and Storage Volume with Amazon Web Services KMS.
If you don't provide a KMS key ID, Amazon SageMaker uses the default Amazon Web Services KMS key for Amazon S3 for your role's account to encrypt your output data.
If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
s3_output_path : s3_uri;The Amazon S3 location to write output data.
*)}Output configuration information for a labeling job.
type nonrec labeling_job_stopping_conditions = {max_percentage_of_input_dataset_labeled : max_percentage_of_input_dataset_labeled
option;The maximum number of input data objects that should be labeled.
*)max_human_labeled_object_count : max_human_labeled_object_count option;The maximum number of objects that can be labeled by human workers.
*)}A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.
Labeling jobs fail after 30 days with an appropriate client error message.
type nonrec labeling_job_resource_config = {vpc_config : vpc_config option;volume_kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training and inference jobs used for automated data labeling.
You can only specify a VolumeKmsKeyId when you create a labeling job with automated data labeling enabled using the API operation CreateLabelingJob. You cannot specify an Amazon Web Services KMS key to encrypt the storage volume used for automated data labeling model training and inference when you create a labeling job using the console. To learn more, see Output Data and Storage Volume Encryption.
The VolumeKmsKeyId can be any of the following formats:
KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
}Configure encryption on the storage volume attached to the ML compute instance used to run automated data labeling model training and inference.
type nonrec labeling_job_algorithms_config = {labeling_job_resource_config : labeling_job_resource_config option;Provides configuration information for a labeling job.
*)initial_active_learning_model_arn : model_arn option;At the end of an auto-label job Ground Truth sends the Amazon Resource Name (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.
*)labeling_job_algorithm_specification_arn : labeling_job_algorithm_specification_arn;Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:
Image classification
arn:aws:sagemaker:{i region}:027400017018:labeling-job-algorithm-specification/image-classificationText classification
arn:aws:sagemaker:{i region}:027400017018:labeling-job-algorithm-specification/text-classificationObject detection
arn:aws:sagemaker:{i region}:027400017018:labeling-job-algorithm-specification/object-detectionSemantic Segmentation
arn:aws:sagemaker:{i region}:027400017018:labeling-job-algorithm-specification/semantic-segmentation}Provides configuration information for auto-labeling of your data objects. A LabelingJobAlgorithmsConfig object must be supplied in order to use auto-labeling.
type nonrec annotation_consolidation_config = {annotation_consolidation_lambda_arn : lambda_function_arn;The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation and to process output data.
For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for AnnotationConsolidationLambdaArn. For custom labeling workflows, see Post-annotation Lambda.
Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBoxarn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBoxarn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBoxarn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBoxarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBoxarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBoxarn:aws:lambda:ap-south-1:565803892007:function:ACS-BoundingBoxarn:aws:lambda:eu-central-1:203001061592:function:ACS-BoundingBoxarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-BoundingBoxarn:aws:lambda:eu-west-2:487402164563:function:ACS-BoundingBoxarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-BoundingBoxarn:aws:lambda:ca-central-1:918755190332:function:ACS-BoundingBoxImage classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassarn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassarn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassarn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassarn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassarn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassarn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassarn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClassMulti-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClassMultiLabelSemantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:ACS-SemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:ACS-SemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-SemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:ACS-SemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-SemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:ACS-SemanticSegmentationText classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassarn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassarn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassarn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassarn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassarn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassarn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassarn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClassMulti-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClassMultiLabelNamed entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognitionarn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognitionarn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognitionarn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognitionarn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognitionarn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognitionarn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognitionVideo Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoMultiClassarn:aws:lambda:us-east-2:266458841044:function:ACS-VideoMultiClassarn:aws:lambda:us-west-2:081040173940:function:ACS-VideoMultiClassarn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoMultiClassarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoMultiClassarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoMultiClassarn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoMultiClassarn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoMultiClassarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoMultiClassarn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoMultiClassarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoMultiClassarn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoMultiClassVideo Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectDetectionarn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectDetectionarn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectDetectionarn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectDetectionarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectDetectionarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectDetectionarn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectDetectionarn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectDetectionarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectDetectionarn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectDetectionarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectDetectionarn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectDetectionVideo Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectTrackingarn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectTrackingarn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectTrackingarn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectTrackingarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectTrackingarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectTrackingarn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectTrackingarn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectTrackingarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectTrackingarn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectTrackingarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectTrackingarn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectTracking3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectDetection3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectTracking3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudSemanticSegmentationUse the following ARNs for Label Verification and Adjustment Jobs
Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels.
Semantic Segmentation Adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentSemanticSegmentationSemantic Segmentation Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationSemanticSegmentationBounding Box Adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentBoundingBoxBounding Box Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationBoundingBoxarn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationBoundingBoxarn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationBoundingBoxarn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationBoundingBoxarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationBoundingBoxarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationBoundingBoxarn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationBoundingBoxarn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationBoundingBoxarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationBoundingBoxarn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationBoundingBoxarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationBoundingBoxarn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationBoundingBoxVideo Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectDetectionVideo Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectTracking3D Point Cloud Object Detection Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects in a 3D point cloud.
arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectDetection3D Point Cloud Object Tracking Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects that appear in a sequence of 3D point cloud frames.
arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectTracking3D Point Cloud Semantic Segmentation Adjustment - Use this task type when you want workers to adjust a point-level semantic segmentation masks using a paint tool.
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudSemanticSegmentationGenerative AI/Custom - Direct passthrough of output data without any transformation.
arn:aws:lambda:us-east-1:432418664414:function:ACS-PassThrougharn:aws:lambda:us-east-2:266458841044:function:ACS-PassThrougharn:aws:lambda:us-west-2:081040173940:function:ACS-PassThrougharn:aws:lambda:eu-west-1:568282634449:function:ACS-PassThrougharn:aws:lambda:ap-northeast-1:477331159723:function:ACS-PassThrougharn:aws:lambda:ap-southeast-2:454466003867:function:ACS-PassThrougharn:aws:lambda:ap-south-1:565803892007:function:ACS-PassThrougharn:aws:lambda:eu-central-1:203001061592:function:ACS-PassThrougharn:aws:lambda:ap-northeast-2:845288260483:function:ACS-PassThrougharn:aws:lambda:eu-west-2:487402164563:function:ACS-PassThrougharn:aws:lambda:ap-southeast-1:377565633583:function:ACS-PassThrougharn:aws:lambda:ca-central-1:918755190332:function:ACS-PassThrough}Configures how labels are consolidated across human workers and processes output data.
type nonrec public_workforce_task_price = {amount_in_usd : us_d option;Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
*)}Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.
Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer.
Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.
Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.
Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars.
Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars.
Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars.
type nonrec human_task_config = {public_workforce_task_price : public_workforce_task_price option;The price that you pay for each task performed by an Amazon Mechanical Turk worker.
*)annotation_consolidation_config : annotation_consolidation_config option;Configures how labels are consolidated across human workers.
*)max_concurrent_task_count : max_concurrent_task_count option;Defines the maximum number of data objects that can be labeled by human workers at the same time. Also referred to as batch size. Each object may have more than one worker at one time. The default value is 1000 objects. To increase the maximum value to 5000 objects, contact Amazon Web Services Support.
*)task_availability_lifetime_in_seconds : task_availability_lifetime_in_seconds
option;The length of time that a task remains available for labeling by human workers. The default and maximum values for this parameter depend on the type of workforce you use.
task_time_limit_in_seconds : task_time_limit_in_seconds;The amount of time that a worker has to complete a task.
If you create a custom labeling job, the maximum value for this parameter is 8 hours (28,800 seconds).
If you create a labeling job using a built-in task type the maximum for this parameter depends on the task type you use:
number_of_human_workers_per_data_object : number_of_human_workers_per_data_object;The number of human workers that will label an object.
*)task_description : task_description;A description of the task for your human workers.
*)task_title : task_title;A title for the task for your human workers.
*)task_keywords : task_keywords option;Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.
*)pre_human_task_lambda_arn : lambda_function_arn option;The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.
For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for PreHumanTaskLambdaArn. For custom labeling workflows, see Pre-annotation Lambda.
Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBoxarn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBoxarn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBoxarn:aws:lambda:ca-central-1:918755190332:function:PRE-BoundingBoxarn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBoxarn:aws:lambda:eu-west-2:487402164563:function:PRE-BoundingBoxarn:aws:lambda:eu-central-1:203001061592:function:PRE-BoundingBoxarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBoxarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-BoundingBoxarn:aws:lambda:ap-south-1:565803892007:function:PRE-BoundingBoxarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-BoundingBoxarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBoxImage classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassarn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClassarn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClassarn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClassarn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClassarn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClassarn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClassarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClassarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClassarn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClassarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClassarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClassMulti-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassMultiLabelarn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClassMultiLabelarn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClassMultiLabelarn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClassMultiLabelarn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClassMultiLabelarn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClassMultiLabelarn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClassMultiLabelarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClassMultiLabelarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClassMultiLabelarn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClassMultiLabelarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClassMultiLabelarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClassMultiLabelSemantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:PRE-SemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:PRE-SemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:PRE-SemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-SemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:PRE-SemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-SemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentationText classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClassarn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClassarn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClassarn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClassarn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClassarn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClassarn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClassarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClassarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClassarn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClassarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClassarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClassMulti-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClassMultiLabelarn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClassMultiLabelarn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClassMultiLabelarn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClassMultiLabelarn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClassMultiLabelarn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClassMultiLabelarn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClassMultiLabelarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClassMultiLabelarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClassMultiLabelarn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClassMultiLabelarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClassMultiLabelarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClassMultiLabelNamed entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognitionarn:aws:lambda:us-east-2:266458841044:function:PRE-NamedEntityRecognitionarn:aws:lambda:us-west-2:081040173940:function:PRE-NamedEntityRecognitionarn:aws:lambda:ca-central-1:918755190332:function:PRE-NamedEntityRecognitionarn:aws:lambda:eu-west-1:568282634449:function:PRE-NamedEntityRecognitionarn:aws:lambda:eu-west-2:487402164563:function:PRE-NamedEntityRecognitionarn:aws:lambda:eu-central-1:203001061592:function:PRE-NamedEntityRecognitionarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-NamedEntityRecognitionarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-NamedEntityRecognitionarn:aws:lambda:ap-south-1:565803892007:function:PRE-NamedEntityRecognitionarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-NamedEntityRecognitionarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-NamedEntityRecognitionVideo Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoMultiClassarn:aws:lambda:us-east-2:266458841044:function:PRE-VideoMultiClassarn:aws:lambda:us-west-2:081040173940:function:PRE-VideoMultiClassarn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoMultiClassarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoMultiClassarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoMultiClassarn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoMultiClassarn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoMultiClassarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoMultiClassarn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoMultiClassarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoMultiClassarn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoMultiClassVideo Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectDetectionarn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectDetectionarn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectDetectionarn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectDetectionarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectDetectionarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectDetectionarn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectDetectionarn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectDetectionarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectDetectionarn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectDetectionarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectDetectionarn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectDetectionVideo Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectTrackingarn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectTrackingarn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectTrackingarn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectTrackingarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectTrackingarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectTrackingarn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectTrackingarn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectTrackingarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectTrackingarn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectTrackingarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectTrackingarn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectTracking3D Point Cloud Modalities
Use the following pre-annotation lambdas for 3D point cloud labeling modality tasks. See 3D Point Cloud Task types to learn more.
3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectDetectionarn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectDetectionarn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectDetectionarn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectDetectionarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectDetectionarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectDetectionarn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectDetectionarn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectDetectionarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectDetectionarn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectDetectionarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectDetectionarn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectDetection3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectTrackingarn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectTrackingarn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectTrackingarn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectTrackingarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectTrackingarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectTrackingarn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectTrackingarn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectTrackingarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectTrackingarn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectTrackingarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectTrackingarn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectTracking3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudSemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudSemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudSemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudSemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudSemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudSemanticSegmentationUse the following ARNs for Label Verification and Adjustment Jobs
Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels.
Bounding box verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationBoundingBoxarn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationBoundingBoxarn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationBoundingBoxarn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationBoundingBoxarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationBoundingBoxarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationBoundingBoxarn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationBoundingBoxarn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationBoundingBoxarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationBoundingBoxarn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationBoundingBoxarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationBoundingBoxarn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationBoundingBoxBounding box adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentBoundingBoxarn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentBoundingBoxarn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentBoundingBoxarn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentBoundingBoxarn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentBoundingBoxarn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentBoundingBoxarn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentBoundingBoxarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentBoundingBoxarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentBoundingBoxarn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentBoundingBoxarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentBoundingBoxarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentBoundingBoxSemantic segmentation verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationSemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationSemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationSemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationSemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationSemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationSemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationSemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationSemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationSemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationSemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationSemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationSemanticSegmentationSemantic segmentation adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentSemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentSemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentSemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentSemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentSemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentSemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentSemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentSemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentSemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentSemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentSemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentSemanticSegmentationVideo Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectDetectionarn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectDetectionarn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectDetectionarn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectDetectionarn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectDetectionarn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectDetectionarn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectDetectionVideo Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectTrackingarn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectTrackingarn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectTrackingarn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectTrackingarn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectTrackingarn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectTrackingarn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectTracking3D point cloud object detection adjustment - Adjust 3D cuboids in a point cloud frame.
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectDetection3D point cloud object tracking adjustment - Adjust 3D cuboids across a sequence of point cloud frames.
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking3D point cloud semantic segmentation adjustment - Adjust semantic segmentation masks in a 3D point cloud.
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudSemanticSegmentationGenerative AI/Custom - Direct passthrough of input data without any transformation.
arn:aws:lambda:us-east-1:432418664414:function:PRE-PassThrougharn:aws:lambda:us-east-2:266458841044:function:PRE-PassThrougharn:aws:lambda:us-west-2:081040173940:function:PRE-PassThrougharn:aws:lambda:ca-central-1:918755190332:function:PRE-PassThrougharn:aws:lambda:eu-west-1:568282634449:function:PRE-PassThrougharn:aws:lambda:eu-west-2:487402164563:function:PRE-PassThrougharn:aws:lambda:eu-central-1:203001061592:function:PRE-PassThrougharn:aws:lambda:ap-northeast-1:477331159723:function:PRE-PassThrougharn:aws:lambda:ap-northeast-2:845288260483:function:PRE-PassThrougharn:aws:lambda:ap-south-1:565803892007:function:PRE-PassThrougharn:aws:lambda:ap-southeast-1:377565633583:function:PRE-PassThrougharn:aws:lambda:ap-southeast-2:454466003867:function:PRE-PassThroughui_config : ui_config;Information about the user interface that workers use to complete the labeling task.
*)workteam_arn : workteam_arn;The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.
*)}Information required for human workers to complete a labeling task.
type nonrec describe_labeling_job_response = {labeling_job_output : labeling_job_output option;The location of the output produced by the labeling job.
*)human_task_config : human_task_config;Configuration information required for human workers to complete a labeling task.
*)labeling_job_algorithms_config : labeling_job_algorithms_config option;Configuration information for automated data labeling.
*)stopping_conditions : labeling_job_stopping_conditions option;A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped.
*)label_category_config_s3_uri : s3_uri option;The S3 location of the JSON file that defines the categories used to label data objects. Please note the following label-category limits:
The file is a JSON structure in the following format:
{
"document-version": "2018-11-28"
"labels": [
{
"label": "{i label 1}" },
{
"label": "{i label 2}" },
...
{
"label": "{i label n}" }
]
}
role_arn : role_arn;The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during data labeling.
*)output_config : labeling_job_output_config;The location of the job's output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
*)input_config : labeling_job_input_config;Input configuration information for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
*)label_attribute_name : label_attribute_name option;The attribute used as the label in the output manifest file.
*)labeling_job_arn : labeling_job_arn;The Amazon Resource Name (ARN) of the labeling job.
*)labeling_job_name : labeling_job_name;The name assigned to the labeling job when it was created.
*)job_reference_code : job_reference_code;A unique identifier for work done as part of a labeling job.
*)last_modified_time : timestamp;The date and time that the labeling job was last updated.
*)creation_time : timestamp;The date and time that the labeling job was created.
*)failure_reason : failure_reason option;If the job failed, the reason that it failed.
*)label_counters : label_counters;Provides a breakdown of the number of data objects labeled by humans, the number of objects labeled by machine, the number of objects than couldn't be labeled, and the total number of objects labeled.
*)labeling_job_status : labeling_job_status;The processing status of the labeling job.
*)}type nonrec describe_labeling_job_request = {labeling_job_name : labeling_job_name;The name of the labeling job to return information for.
*)}type nonrec describe_job_schema_version_response = {job_config_schema : job_config_document;The JSON schema document that defines the structure of the job configuration.
*)job_config_schema_version : job_schema_version;The version of the schema.
*)job_category : job_category;The category of the job schema.
*)}type nonrec describe_job_schema_version_request = {job_config_schema_version : job_schema_version option;The version of the schema to retrieve. If not specified, the latest version is returned.
*)job_category : job_category;The category of the job schema to describe.
*)}type nonrec describe_job_response = {failure_reason : failure_reason option;If the job failed, the reason it failed.
*)secondary_status_transitions : job_secondary_status_transitions;A list of secondary status transitions for the job, with timestamps and optional status messages.
*)secondary_status : job_secondary_status;The detailed secondary status of the job, providing more granular information about the job's progress. Secondary statuses may change between releases.
*)job_status : job_status;The current status of the job.
*)end_time : timestamp option;The date and time that the job ended.
*)last_modified_time : timestamp;The date and time that the job was last modified.
*)creation_time : timestamp;The date and time that the job was created.
*)job_config_document : job_config_document option;The JSON configuration document for the job.
*)job_config_schema_version : job_schema_version;The schema version used for the job configuration document.
*)job_category : job_category;The category of the job.
*)role_arn : role_arn;The ARN of the IAM role associated with the job.
*)job_arn : job_arn;The Amazon Resource Name (ARN) of the job.
*)job_name : job_name;The name of the job.
*)}type nonrec describe_job_request = {job_category : job_category;The category of the job.
*)job_name : job_name;The name of the job to describe.
*)}type nonrec recommendation_job_resource_limit = {max_parallel_of_tests : max_parallel_of_tests option;Defines the maximum number of parallel load tests.
*)max_number_of_tests : max_number_of_tests option;Defines the maximum number of load tests.
*)}Specifies the maximum number of jobs that can run in parallel and the maximum number of jobs that can run.
type nonrec categorical_parameter_range_values = string128 listtype nonrec categorical_parameter = {value : categorical_parameter_range_values;The list of values you can pass.
*)name : string64;The Name of the environment variable.
*)}Environment parameters you want to benchmark your load test against.
type nonrec categorical_parameters = categorical_parameter listtype nonrec environment_parameter_ranges = {categorical_parameter_ranges : categorical_parameters option;Specified a list of parameters for each category.
*)}Specifies the range of environment parameters
type nonrec endpoint_input_configuration = {environment_parameter_ranges : environment_parameter_ranges option;The parameter you want to benchmark against.
*)inference_specification_name : inference_specification_name option;The inference specification name in the model package version.
*)serverless_config : production_variant_serverless_config option;instance_type : production_variant_instance_type option;The instance types to use for the load test.
*)}The endpoint configuration for the load test.
type nonrec endpoint_input_configurations = endpoint_input_configuration listtype nonrec recommendation_job_supported_content_types =
recommendation_job_supported_content_type listtype nonrec recommendation_job_payload_config = {supported_content_types : recommendation_job_supported_content_types option;The supported MIME types for the input data.
*)sample_payload_url : s3_uri option;The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
*)}The configuration for the payload for a recommendation job.
type nonrec recommendation_job_supported_instance_types = string_ listtype nonrec recommendation_job_supported_response_mime_types =
recommendation_job_supported_response_mime_type listtype nonrec recommendation_job_container_config = {supported_response_mime_types : recommendation_job_supported_response_mime_types
option;The supported MIME types for the output data.
*)data_input_config : recommendation_job_data_input_config option;Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. This field is used for optimizing your model using SageMaker Neo. For more information, see DataInputConfig.
*)supported_endpoint_type : recommendation_job_supported_endpoint_type option;The endpoint type to receive recommendations for. By default this is null, and the results of the inference recommendation job return a combined list of both real-time and serverless benchmarks. By specifying a value for this field, you can receive a longer list of benchmarks for the desired endpoint type.
*)supported_instance_types : recommendation_job_supported_instance_types option;A list of the instance types that are used to generate inferences in real-time.
*)nearest_model_name : string_ option;The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model.
Valid Values: efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet
payload_config : recommendation_job_payload_config option;Specifies the SamplePayloadUrl and all other sample payload-related fields.
framework_version : recommendation_job_framework_version option;The framework version of the container image.
*)framework : string_ option;The machine learning framework of the container image.
Valid Values: TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN
task : string_ option;The machine learning task that the model accomplishes.
Valid Values: IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION | REGRESSION | OTHER
domain : string_ option;The machine learning domain of the model and its components.
Valid Values: COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING
}Specifies mandatory fields for running an Inference Recommender job directly in the CreateInferenceRecommendationsJob API. The fields specified in ContainerConfig override the corresponding fields in the model package. Use ContainerConfig if you want to specify these fields for the recommendation job but don't want to edit them in your model package.
type nonrec endpoint_info = {endpoint_name : endpoint_name option;The name of a customer's endpoint.
*)}Details about a customer endpoint that was compared in an Inference Recommender job.
type nonrec endpoints = endpoint_info listtype nonrec recommendation_job_vpc_security_group_ids =
recommendation_job_vpc_security_group_id listtype nonrec recommendation_job_vpc_subnets =
recommendation_job_vpc_subnet_id listtype nonrec recommendation_job_vpc_config = {subnets : recommendation_job_vpc_subnets;The ID of the subnets in the VPC to which you want to connect your model.
*)security_group_ids : recommendation_job_vpc_security_group_ids;The VPC security group IDs. IDs have the form of sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
}Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job.
type nonrec recommendation_job_input_config = {vpc_config : recommendation_job_vpc_config option;Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job.
*)endpoints : endpoints option;Existing customer endpoints on which to run an Inference Recommender job.
*)container_config : recommendation_job_container_config option;Specifies mandatory fields for running an Inference Recommender job. The fields specified in ContainerConfig override the corresponding fields in the model package.
volume_kms_key_id : kms_key_id option;The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. This key will be passed to SageMaker Hosting for endpoint creation.
The SageMaker execution role must have kms:CreateGrant permission in order to encrypt data on the storage volume of the endpoints created for inference recommendation. The inference recommendation job will fail asynchronously during endpoint configuration creation if the role passed does not have kms:CreateGrant permission.
The KmsKeyId can be any of the following formats:
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"
// KMS Key Alias
"alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"
For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
*)endpoint_configurations : endpoint_input_configurations option;Specifies the endpoint configuration to use for a job.
*)resource_limit : recommendation_job_resource_limit option;Defines the resource limit of the job.
*)traffic_pattern : traffic_pattern option;Specifies the traffic pattern of the job.
*)job_duration_in_seconds : job_duration_in_seconds option;Specifies the maximum duration of the job, in seconds. The maximum value is 18,000 seconds.
*)model_name : model_name option;The name of the created model.
*)model_package_version_arn : model_package_arn option;The Amazon Resource Name (ARN) of a versioned model package.
*)}The input configuration of the recommendation job.
type nonrec model_latency_threshold = {value_in_milliseconds : integer option;The model latency percentile value in milliseconds.
*)percentile : string64 option;The model latency percentile threshold. Acceptable values are P95 and P99. For custom load tests, specify the value as P95.
}The model latency threshold.
type nonrec model_latency_thresholds = model_latency_threshold listtype nonrec recommendation_job_stopping_conditions = {flat_invocations : flat_invocations option;Stops a load test when the number of invocations (TPS) peaks and flattens, which means that the instance has reached capacity. The default value is Stop. If you want the load test to continue after invocations have flattened, set the value to Continue.
model_latency_thresholds : model_latency_thresholds option;The interval of time taken by a model to respond as viewed from SageMaker. The interval includes the local communication time taken to send the request and to fetch the response from the container of a model and the time taken to complete the inference in the container.
*)max_invocations : integer option;The maximum number of requests per minute expected for the endpoint.
*)}Specifies conditions for stopping a job. When a job reaches a stopping condition limit, SageMaker ends the job.
type nonrec inference_recommendation = {invocation_start_time : invocation_start_time option;A timestamp that shows when the benchmark started.
*)invocation_end_time : invocation_end_time option;A timestamp that shows when the benchmark completed.
*)model_configuration : model_configuration;Defines the model configuration.
*)endpoint_configuration : endpoint_output_configuration;Defines the endpoint configuration parameters.
*)metrics : recommendation_metrics option;The metrics used to decide what recommendation to make.
*)recommendation_id : string_ option;The recommendation ID which uniquely identifies each recommendation.
*)}A list of recommendations made by Amazon SageMaker Inference Recommender.
type nonrec inference_recommendations = inference_recommendation listtype nonrec endpoint_performance = {endpoint_info : endpoint_info;metrics : inference_metrics;The metrics for an existing endpoint.
*)}The performance results from running an Inference Recommender job on an existing endpoint.
type nonrec endpoint_performances = endpoint_performance listtype nonrec describe_inference_recommendations_job_response = {endpoint_performances : endpoint_performances option;The performance results from running an Inference Recommender job on an existing endpoint.
*)inference_recommendations : inference_recommendations option;The recommendations made by Inference Recommender.
*)stopping_conditions : recommendation_job_stopping_conditions option;The stopping conditions that you provided when you initiated the job.
*)input_config : recommendation_job_input_config;Returns information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations you provided when you initiated the job.
*)failure_reason : failure_reason option;If the job fails, provides information why the job failed.
*)last_modified_time : last_modified_time;A timestamp that shows when the job was last modified.
*)completion_time : timestamp option;A timestamp that shows when the job completed.
*)creation_time : creation_time;A timestamp that shows when the job was created.
*)status : recommendation_job_status;The status of the job.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role you provided when you initiated the job.
*)job_arn : recommendation_job_arn;The Amazon Resource Name (ARN) of the job.
*)job_type : recommendation_job_type;The job type that you provided when you initiated the job.
*)job_description : recommendation_job_description option;The job description that you provided when you initiated the job.
*)job_name : recommendation_job_name;The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
*)}type nonrec describe_inference_recommendations_job_request = {job_name : recommendation_job_name;The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
*)}type nonrec endpoint_metadata = {failure_reason : failure_reason option;If the status of the endpoint is Failed, or the status is InService but update operation fails, this provides the reason why it failed.
endpoint_status : endpoint_status option;The status of the endpoint. For possible values of the status of an endpoint, see EndpointSummary.
*)endpoint_config_name : endpoint_config_name option;The name of the endpoint configuration.
*)endpoint_name : endpoint_name;The name of the endpoint.
*)}The metadata of the endpoint.
type nonrec model_variant_config_summary = {status : model_variant_status;The status of deployment for the model variant on the hosted inference endpoint.
Creating - Amazon SageMaker is preparing the model variant on the hosted inference endpoint.InService - The model variant is running on the hosted inference endpoint.Updating - Amazon SageMaker is updating the model variant on the hosted inference endpoint.Deleting - Amazon SageMaker is deleting the model variant on the hosted inference endpoint.Deleted - The model variant has been deleted on the hosted inference endpoint. This can only happen after stopping the experiment.infrastructure_config : model_infrastructure_config;The configuration of the infrastructure that the model has been deployed to.
*)variant_name : model_variant_name;The name of the variant.
*)model_name : model_name;The name of the Amazon SageMaker Model entity.
*)}Summary of the deployment configuration of a model.
type nonrec model_variant_config_summary_list =
model_variant_config_summary listtype nonrec describe_inference_experiment_response = {kms_key : kms_key_id option;The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. For more information, see CreateInferenceExperiment.
*)shadow_mode_config : shadow_mode_config option;The configuration of ShadowMode inference experiment type, which shows the production variant that takes all the inference requests, and the shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant it also shows the percentage of requests that Amazon SageMaker replicates.
data_storage_config : inference_experiment_data_storage_config option;The Amazon S3 location and configuration for storing inference request and response data.
*)model_variants : model_variant_config_summary_list;An array of ModelVariantConfigSummary objects. There is one for each variant in the inference experiment. Each ModelVariantConfigSummary object in the array describes the infrastructure configuration for deploying the corresponding variant.
endpoint_metadata : endpoint_metadata;The metadata of the endpoint on which the inference experiment ran.
*)role_arn : role_arn option;The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
*)last_modified_time : timestamp option;The timestamp at which you last modified the inference experiment.
*)completion_time : timestamp option;The timestamp at which the inference experiment was completed.
*)creation_time : timestamp option;The timestamp at which you created the inference experiment.
*)description : inference_experiment_description option;The description of the inference experiment.
*)status_reason : inference_experiment_status_reason option;The error message or client-specified Reason from the StopInferenceExperiment API, that explains the status of the inference experiment.
status : inference_experiment_status;The status of the inference experiment. The following are the possible statuses for an inference experiment:
Creating - Amazon SageMaker is creating your experiment.Created - Amazon SageMaker has finished the creation of your experiment and will begin the experiment at the scheduled time.Updating - When you make changes to your experiment, your experiment shows as updating.Starting - Amazon SageMaker is beginning your experiment.Running - Your experiment is in progress.Stopping - Amazon SageMaker is stopping your experiment.Completed - Your experiment has completed.Cancelled - When you conclude your experiment early using the StopInferenceExperiment API, or if any operation fails with an unexpected error, it shows as cancelled.schedule : inference_experiment_schedule option;The duration for which the inference experiment ran or will run.
*)type_ : inference_experiment_type;The type of the inference experiment.
*)name : inference_experiment_name;The name of the inference experiment.
*)arn : inference_experiment_arn;The ARN of the inference experiment being described.
*)}type nonrec describe_inference_experiment_request = {name : inference_experiment_name;The name of the inference experiment to describe.
*)}type nonrec inference_component_container_specification_summary = {container_metrics_config : container_metrics_config option;The container metrics scraping configuration for this inference component, including the metrics endpoint path and publishing frequency.
*)environment : environment_map option;The environment variables to set in the Docker container.
*)artifact_url : url option;The Amazon S3 path where the model artifacts are stored.
*)deployed_image : deployed_image option;}Details about the resources that are deployed with this inference component.
type nonrec inference_component_data_cache_config_summary = {enable_caching : enable_caching;Indicates whether the inference component caches model artifacts as part of the auto scaling process.
*)}Settings that affect how the inference component caches data.
type nonrec inference_component_specification_summary = {scheduling_config : inference_component_scheduling_config option;The scheduling configuration that determines how inference component copies are placed across available instances when copies are added or removed.
*)data_cache_config : inference_component_data_cache_config_summary option;Settings that affect how the inference component caches data.
*)base_inference_component_name : inference_component_name option;The name of the base inference component that contains this inference component.
*)compute_resource_requirements : inference_component_compute_resource_requirements
option;The compute resources allocated to run the model, plus any adapter models, that you assign to the inference component.
*)startup_parameters : inference_component_startup_parameters option;Settings that take effect while the model container starts up.
*)container : inference_component_container_specification_summary option;Details about the container that provides the runtime environment for the model that is deployed with the inference component.
*)model_name : model_name option;The name of the SageMaker AI model object that is deployed with the inference component.
*)instance_type : production_variant_instance_type option;The ML compute instance type associated with this inference component specification.
*)}Details about the resources that are deployed with this inference component.
type nonrec inference_component_specification_summary_list =
inference_component_specification_summary listtype nonrec inference_component_placement_status = {current_copy_count : inference_component_copy_count;The number of inference component copies currently placed on instances of this type.
*)instance_type : production_variant_instance_type;The ML compute instance type where the inference component copies are placed.
*)}The placement status of an inference component on a specific instance type. Shows the number of inference component copies currently placed on instances of a given type.
type nonrec inference_component_placement_status_list =
inference_component_placement_status listtype nonrec inference_component_runtime_config_summary = {placement_status : inference_component_placement_status_list option;The placement status of the inference component across instance types. Shows how the inference component copies are distributed across instance types.
*)current_copy_count : inference_component_copy_count option;The number of runtime copies of the model container that are currently deployed.
*)desired_copy_count : inference_component_copy_count option;The number of runtime copies of the model container that you requested to deploy with the inference component.
*)}Details about the runtime settings for the model that is deployed with the inference component.
type nonrec describe_inference_component_output = {last_deployment_config : inference_component_deployment_config option;The deployment and rollback settings that you assigned to the inference component.
*)inference_component_status : inference_component_status option;The status of the inference component.
*)last_modified_time : timestamp;The time when the inference component was last updated.
*)creation_time : timestamp;The time when the inference component was created.
*)runtime_config : inference_component_runtime_config_summary option;Details about the runtime settings for the model that is deployed with the inference component.
*)specifications : inference_component_specification_summary_list option;A list of specification summaries for the inference component, one per instance type. This parameter is populated when the inference component was created with multiple specifications. When this parameter is populated, the singular Specification parameter is not returned.
specification : inference_component_specification_summary option;Details about the resources that are deployed with this inference component.
*)failure_reason : failure_reason option;If the inference component status is Failed, the reason for the failure.
variant_name : variant_name option;The name of the production variant that hosts the inference component.
*)endpoint_arn : endpoint_arn;The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.
*)endpoint_name : endpoint_name;The name of the endpoint that hosts the inference component.
*)inference_component_arn : inference_component_arn;The Amazon Resource Name (ARN) of the inference component.
*)inference_component_name : inference_component_name;The name of the inference component.
*)}type nonrec describe_inference_component_input = {inference_component_name : inference_component_name;The name of the inference component.
*)}type nonrec describe_image_version_response = {release_notes : release_notes option;The maintainer description of the image version.
*)horovod : horovod option;Indicates Horovod compatibility.
*)processor : processor option;Indicates CPU or GPU compatibility.
CPU: The image version is compatible with CPU.GPU: The image version is compatible with GPU.programming_lang : programming_lang option;The supported programming language and its version.
*)ml_framework : ml_framework option;The machine learning framework vended in the image version.
*)job_type : job_type option;Indicates SageMaker AI job type compatibility.
TRAINING: The image version is compatible with SageMaker AI training jobs.INFERENCE: The image version is compatible with SageMaker AI inference jobs.NOTEBOOK_KERNEL: The image version is compatible with SageMaker AI notebook kernels.vendor_guidance : vendor_guidance option;The stability of the image version specified by the maintainer.
NOT_PROVIDED: The maintainers did not provide a status for image version stability.STABLE: The image version is stable.TO_BE_ARCHIVED: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months.ARCHIVED: The image version is archived. Archived image versions are not searchable and are no longer actively supported.version : image_version_number option;The version number.
*)last_modified_time : timestamp option;When the version was last modified.
*)image_version_status : image_version_status option;The status of the version.
*)image_version_arn : image_version_arn option;The ARN of the version.
*)image_arn : image_arn option;The ARN of the image the version is based on.
*)failure_reason : failure_reason option;When a create or delete operation fails, the reason for the failure.
*)creation_time : timestamp option;When the version was created.
*)container_image : image_container_image option;The registry path of the container image that contains this image version.
*)base_image : image_base_image option;The registry path of the container image on which this image version is based.
*)}type nonrec describe_image_version_request = {alias : sage_maker_image_version_alias option;The alias of the image version.
*)version : image_version_number option;The version of the image. If not specified, the latest version is described.
*)image_name : image_name;The name of the image.
*)}type nonrec describe_image_response = {role_arn : role_arn option;The ARN of the IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.
*)last_modified_time : timestamp option;When the image was last modified.
*)image_status : image_status option;The status of the image.
*)image_name : image_name option;The name of the image.
*)image_arn : image_arn option;The ARN of the image.
*)failure_reason : failure_reason option;When a create, update, or delete operation fails, the reason for the failure.
*)display_name : image_display_name option;The name of the image as displayed.
*)description : image_description option;The description of the image.
*)creation_time : timestamp option;When the image was created.
*)}type nonrec describe_image_request = {image_name : image_name;The name of the image to describe.
*)}A flag to indicate if you want to use Autotune to automatically find optimal values for the following fields:
type nonrec describe_hyper_parameter_tuning_job_response = {consumed_resources : hyper_parameter_tuning_job_consumed_resources option;tuning_job_completion_details : hyper_parameter_tuning_job_completion_details
option;Tuning job completion information returned as the response from a hyperparameter tuning job. This information tells if your tuning job has or has not converged. It also includes the number of training jobs that have not improved model performance as evaluated against the objective function.
*)failure_reason : failure_reason option;If the tuning job failed, the reason it failed.
*)autotune : autotune option;A flag to indicate if autotune is enabled for the hyperparameter tuning job.
*)warm_start_config : hyper_parameter_tuning_job_warm_start_config option;The configuration for starting the hyperparameter parameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
*)overall_best_training_job : hyper_parameter_training_job_summary option;If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.
best_training_job : hyper_parameter_training_job_summary option;A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.
*)objective_status_counters : objective_status_counters;The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.
*)training_job_status_counters : training_job_status_counters;The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.
*)last_modified_time : timestamp option;The date and time that the status of the tuning job was modified.
*)hyper_parameter_tuning_end_time : timestamp option;The date and time that the tuning job ended.
*)creation_time : timestamp;The date and time that the tuning job started.
*)hyper_parameter_tuning_job_status : hyper_parameter_tuning_job_status;The status of the tuning job.
*)training_job_definitions : hyper_parameter_training_job_definitions option;training_job_definition : hyper_parameter_training_job_definition option;The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.
*)hyper_parameter_tuning_job_config : hyper_parameter_tuning_job_config;hyper_parameter_tuning_job_arn : hyper_parameter_tuning_job_arn;The Amazon Resource Name (ARN) of the tuning job.
*)hyper_parameter_tuning_job_name : hyper_parameter_tuning_job_name;The name of the hyperparameter tuning job.
*)}type nonrec describe_hyper_parameter_tuning_job_request = {hyper_parameter_tuning_job_name : hyper_parameter_tuning_job_name;The name of the tuning job.
*)}type nonrec describe_human_task_ui_response = {ui_template : ui_template_info;creation_time : timestamp;The timestamp when the human task user interface was created.
*)human_task_ui_status : human_task_ui_status option;The status of the human task user interface (worker task template). Valid values are listed below.
*)human_task_ui_name : human_task_ui_name;The name of the human task user interface (worker task template).
*)human_task_ui_arn : human_task_ui_arn;The Amazon Resource Name (ARN) of the human task user interface (worker task template).
*)}type nonrec describe_human_task_ui_request = {human_task_ui_name : human_task_ui_name;The name of the human task user interface (worker task template) you want information about.
*)}type nonrec hub_content_dependency = {dependency_copy_path : dependency_copy_path option;The hub content dependency copy path.
*)dependency_origin_path : dependency_origin_path option;The hub content dependency origin path.
*)}Any dependencies related to hub content, such as scripts, model artifacts, datasets, or notebooks.
type nonrec hub_content_dependency_list = hub_content_dependency listtype nonrec describe_hub_content_response = {last_modified_time : timestamp option;The last modified time of the hub content.
*)creation_time : timestamp;The date and time that hub content was created.
*)failure_reason : failure_reason option;The failure reason if importing hub content failed.
*)hub_content_status : hub_content_status;The status of the hub content.
*)hub_content_dependencies : hub_content_dependency_list option;The location of any dependencies that the hub content has, such as scripts, model artifacts, datasets, or notebooks.
*)hub_content_search_keywords : hub_content_search_keyword_list option;The searchable keywords for the hub content.
*)support_status : hub_content_support_status option;The support status of the hub content.
*)reference_min_version : reference_min_version option;The minimum version of the hub content.
*)sage_maker_public_hub_content_arn : sage_maker_public_hub_content_arn option;The ARN of the public hub content.
*)hub_content_document : hub_content_document;The hub content document that describes information about the hub content such as type, associated containers, scripts, and more.
*)hub_content_markdown : hub_content_markdown option;A string that provides a description of the hub content. This string can include links, tables, and standard markdown formating.
*)hub_content_description : hub_content_description option;A description of the hub content.
*)hub_content_display_name : hub_content_display_name option;The display name of the hub content.
*)hub_arn : hub_arn;The Amazon Resource Name (ARN) of the hub that contains the content.
*)hub_name : hub_name;The name of the hub that contains the content.
*)document_schema_version : document_schema_version;The document schema version for the hub content.
*)hub_content_type : hub_content_type;The type of hub content.
*)hub_content_version : hub_content_version;The version of the hub content.
*)hub_content_arn : hub_content_arn;The Amazon Resource Name (ARN) of the hub content.
*)hub_content_name : hub_content_name;The name of the hub content.
*)}type nonrec describe_hub_content_request = {hub_content_version : hub_content_version option;The version of the content to describe.
*)hub_content_name : hub_content_name;The name of the content to describe.
*)hub_content_type : hub_content_type;The type of content in the hub.
*)hub_name : hub_name_or_arn;The name of the hub that contains the content to describe.
*)}type nonrec hub_s3_storage_config = {s3_output_path : s3_output_path option;The Amazon S3 bucket prefix for hosting hub content.
*)}The Amazon S3 storage configuration of a hub.
type nonrec describe_hub_response = {last_modified_time : timestamp;The date and time that the hub was last modified.
*)creation_time : timestamp;The date and time that the hub was created.
*)failure_reason : failure_reason option;The failure reason if importing hub content failed.
*)hub_status : hub_status;The status of the hub.
*)s3_storage_config : hub_s3_storage_config option;The Amazon S3 storage configuration for the hub.
*)hub_search_keywords : hub_search_keyword_list option;The searchable keywords for the hub.
*)hub_description : hub_description option;A description of the hub.
*)hub_display_name : hub_display_name option;The display name of the hub.
*)hub_arn : hub_arn;The Amazon Resource Name (ARN) of the hub.
*)hub_name : hub_name;The name of the hub.
*)}type nonrec describe_hub_request = {hub_name : hub_name_or_arn;The name of the hub to describe.
*)}type nonrec human_loop_request_source = {aws_managed_human_loop_request_source : aws_managed_human_loop_request_source;Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:
*)}Container for configuring the source of human task requests.
type nonrec human_loop_activation_conditions_config = {human_loop_activation_conditions : human_loop_activation_conditions;JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the Amazon SageMaker Developer Guide.
*)}Defines under what conditions SageMaker creates a human loop. Used within CreateFlowDefinition. See HumanLoopActivationConditionsConfig for the required format of activation conditions.
type nonrec human_loop_activation_config = {human_loop_activation_conditions_config : human_loop_activation_conditions_config;Container structure for defining under what conditions SageMaker creates a human loop.
*)}Provides information about how and under what conditions SageMaker creates a human loop. If HumanLoopActivationConfig is not given, then all requests go to humans.
type nonrec flow_definition_task_keywords = flow_definition_task_keyword listtype nonrec human_loop_config = {public_workforce_task_price : public_workforce_task_price option;task_keywords : flow_definition_task_keywords option;Keywords used to describe the task so that workers can discover the task.
*)task_time_limit_in_seconds : flow_definition_task_time_limit_in_seconds option;The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour).
*)task_availability_lifetime_in_seconds : flow_definition_task_availability_lifetime_in_seconds
option;The length of time that a task remains available for review by human workers.
*)task_count : flow_definition_task_count;The number of distinct workers who will perform the same task on each object. For example, if TaskCount is set to 3 for an image classification labeling job, three workers will classify each input image. Increasing TaskCount can improve label accuracy.
task_description : flow_definition_task_description;A description for the human worker task.
*)task_title : flow_definition_task_title;A title for the human worker task.
*)human_task_ui_arn : human_task_ui_arn;The Amazon Resource Name (ARN) of the human task user interface.
You can use standard HTML and Crowd HTML Elements to create a custom worker task template. You use this template to create a human task UI.
To learn how to create a custom HTML template, see Create Custom Worker Task Template.
To learn how to create a human task UI, which is a worker task template that can be used in a flow definition, see Create and Delete a Worker Task Templates.
*)workteam_arn : workteam_arn;Amazon Resource Name (ARN) of a team of workers. To learn more about the types of workforces and work teams you can create and use with Amazon A2I, see Create and Manage Workforces.
*)}Describes the work to be performed by human workers.
type nonrec flow_definition_output_config = {kms_key_id : kms_key_id option;The Amazon Key Management Service (KMS) key ID for server-side encryption.
*)s3_output_path : s3_uri;The Amazon S3 path where the object containing human output will be made available.
To learn more about the format of Amazon A2I output data, see Amazon A2I Output Data.
*)}Contains information about where human output will be stored.
type nonrec describe_flow_definition_response = {failure_reason : failure_reason option;The reason your flow definition failed.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) execution role for the flow definition.
*)output_config : flow_definition_output_config;An object containing information about the output file.
*)human_loop_config : human_loop_config option;An object containing information about who works on the task, the workforce task price, and other task details.
*)human_loop_activation_config : human_loop_activation_config option;An object containing information about what triggers a human review workflow.
*)human_loop_request_source : human_loop_request_source option;Container for configuring the source of human task requests. Used to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
*)creation_time : timestamp;The timestamp when the flow definition was created.
*)flow_definition_status : flow_definition_status;The status of the flow definition. Valid values are listed below.
*)flow_definition_name : flow_definition_name;The Amazon Resource Name (ARN) of the flow definition.
*)flow_definition_arn : flow_definition_arn;The Amazon Resource Name (ARN) of the flow defintion.
*)}type nonrec describe_flow_definition_request = {flow_definition_name : flow_definition_name;The name of the flow definition.
*)}type nonrec describe_feature_metadata_response = {parameters : feature_parameters option;The key-value pairs that you added to describe the feature.
*)description : feature_description option;The description you added to describe the feature.
*)last_modified_time : last_modified_time;A timestamp indicating when the metadata for the feature group was modified. For example, if you add a parameter describing the feature, the timestamp changes to reflect the last time you
*)creation_time : creation_time;A timestamp indicating when the feature was created.
*)feature_type : feature_type;The data type of the feature.
*)feature_name : feature_name;The name of the feature that you've specified.
*)feature_group_name : feature_group_name;The name of the feature group that you've specified.
*)feature_group_arn : feature_group_arn;The Amazon Resource Number (ARN) of the feature group that contains the feature.
*)}type nonrec describe_feature_metadata_request = {feature_name : feature_name;The name of the feature.
*)feature_group_name : feature_group_name_or_arn;The name or Amazon Resource Name (ARN) of the feature group containing the feature.
*)}type nonrec online_store_total_size_bytes = Smaws_Lib.CoreTypes.Int64.ttype nonrec describe_feature_group_response = {online_store_total_size_bytes : online_store_total_size_bytes option;The size of the OnlineStore in bytes.
next_token : next_token;A token to resume pagination of the list of Features (FeatureDefinitions).
description : description option;A free form description of the feature group.
*)failure_reason : failure_reason option;The reason that the FeatureGroup failed to be replicated in the OfflineStore. This is failure can occur because:
FeatureGroup could not be created in the OfflineStore.FeatureGroup could not be deleted from the OfflineStore.last_update_status : last_update_status option;A value indicating whether the update made to the feature group was successful.
*)offline_store_status : offline_store_status option;The status of the OfflineStore. Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked
feature_group_status : feature_group_status option;The status of the feature group.
*)role_arn : role_arn option;The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.
*)throughput_config : throughput_config_description option;offline_store_config : offline_store_config option;The configuration of the offline store. It includes the following configurations:
online_store_config : online_store_config option;The configuration for the OnlineStore.
last_modified_time : last_modified_time option;A timestamp indicating when the feature group was last updated.
*)creation_time : creation_time;A timestamp indicating when SageMaker created the FeatureGroup.
feature_definitions : feature_definitions;A list of the Features in the FeatureGroup. Each feature is defined by a FeatureName and FeatureType.
event_time_feature_name : feature_name;The name of the feature that stores the EventTime of a Record in a FeatureGroup.
An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup. All Records in the FeatureGroup have a corresponding EventTime.
record_identifier_feature_name : feature_name;The name of the Feature used for RecordIdentifier, whose value uniquely identifies a record stored in the feature store.
feature_group_name : feature_group_name;he name of the FeatureGroup.
feature_group_arn : feature_group_arn;The Amazon Resource Name (ARN) of the FeatureGroup.
}type nonrec describe_feature_group_request = {next_token : next_token option;A token to resume pagination of the list of Features (FeatureDefinitions). 2,500 Features are returned by default.
feature_group_name : feature_group_name_or_arn;The name or Amazon Resource Name (ARN) of the FeatureGroup you want described.
}type nonrec describe_experiment_response = {last_modified_by : user_context option;Who last modified the experiment.
*)last_modified_time : timestamp option;When the experiment was last modified.
*)created_by : user_context option;Who created the experiment.
*)creation_time : timestamp option;When the experiment was created.
*)description : experiment_description option;The description of the experiment.
*)source : experiment_source option;The Amazon Resource Name (ARN) of the source and, optionally, the type.
*)display_name : experiment_entity_name option;The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
experiment_arn : experiment_arn option;The Amazon Resource Name (ARN) of the experiment.
*)experiment_name : experiment_entity_name option;The name of the experiment.
*)}type nonrec describe_experiment_request = {experiment_name : experiment_entity_name;The name of the experiment to describe.
*)}type nonrec instance_pool = {priority : instance_pool_priority;The priority for the instance pool. SageMaker attempts to provision instances in order of priority, starting with the lowest value. If instances for a higher-priority pool are unavailable, SageMaker attempts to provision from the next pool.
Valid values: 1 to 5, where 1 is the highest priority.
*)model_name_override : model_name option;The name of a SageMaker model to use for this instance pool instead of the model specified for the production variant. Use this to deploy a different model optimized for the instance type in this pool.
*)instance_type : production_variant_instance_type;The ML compute instance type for the instance pool.
*)}Specifies an instance type and its priority for a heterogeneous endpoint. Use instance pools to configure a production variant with multiple instance types, enabling the endpoint to provision instances across different types based on priority.
type nonrec instance_pool_list = instance_pool listtype nonrec production_variant_core_dump_config = {kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias
"alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
destination_s3_uri : destination_s3_uri;The Amazon S3 bucket to send the core dump to.
*)}Specifies configuration for a core dump from the model container when the process crashes.
type nonrec production_variant_capacity_reservation_config = {ml_reservation_arn : ml_reservation_arn option;The Amazon Resource Name (ARN) that uniquely identifies the ML capacity reservation that SageMaker AI applies when it deploys the endpoint.
*)capacity_reservation_preference : capacity_reservation_preference option;Options that you can choose for the capacity reservation. SageMaker AI supports the following options:
capacity-reservations-only SageMaker AI launches instances only into an ML capacity reservation. If no capacity is available, the instances fail to launch.
*)}Settings for the capacity reservation for the compute instances that SageMaker AI reserves for an endpoint.
type nonrec production_variant = {capacity_reservation_config : production_variant_capacity_reservation_config
option;Settings for the capacity reservation for the compute instances that SageMaker AI reserves for an endpoint.
*)inference_ami_version : production_variant_inference_ami_version option;Specifies an option from a collection of preconfigured Amazon Machine Image (AMI) images. Each image is configured by Amazon Web Services with a set of software and driver versions. Amazon Web Services optimizes these configurations for different machine learning workloads.
By selecting an AMI version, you can ensure that your inference environment is compatible with specific software requirements, such as CUDA driver versions, Linux kernel versions, or Amazon Web Services Neuron driver versions.
The AMI version names, and their configurations, are the following:
al2-ami-sagemaker-inference-gpu-2
al2-ami-sagemaker-inference-gpu-2-1
al2-ami-sagemaker-inference-gpu-3-1
al2023-ami-sagemaker-inference-gpu-4-1
al2-ami-sagemaker-inference-neuron-2
routing_config : production_variant_routing_config option;Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
*)managed_instance_scaling : production_variant_managed_instance_scaling option;Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
*)enable_ssm_access : production_variant_ssm_access option;You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint.
container_startup_health_check_timeout_in_seconds : production_variant_container_startup_health_check_timeout_in_seconds
option;The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.
*)model_data_download_timeout_in_seconds : production_variant_model_data_download_timeout_in_seconds
option;The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.
*)volume_size_in_g_b : production_variant_volume_size_in_g_b option;The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.
*)serverless_config : production_variant_serverless_config option;The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
*)core_dump_config : production_variant_core_dump_config option;Specifies configuration for a core dump from the model container when the process crashes.
*)accelerator_type : production_variant_accelerator_type option;This parameter is no longer supported. Elastic Inference (EI) is no longer available.
This parameter was used to specify the size of the EI instance to use for the production variant.
*)initial_variant_weight : variant_weight option;Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.
variant_instance_provision_timeout_in_seconds : variant_instance_provision_timeout_in_seconds
option;The timeout value, in seconds, for provisioning instances for the production variant. When SageMaker encounters an insufficient capacity error while provisioning instances, it retries with the next instance pool (if configured) or waits until the timeout expires. This timeout applies only to capacity provisioning and does not include the time for model download or container startup.
Valid values: 300 to 3600.
*)instance_pools : instance_pool_list option;A list of instance pools for the production variant. Each instance pool specifies an instance type and its priority for provisioning. Use instance pools to configure heterogeneous endpoints that deploy models across multiple instance types.
*)instance_type : production_variant_instance_type option;The ML compute instance type.
*)initial_instance_count : initial_task_count option;Number of instances to launch initially.
*)model_name : model_name option;The name of the model that you want to host. This is the name that you specified when creating the model.
*)variant_name : variant_name;The name of the production variant.
*)}Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants.
type nonrec production_variant_list = production_variant listtype nonrec capture_option = {capture_mode : capture_mode;Specify the boundary of data to capture.
*)}Specifies data Model Monitor will capture.
type nonrec capture_option_list = capture_option listtype nonrec data_capture_config = {capture_content_type_header : capture_content_type_header option;Configuration specifying how to treat different headers. If no headers are specified SageMaker AI will by default base64 encode when capturing the data.
*)capture_options : capture_option_list;Specifies data Model Monitor will capture. You can configure whether to collect only input, only output, or both
*)kms_key_id : kms_key_id option;The Amazon Resource Name (ARN) of an Key Management Service key that SageMaker AI uses to encrypt the captured data at rest using Amazon S3 server-side encryption.
The KmsKeyId can be any of the following formats:
1234abcd-12ab-34cd-56ef-1234567890abarn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890abalias/ExampleAliasarn:aws:kms:us-west-2:111122223333:alias/ExampleAliasdestination_s3_uri : destination_s3_uri;The Amazon S3 location used to capture the data.
*)initial_sampling_percentage : sampling_percentage;The percentage of requests SageMaker AI will capture. A lower value is recommended for Endpoints with high traffic.
*)enable_capture : enable_capture option;Whether data capture should be enabled or disabled (defaults to enabled).
*)}Configuration to control how SageMaker AI captures inference data.
type nonrec async_inference_client_config = {max_concurrent_invocations_per_instance : max_concurrent_invocations_per_instance
option;The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.
*)}Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.
type nonrec async_notification_topic_type_list =
async_notification_topic_types listtype nonrec async_inference_notification_config = {include_inference_response_in : async_notification_topic_type_list option;The Amazon SNS topics where you want the inference response to be included.
The inference response is included only if the response size is less than or equal to 128 KB.
*)error_topic : sns_topic_arn option;Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.
*)success_topic : sns_topic_arn option;Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.
*)}Specifies the configuration for notifications of inference results for asynchronous inference.
type nonrec async_inference_output_config = {s3_failure_path : destination_s3_uri option;The Amazon S3 location to upload failure inference responses to.
*)notification_config : async_inference_notification_config option;Specifies the configuration for notifications of inference results for asynchronous inference.
*)s3_output_path : destination_s3_uri option;The Amazon S3 location to upload inference responses to.
*)kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.
*)}Specifies the configuration for asynchronous inference invocation outputs.
type nonrec async_inference_config = {output_config : async_inference_output_config;Specifies the configuration for asynchronous inference invocation outputs.
*)client_config : async_inference_client_config option;Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.
*)}Specifies configuration for how an endpoint performs asynchronous inference.
type nonrec clarify_label_headers = clarify_header listtype nonrec clarify_feature_headers = clarify_header listtype nonrec clarify_feature_types = clarify_feature_type listtype nonrec clarify_inference_config = {feature_types : clarify_feature_types option;A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one 'text' string (for example, ['text']). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.
feature_headers : clarify_feature_headers option;The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.
label_headers : clarify_label_headers option;For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.
label_attribute : clarify_label_attribute option;A JMESPath expression used to locate the list of label headers in the model container output.
Example: If the model container output of a batch request is '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}', then set LabelAttribute to 'labels' to extract the list of label headers ["cat","dog","fish"]
probability_attribute : clarify_probability_attribute option;A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.
Example: If the model container output of a single request is '{"predicted_label":1,"probability":0.6}', then set ProbabilityAttribute to 'probability'.
label_index : clarify_label_index option;A zero-based index used to extract a label header or list of label headers from model container output in CSV format.
Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"', set LabelIndex to 0 to select the label headers ['cat','dog','fish'].
probability_index : clarify_probability_index option;A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.
Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6', set ProbabilityIndex to 1 to select the probability value 0.6.
Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"', set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3].
max_payload_in_m_b : clarify_max_payload_in_m_b option;The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.
max_record_count : clarify_max_record_count option;The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset. A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1, the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.
content_template : clarify_content_template option;A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features [1,2,3] into the record string '{"myfeatures":[1,2,3]}'. Required only when the model container input is in JSON Lines format.
features_attribute : clarify_features_attribute option;Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures', it extracts a list of features [1,2,3] from request data '{"myfeatures":[1,2,3]}'.
}The inference configuration parameter for the model container.
type nonrec clarify_shap_baseline_config = {shap_baseline_uri : url option;The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud.
shap_baseline : clarify_shap_baseline option;The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.
mime_type : clarify_mime_type option;The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines'. Defaults to 'text/csv'.
}The configuration for the SHAP baseline (also called the background or reference dataset) of the Kernal SHAP algorithm.
ShapBaseline and ShapBaselineUri are mutually exclusive parameters. One or the either is required to configure a SHAP baseline.type nonrec clarify_text_language = | MULTI_LANGUAGE| LIGURIAN| YORUBA| URDU| UKRAINIAN| TURKISH| TELUGU| TATAR| TAMIL| TAGALOG| SWEDISH| SPANISH| SLOVENIAN| SLOVAK| SINHALA| SETSWANA| SERBIAN| SANSKRIT| RUSSIAN| ROMANIAN| PORTUGUESE| POLISH| PERSIAN| NORWEGIAN_BOKMAL| NEPALI| MARATHI| MALAYALAM| MACEDONIAN| LUXEMBOURGISH| LITHUANIAN| LATVIAN| KYRGYZ| KANNADA| ITALIAN| IRISH| INDONESIAN| ICELANDIC| HUNGARIAN| HINDI| HEBREW| GUJARATI| GREEK| GERMAN| FRENCH| FINNISH| ESTONIAN| ENGLISH| DUTCH| DANISH| CZECH| CROATIAN| CHINESE| CATALAN| BULGARIAN| BENGALI| BASQUE| ARMENIAN| ARABIC| ALBANIAN| AFRIKAANStype nonrec clarify_text_config = {granularity : clarify_text_granularity;The unit of granularity for the analysis of text features. For example, if the unit is 'token', then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.
language : clarify_text_language;}A parameter used to configure the SageMaker Clarify explainer to treat text features as text so that explanations are provided for individual units of text. Required only for natural language processing (NLP) explainability.
type nonrec clarify_shap_config = {text_config : clarify_text_config option;A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.
*)seed : clarify_shap_seed option;The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.
*)use_logit : clarify_shap_use_logit option;A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.
*)number_of_samples : clarify_shap_number_of_samples option;The number of samples to be used for analysis by the Kernal SHAP algorithm.
The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint.
*)shap_baseline_config : clarify_shap_baseline_config;The configuration for the SHAP baseline of the Kernal SHAP algorithm.
*)}The configuration for SHAP analysis using SageMaker Clarify Explainer.
type nonrec clarify_explainer_config = {shap_config : clarify_shap_config;The configuration for SHAP analysis.
*)inference_config : clarify_inference_config option;The inference configuration parameter for the model container.
*)enable_explanations : clarify_enable_explanations option;A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See EnableExplanationsfor additional information.
}The configuration parameters for the SageMaker Clarify explainer.
type nonrec explainer_config = {clarify_explainer_config : clarify_explainer_config option;A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.
}A parameter to activate explainers.
type nonrec metrics_config = {metric_publish_frequency_in_seconds : metric_publish_frequency_in_seconds
option;The interval, in seconds, at which metrics are published to Amazon CloudWatch. Defaults to 60. Valid values: 10, 30, 60, 120, 180, 240, 300.
When EnableEnhancedMetrics is set to False, this interval applies to utilization metrics only. Invocation metrics continue to be published at the default 60-second interval. When EnableEnhancedMetrics is set to True, this interval applies to both utilization and invocation metrics.
When EnableDetailedObservability is set to True, this interval applies to per-GPU metrics, per-instance host metrics, container metrics, and fleet-level inference component lifecycle and placement metrics.
enable_detailed_observability : enable_detailed_observability option;Indicates whether detailed observability is enabled for the endpoint. When set to True, the following metrics are published at the configured frequency:
For first-party and Deep Learning Containers (DLC), the Prometheus endpoint path is determined automatically. For Bring-Your-Own-Container (BYOC) cases, you can optionally set ContainerMetricsConfig to specify a custom endpoint path. If not specified, the default path /metrics on port 8080 is used.
When set to False, these additional metrics are not published. Standard invocation and utilization metrics controlled by EnableEnhancedMetrics are unaffected.
The default value for new endpoint configurations is True. For existing endpoint configurations created before this feature, the value is False unless explicitly set.
enable_enhanced_metrics : enable_enhanced_metrics option;Specifies whether to enable enhanced metrics for the endpoint. Enhanced metrics provide utilization and invocation data at instance and container granularity. Container granularity is supported for Inference Components. The default is False.
}The configuration for Utilization metrics.
type nonrec describe_endpoint_config_output = {metrics_config : metrics_config option;The configuration parameters for utilization metrics.
*)enable_network_isolation : boolean_ option;Indicates whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.
*)vpc_config : vpc_config option;execution_role_arn : role_arn option;The Amazon Resource Name (ARN) of the IAM role that you assigned to the endpoint configuration.
*)shadow_production_variants : production_variant_list option;An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants.
explainer_config : explainer_config option;The configuration parameters for an explainer.
*)async_inference_config : async_inference_config option;Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
creation_time : timestamp;A timestamp that shows when the endpoint configuration was created.
*)kms_key_id : kms_key_id option;Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
*)data_capture_config : data_capture_config option;production_variants : production_variant_list;An array of ProductionVariant objects, one for each model that you want to host at this endpoint.
endpoint_config_arn : endpoint_config_arn;The Amazon Resource Name (ARN) of the endpoint configuration.
*)endpoint_config_name : endpoint_config_name;Name of the SageMaker endpoint configuration.
*)}type nonrec describe_endpoint_config_input = {endpoint_config_name : endpoint_config_name;The name of the endpoint configuration.
*)}type nonrec pending_production_variant_summary = {routing_config : production_variant_routing_config option;Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
*)managed_instance_scaling : production_variant_managed_instance_scaling option;Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
*)desired_serverless_config : production_variant_serverless_config option;The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.
*)current_serverless_config : production_variant_serverless_config option;The serverless configuration for the endpoint.
*)variant_status : production_variant_status_list option;The endpoint variant status which describes the current deployment stage status or operational status.
*)accelerator_type : production_variant_accelerator_type option;This parameter is no longer supported. Elastic Inference (EI) is no longer available.
This parameter was used to specify the size of the EI instance to use for the production variant.
*)instance_pools : instance_pool_summary_list option;A list of instance pools for the production variant. Each pool indicates the instance type and the current number of instances of that type.
*)instance_type : production_variant_instance_type option;The type of instances associated with the variant.
*)desired_instance_count : task_count option;The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
*)current_instance_count : task_count option;The number of instances associated with the variant.
*)desired_weight : variant_weight option;The requested weight for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
*)current_weight : variant_weight option;The weight associated with the variant.
*)deployed_images : deployed_images option;An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant.
variant_name : variant_name;The name of the variant.
*)}The production variant summary for a deployment when an endpoint is creating or updating with the CreateEndpoint or UpdateEndpoint operations. Describes the VariantStatus , weight and capacity for a production variant associated with an endpoint.
type nonrec pending_production_variant_summary_list =
pending_production_variant_summary listtype nonrec pending_deployment_summary = {shadow_production_variants : pending_production_variant_summary_list option;An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants for the in-progress deployment.
start_time : timestamp option;The start time of the deployment.
*)production_variants : pending_production_variant_summary_list option;An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint for the in-progress deployment.
*)endpoint_config_name : endpoint_config_name;The name of the endpoint configuration used in the deployment.
*)}The summary of an in-progress deployment when an endpoint is creating or updating with a new endpoint configuration.
type nonrec describe_endpoint_output = {metrics_config : metrics_config option;The configuration parameters for utilization metrics.
*)shadow_production_variants : production_variant_summary_list option;An array of ProductionVariantSummary objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants.
explainer_config : explainer_config option;The configuration parameters for an explainer.
*)pending_deployment_summary : pending_deployment_summary option;Returns the summary of an in-progress deployment. This field is only returned when the endpoint is creating or updating with a new endpoint configuration.
*)async_inference_config : async_inference_config option;Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
last_deployment_config : deployment_config option;The most recent deployment configuration for the endpoint.
*)last_modified_time : timestamp;A timestamp that shows when the endpoint was last modified.
*)creation_time : timestamp;A timestamp that shows when the endpoint was created.
*)failure_reason : failure_reason option;If the status of the endpoint is Failed, the reason why it failed.
endpoint_status : endpoint_status;The status of the endpoint.
OutOfService: Endpoint is not available to take incoming requests.Creating: CreateEndpoint is executing.Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count.RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.InService: Endpoint is available to process incoming requests.Deleting: DeleteEndpoint is executing.Failed: Endpoint could not be created, updated, or re-scaled. Use the FailureReason value returned by DescribeEndpoint for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.UpdateRollbackFailed: Both the rolling deployment and auto-rollback failed. Your endpoint is in service with a mix of the old and new endpoint configurations. For information about how to remedy this issue and restore the endpoint's status to InService, see Rolling Deployments.data_capture_config : data_capture_config_summary option;production_variants : production_variant_summary_list option;endpoint_config_name : endpoint_config_name option;The name of the endpoint configuration associated with this endpoint.
*)endpoint_arn : endpoint_arn;The Amazon Resource Name (ARN) of the endpoint.
*)endpoint_name : endpoint_name;Name of the endpoint.
*)}type nonrec edge_preset_deployment_output = {status_message : string_ option;Returns a message describing the status of the deployed resource.
*)status : edge_preset_deployment_status option;The status of the deployable resource.
*)artifact : edge_preset_deployment_artifact option;The Amazon Resource Name (ARN) of the generated deployable resource.
*)type_ : edge_preset_deployment_type;The deployment type created by SageMaker Edge Manager. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
*)}The output of a SageMaker Edge Manager deployable resource.
type nonrec describe_edge_packaging_job_response = {preset_deployment_output : edge_preset_deployment_output option;The output of a SageMaker Edge Manager deployable resource.
*)model_signature : string_ option;The signature document of files in the model artifact.
*)model_artifact : s3_uri option;The Amazon Simple Storage (S3) URI where model artifacts ares stored.
*)last_modified_time : timestamp option;The timestamp of when the job was last updated.
*)creation_time : timestamp option;The timestamp of when the packaging job was created.
*)edge_packaging_job_status_message : string_ option;Returns a message describing the job status and error messages.
*)edge_packaging_job_status : edge_packaging_job_status;The current status of the packaging job.
*)resource_key : kms_key_id option;The Amazon Web Services KMS key to use when encrypting the EBS volume the job run on.
*)output_config : edge_output_config option;The output configuration for the edge packaging job.
*)role_arn : role_arn option;The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact Neo.
*)model_version : edge_version option;The version of the model.
*)model_name : entity_name option;The name of the model.
*)compilation_job_name : entity_name option;The name of the SageMaker Neo compilation job that is used to locate model artifacts that are being packaged.
*)edge_packaging_job_name : entity_name;The name of the edge packaging job.
*)edge_packaging_job_arn : edge_packaging_job_arn;The Amazon Resource Name (ARN) of the edge packaging job.
*)}type nonrec describe_edge_packaging_job_request = {edge_packaging_job_name : entity_name;The name of the edge packaging job.
*)}type nonrec edge_deployment_model_config = {edge_packaging_job_name : entity_name;The edge packaging job associated with this deployment.
*)model_handle : entity_name;The name the device application uses to reference this model.
*)}Contains information about the configuration of a model in a deployment.
type nonrec edge_deployment_model_configs = edge_deployment_model_config listtype nonrec device_names = device_name listtype nonrec device_selection_config = {device_name_contains : device_name option;A filter to select devices with names containing this name.
*)device_names : device_names option;List of devices chosen to deploy.
*)percentage : percentage option;Percentage of devices in the fleet to deploy to the current stage.
*)device_subset_type : device_subset_type;Type of device subsets to deploy to the current stage.
*)}Contains information about the configurations of selected devices.
type nonrec edge_deployment_config = {failure_handling_policy : failure_handling_policy;Toggle that determines whether to rollback to previous configuration if the current deployment fails. By default this is turned on. You may turn this off if you want to investigate the errors yourself.
*)}Contains information about the configuration of a deployment.
type nonrec edge_deployment_status = {edge_deployment_stage_start_time : timestamp option;The time when the deployment API started.
*)edge_deployment_status_message : string_ option;A detailed message about deployment status in current stage.
*)edge_deployment_failed_in_stage : integer;The number of edge devices that failed the deployment in current stage.
*)edge_deployment_pending_in_stage : integer;The number of edge devices yet to pick up the deployment in current stage, or in progress.
*)edge_deployment_success_in_stage : integer;The number of edge devices with the successful deployment in the current stage.
*)stage_status : stage_status;The general status of the current stage.
*)}Contains information summarizing the deployment stage results.
type nonrec deployment_stage_status_summary = {deployment_status : edge_deployment_status;General status of the current state.
*)deployment_config : edge_deployment_config;Configuration of the deployment details.
*)device_selection_config : device_selection_config;Configuration of the devices in the stage.
*)stage_name : entity_name;The name of the stage.
*)}Contains information summarizing the deployment stage results.
type nonrec deployment_stage_status_summaries =
deployment_stage_status_summary listtype nonrec describe_edge_deployment_plan_response = {last_modified_time : timestamp option;The time when the edge deployment plan was last updated.
*)creation_time : timestamp option;The time when the edge deployment plan was created.
*)next_token : next_token option;Token to use when calling the next set of stages in the edge deployment plan.
*)stages : deployment_stage_status_summaries;List of stages in the edge deployment plan.
*)edge_deployment_failed : integer option;The number of edge devices that failed the deployment.
*)edge_deployment_pending : integer option;The number of edge devices yet to pick up deployment, or in progress.
*)edge_deployment_success : integer option;The number of edge devices with the successful deployment.
*)device_fleet_name : entity_name;The device fleet used for this edge deployment plan.
*)model_configs : edge_deployment_model_configs;List of models associated with the edge deployment plan.
*)edge_deployment_plan_name : entity_name;The name of the edge deployment plan.
*)edge_deployment_plan_arn : edge_deployment_plan_arn;The ARN of edge deployment plan.
*)}type nonrec describe_edge_deployment_plan_request = {max_results : deployment_stage_max_results option;The maximum number of results to select (50 by default).
*)next_token : next_token option;If the edge deployment plan has enough stages to require tokening, then this is the response from the last list of stages returned.
*)edge_deployment_plan_name : entity_name;The name of the deployment plan to describe.
*)}type nonrec r_studio_server_pro_domain_settings = {default_resource_spec : resource_spec option;r_studio_package_manager_url : string_ option;A URL pointing to an RStudio Package Manager server.
*)r_studio_connect_url : string_ option;A URL pointing to an RStudio Connect server.
*)domain_execution_role_arn : role_arn;The ARN of the execution role for the RStudioServerPro Domain-level app.
}A collection of settings that configure the RStudioServerPro Domain-level app.
type nonrec domain_settings = {ip_address_type : ip_address_type option;The IP address type for the domain. Specify ipv4 for IPv4-only connectivity or dualstack for both IPv4 and IPv6 connectivity. When you specify dualstack, the subnet must support IPv6 CIDR blocks. If not specified, defaults to ipv4.
unified_studio_settings : unified_studio_settings option;The settings that apply to an SageMaker AI domain when you use it in Amazon SageMaker Unified Studio.
*)amazon_q_settings : amazon_q_settings option;A collection of settings that configure the Amazon Q experience within the domain. The AuthMode that you use to create the domain must be SSO.
docker_settings : docker_settings option;A collection of settings that configure the domain's Docker interaction.
*)trusted_identity_propagation_settings : trusted_identity_propagation_settings
option;The Trusted Identity Propagation (TIP) settings for the SageMaker domain. These settings determine how user identities from IAM Identity Center are propagated through the domain to TIP enabled Amazon Web Services services.
*)execution_role_identity_config : execution_role_identity_config option;The configuration for attaching a SageMaker AI user profile name to the execution role as a sts:SourceIdentity key.
*)r_studio_server_pro_domain_settings : r_studio_server_pro_domain_settings
option;A collection of settings that configure the RStudioServerPro Domain-level app.
security_group_ids : domain_security_group_ids option;The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.
}A collection of settings that apply to the SageMaker Domain. These settings are specified through the CreateDomain API call.
type nonrec describe_domain_response = {default_space_settings : default_space_settings option;The default settings for shared spaces that users create in the domain.
*)tag_propagation : tag_propagation option;Indicates whether custom tag propagation is supported for the domain.
*)home_efs_file_system_creation : home_efs_file_system_creation option;Indicates whether a home EFS file system is created for the domain.
*)app_security_group_management : app_security_group_management option;The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided.
kms_key_id : kms_key_id option;The Amazon Web Services KMS customer managed key used to encrypt the EFS volume attached to the domain.
*)vpc_id : vpc_id option;The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
*)url : string1024 option;The domain's URL.
*)subnet_ids : subnets option;The VPC subnets that the domain uses for communication.
*)home_efs_file_system_kms_key_id : kms_key_id option;Use KmsKeyId.
app_network_access_type : app_network_access_type option;Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly.
PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker AI, which allows direct internet accessVpcOnly - All traffic is through the specified VPC and subnetsdomain_settings : domain_settings option;A collection of Domain settings.
default_user_settings : user_settings option;Settings which are applied to UserProfiles in this domain if settings are not explicitly specified in a given UserProfile.
*)auth_mode : auth_mode option;The domain's authentication mode.
*)security_group_id_for_domain_boundary : security_group_id option;The ID of the security group that authorizes traffic between the RSessionGateway apps and the RStudioServerPro app.
failure_reason : failure_reason option;The failure reason.
*)last_modified_time : last_modified_time option;The last modified time.
*)creation_time : creation_time option;The creation time.
*)status : domain_status option;The status.
*)single_sign_on_application_arn : single_sign_on_application_arn option;The ARN of the application managed by SageMaker AI in IAM Identity Center. This value is only returned for domains created after October 1, 2023.
*)single_sign_on_managed_application_instance_id : string256 option;The IAM Identity Center managed application instance ID.
*)home_efs_file_system_id : resource_id option;The ID of the Amazon Elastic File System managed by this Domain.
*)domain_name : domain_name option;The domain name.
*)domain_id : domain_id option;The domain ID.
*)domain_arn : domain_arn option;The domain's Amazon Resource Name (ARN).
*)}type nonrec describe_device_fleet_response = {iot_role_alias : iot_role_alias option;The Amazon Resource Name (ARN) alias created in Amazon Web Services Internet of Things (IoT).
*)role_arn : role_arn option;The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
*)last_modified_time : timestamp;Timestamp of when the device fleet was last updated.
*)creation_time : timestamp;Timestamp of when the device fleet was created.
*)description : device_fleet_description option;A description of the fleet.
*)output_config : edge_output_config;The output configuration for storing sampled data.
*)device_fleet_arn : device_fleet_arn;The The Amazon Resource Name (ARN) of the fleet.
*)device_fleet_name : entity_name;The name of the fleet.
*)}type nonrec describe_device_fleet_request = {device_fleet_name : entity_name;The name of the fleet.
*)}type nonrec edge_model = {latest_inference : timestamp option;The timestamp of the last inference that was made.
*)latest_sample_time : timestamp option;The timestamp of the last data sample taken.
*)model_version : edge_version;The model version.
*)model_name : entity_name;The name of the model.
*)}The model on the edge device.
type nonrec edge_models = edge_model listtype nonrec describe_device_response = {agent_version : edge_version option;Edge Manager agent version.
*)next_token : next_token option;The response from the last list when returning a list large enough to need tokening.
*)max_models : integer option;The maximum number of models.
*)models : edge_models option;Models on the device.
*)latest_heartbeat : timestamp option;The last heartbeat received from the device.
*)registration_time : timestamp;The timestamp of the last registration or de-reregistration.
*)iot_thing_name : thing_name option;The Amazon Web Services Internet of Things (IoT) object thing name associated with the device.
*)device_fleet_name : entity_name;The name of the fleet the device belongs to.
*)description : device_description option;A description of the device.
*)device_name : entity_name;The unique identifier of the device.
*)device_arn : device_arn option;The Amazon Resource Name (ARN) of the device.
*)}type nonrec describe_device_request = {device_fleet_name : entity_name;The name of the fleet the devices belong to.
*)device_name : entity_name;The unique ID of the device.
*)next_token : next_token option;Next token of device description.
*)}type nonrec data_quality_baseline_config = {statistics_resource : monitoring_statistics_resource option;constraints_resource : monitoring_constraints_resource option;baselining_job_name : processing_job_name option;The name of the job that performs baselining for the data quality monitoring job.
*)}Configuration for monitoring constraints and monitoring statistics. These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.
type nonrec data_quality_app_specification = {environment : monitoring_environment_map option;Sets the environment variables in the container that the monitoring job runs.
*)post_analytics_processor_source_uri : s3_uri option;An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
*)record_preprocessor_source_uri : s3_uri option;An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
*)container_arguments : monitoring_container_arguments option;The arguments to send to the container that the monitoring job runs.
*)container_entrypoint : container_entrypoint option;The entrypoint for a container used to run a monitoring job.
*)image_uri : image_uri;The container image that the data quality monitoring job runs.
*)}Information about the container that a data quality monitoring job runs.
type nonrec data_quality_job_input = {batch_transform_input : batch_transform_input option;Input object for the batch transform job.
*)endpoint_input : endpoint_input option;}The input for the data quality monitoring job. Currently endpoints are supported for input.
type nonrec describe_data_quality_job_definition_response = {stopping_condition : monitoring_stopping_condition option;role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.
*)network_config : monitoring_network_config option;The networking configuration for the data quality monitoring job.
*)job_resources : monitoring_resources;data_quality_job_output_config : monitoring_output_config;data_quality_job_input : data_quality_job_input;The list of inputs for the data quality monitoring job. Currently endpoints are supported.
*)data_quality_app_specification : data_quality_app_specification;Information about the container that runs the data quality monitoring job.
*)data_quality_baseline_config : data_quality_baseline_config option;The constraints and baselines for the data quality monitoring job definition.
*)creation_time : timestamp;The time that the data quality monitoring job definition was created.
*)job_definition_name : monitoring_job_definition_name;The name of the data quality monitoring job definition.
*)job_definition_arn : monitoring_job_definition_arn;The Amazon Resource Name (ARN) of the data quality monitoring job definition.
*)}type nonrec describe_data_quality_job_definition_request = {job_definition_name : monitoring_job_definition_name;The name of the data quality monitoring job definition to describe.
*)}type nonrec describe_context_response = {lineage_group_arn : lineage_group_arn option;The Amazon Resource Name (ARN) of the lineage group.
*)last_modified_by : user_context option;last_modified_time : timestamp option;When the context was last modified.
*)created_by : user_context option;creation_time : timestamp option;When the context was created.
*)properties : lineage_entity_parameters option;A list of the context's properties.
*)description : experiment_description option;The description of the context.
*)context_type : string256 option;The type of the context.
*)source : context_source option;The source of the context.
*)context_arn : context_arn option;The Amazon Resource Name (ARN) of the context.
*)context_name : context_name option;The name of the context.
*)}type nonrec describe_context_request = {context_name : context_name_or_arn;The name of the context to describe.
*)}type nonrec describe_compute_quota_response = {last_modified_by : user_context option;last_modified_time : timestamp option;Last modified time of the compute allocation configuration.
*)created_by : user_context option;creation_time : timestamp;Creation time of the compute allocation configuration.
*)activation_state : activation_state option;The state of the compute allocation being described. Use to enable or disable compute allocation.
Default is Enabled.
compute_quota_target : compute_quota_target;The target entity to allocate compute resources to.
*)compute_quota_config : compute_quota_config option;Configuration of the compute allocation definition. This includes the resource sharing option, and the setting to preempt low priority tasks.
*)cluster_arn : cluster_arn option;ARN of the cluster.
*)failure_reason : failure_reason option;Failure reason of the compute allocation definition.
*)status : scheduler_resource_status;Status of the compute allocation definition.
*)compute_quota_version : integer;Version of the compute allocation definition.
*)description : entity_description option;Description of the compute allocation definition.
*)name : entity_name;Name of the compute allocation definition.
*)compute_quota_id : compute_quota_id;ID of the compute allocation definition.
*)compute_quota_arn : compute_quota_arn;ARN of the compute allocation definition.
*)}type nonrec describe_compute_quota_request = {compute_quota_version : integer option;Version of the compute allocation definition.
*)compute_quota_id : compute_quota_id;ID of the compute allocation definition.
*)}type nonrec model_digests = {artifact_digest : artifact_digest option;Provides a hash value that uniquely identifies the stored model artifacts.
*)}Provides information to verify the integrity of stored model artifacts.
type nonrec input_config = {framework_version : framework_version option;Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.
For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks.
*)framework : framework;Identifies the framework in which the model was trained. For example: TENSORFLOW.
*)data_input_config : data_input_config option;Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are Framework specific.
TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
{"input":[1,1024,1024,3]}{\"input\":[1,1024,1024,3]}Examples for two inputs:
{"data1": [1,28,28,1], "data2":[1,28,28,1]}{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.
Examples for one input:
{"input_1":[1,3,224,224]}{\"input_1\":[1,3,224,224]}Examples for two inputs:
{"input_1": [1,3,224,224], "input_2":[1,3,224,224]} {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}MXNET/ONNX/DARKNET: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
{"data":[1,3,1024,1024]}{\"data\":[1,3,1024,1024]}Examples for two inputs:
{"var1": [1,1,28,28], "var2":[1,1,28,28]} {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.
Examples for one input in dictionary format:
{"input0":[1,3,224,224]}{\"input0\":[1,3,224,224]}[[1,3,224,224]]Examples for two inputs in dictionary format:
{"input0":[1,3,224,224], "input1":[1,3,224,224]}{\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]} [[1,3,224,224], [1,3,224,224]]XGBOOST: input data name and shape are not needed.DataInputConfig supports the following parameters for CoreML TargetDevice (ML Model format):
shape: Input shape, for example {"input_1": {"shape": [1,224,224,3]}}. In addition to static input shapes, CoreML converter supports Flexible input shapes:
{"input_1": {"shape": ["1..10", 224, 224, 3]}}{"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}default_shape: Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}type: Input type. Allowed values: Image and Tensor. By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such as bias and scale.bias: If the input type is an Image, you need to provide the bias vector.scale: If the input type is an Image, you need to provide a scale factor.CoreML ClassifierConfig parameters can be specified using OutputConfig CompilerOptions. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:
Tensor type input:
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}Tensor type input without input name (PyTorch):
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]Image type input:
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}Image type input without input name (PyTorch):
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}Depending on the model format, DataInputConfig requires the following parameters for ml_eia2 OutputConfig:TargetDevice.
For TensorFlow models saved in the SavedModel format, specify the input names from signature_def_key and the input model shapes for DataInputConfig. Specify the signature_def_key in OutputConfig:CompilerOptions if the model does not use TensorFlow's default signature def key. For example:
"DataInputConfig": {"inputs": [1, 224, 224, 3]}"CompilerOptions": {"signature_def_key": "serving_custom"}For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in DataInputConfig and the output tensor names for output_names in OutputConfig:CompilerOptions. For example:
"DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}"CompilerOptions": {"output_names": ["output_tensor:0"]}s3_uri : s3_uri;The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
*)}Contains information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
type nonrec output_config = {kms_key_id : kms_key_id option;The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker AI uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't provide a KMS key ID, Amazon SageMaker AI uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KmsKeyId can be any of the following formats:
1234abcd-12ab-34cd-56ef-1234567890abarn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890abalias/ExampleAliasarn:aws:kms:us-west-2:111122223333:alias/ExampleAliascompiler_options : compiler_options option;Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.
DTYPE: Specifies the data type for the input. When compiling for ml_* (except for ml_inf) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:
"float" or "float32"."int64" or "long".For example, {"dtype" : "float32"}.
CPU: Compilation for CPU supports the following compiler options.
mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}ARM: Details of ARM CPU compilations.
NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.
NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.
gpu_code: Specifies the targeted architecture.trt-ver: Specifies the TensorRT versions in x.y.z. format.cuda-ver: Specifies the CUDA version in x.y format.For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
ANDROID: Compilation for the Android OS supports the following compiler options:
ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".
For information about supported compiler options, see Neuron Compiler CLI Reference Guide.
CoreML: Compilation for the CoreML OutputConfig TargetDevice supports the following compiler options:
class_labels: Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"}. Labels inside the txt file should be separated by newlines.target_platform : target_platform option;Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.
The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
Raspberry Pi 3 Model B+
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},
"CompilerOptions": {'mattr': ['+neon']}
Jetson TX2
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},
"CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
EC2 m5.2xlarge instance OS
"TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},
"CompilerOptions": {'mcpu': 'skylake-avx512'}
RK3399
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
ARMv7 phone (CPU)
"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},
"CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
ARMv8 phone (CPU)
"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},
"CompilerOptions": {'ANDROID_PLATFORM': 29}
target_device : target_device option;Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.
Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.
s3_output_location : s3_uri;Identifies the S3 bucket where you want Amazon SageMaker AI to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
}Contains information about the output location for the compiled model and the target device that the model runs on. TargetDevice and TargetPlatform are mutually exclusive, so you need to choose one between the two to specify your target device or platform. If you cannot find your device you want to use from the TargetDevice list, use TargetPlatform to describe the platform of your edge device and CompilerOptions if there are specific settings that are required or recommended to use for particular TargetPlatform.
type nonrec neo_vpc_security_group_ids = neo_vpc_security_group_id listtype nonrec neo_vpc_subnets = neo_vpc_subnet_id listtype nonrec neo_vpc_config = {subnets : neo_vpc_subnets;The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.
*)security_group_ids : neo_vpc_security_group_ids;The VPC security group IDs. IDs have the form of sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
}The VpcConfig configuration object that specifies the VPC that you want the compilation jobs to connect to. For more information on controlling access to your Amazon S3 buckets used for compilation job, see Give Amazon SageMaker AI Compilation Jobs Access to Resources in Your Amazon VPC.
type nonrec derived_information = {derived_data_input_config : data_input_config option;The data input configuration that SageMaker Neo automatically derived for the model. When SageMaker Neo derives this information, you don't need to specify the data input configuration when you create a compilation job.
*)}Information that SageMaker Neo automatically derived about the model.
type nonrec describe_compilation_job_response = {derived_information : derived_information option;Information that SageMaker Neo automatically derived about the model.
*)vpc_config : neo_vpc_config option;A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.
*)output_config : output_config;Information about the output location for the compiled model and the target device that the model runs on.
*)input_config : input_config;Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI assumes to perform the model compilation job.
*)model_digests : model_digests option;Provides a BLAKE2 hash value that identifies the compiled model artifacts in Amazon S3.
*)model_artifacts : model_artifacts;Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.
*)failure_reason : failure_reason;If a model compilation job failed, the reason it failed.
*)last_modified_time : last_modified_time;The time that the status of the model compilation job was last modified.
*)creation_time : creation_time;The time that the model compilation job was created.
*)model_package_version_arn : model_package_arn option;The Amazon Resource Name (ARN) of the versioned model package that was provided to SageMaker Neo when you initiated a compilation job.
*)inference_image : inference_image option;The inference image to use when compiling a model. Specify an image only if the target device is a cloud instance.
*)stopping_condition : stopping_condition;Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker AI ends the compilation job. Use this API to cap model training costs.
*)compilation_end_time : timestamp option;The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job's model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker AI detected that the job failed.
*)compilation_start_time : timestamp option;The time when the model compilation job started the CompilationJob instances.
You are billed for the time between this timestamp and the timestamp in the CompilationEndTime field. In Amazon CloudWatch Logs, the start time might be later than this time. That's because it takes time to download the compilation job, which depends on the size of the compilation job container.
compilation_job_status : compilation_job_status;The status of the model compilation job.
*)compilation_job_arn : compilation_job_arn;The Amazon Resource Name (ARN) of the model compilation job.
*)compilation_job_name : entity_name;The name of the model compilation job.
*)}type nonrec describe_compilation_job_request = {compilation_job_name : entity_name;The name of the model compilation job that you want information about.
*)}type nonrec describe_code_repository_output = {git_config : git_config option;Configuration details about the repository, including the URL where the repository is located, the default branch, and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.
*)last_modified_time : last_modified_time;The date and time that the repository was last changed.
*)creation_time : creation_time;The date and time that the repository was created.
*)code_repository_arn : code_repository_arn;The Amazon Resource Name (ARN) of the Git repository.
*)code_repository_name : entity_name;The name of the Git repository.
*)}type nonrec describe_code_repository_input = {code_repository_name : entity_name;The name of the Git repository to describe.
*)}type nonrec describe_cluster_scheduler_config_response = {last_modified_by : user_context option;last_modified_time : timestamp option;Last modified time of the cluster policy.
*)created_by : user_context option;creation_time : timestamp;Creation time of the cluster policy.
*)description : entity_description option;Description of the cluster policy.
*)scheduler_config : scheduler_config option;Cluster policy configuration. This policy is used for task prioritization and fair-share allocation. This helps prioritize critical workloads and distributes idle compute across entities.
*)cluster_arn : cluster_arn option;ARN of the cluster where the cluster policy is applied.
*)status_details : status_details_map option;Additional details about the status of the cluster policy. This field provides context when the policy is in a non-active state, such as during creation, updates, or if failures occur.
*)failure_reason : failure_reason option;Failure reason of the cluster policy.
*)status : scheduler_resource_status;Status of the cluster policy.
*)cluster_scheduler_config_version : integer;Version of the cluster policy.
*)name : entity_name;Name of the cluster policy.
*)cluster_scheduler_config_id : cluster_scheduler_config_id;ID of the cluster policy.
*)cluster_scheduler_config_arn : cluster_scheduler_config_arn;ARN of the cluster policy.
*)}type nonrec describe_cluster_scheduler_config_request = {cluster_scheduler_config_version : integer option;Version of the cluster policy.
*)cluster_scheduler_config_id : cluster_scheduler_config_id;ID of the cluster policy.
*)}type nonrec cluster_instance_placement = {availability_zone_id : cluster_availability_zone_id option;The unique identifier (ID) of the Availability Zone where the node in the SageMaker HyperPod cluster is launched.
*)availability_zone : cluster_availability_zone option;The Availability Zone where the node in the SageMaker HyperPod cluster is launched.
*)}Specifies the placement details for the node in the SageMaker HyperPod cluster, including the Availability Zone and the unique identifier (ID) of the Availability Zone.
type nonrec cluster_kubernetes_config_node_details = {desired_taints : cluster_kubernetes_taints option;The desired taints to be applied to the cluster node.
*)current_taints : cluster_kubernetes_taints option;The current taints applied to the cluster node.
*)desired_labels : cluster_kubernetes_labels option;The desired labels to be applied to the cluster node.
*)current_labels : cluster_kubernetes_labels option;The current labels applied to the cluster node.
*)}Node-specific Kubernetes configuration showing both current and desired state of labels and taints for an individual cluster node.
type nonrec cluster_network_interface_details = {interface_type : cluster_interface_type option;The type of network interface for the instance group. Valid values are efa and efa-only.
}The network interface configuration details for a Amazon SageMaker HyperPod cluster instance group.
type nonrec cluster_node_details = {network_interface : cluster_network_interface_details option;The network interface configuration for the cluster node.
*)capacity_type : cluster_capacity_type option;The capacity type of the node. Valid values are OnDemand and Spot. When set to OnDemand, the node is launched as an On-Demand instance. When set to Spot, the node is launched as a Spot instance.
kubernetes_config : cluster_kubernetes_config_node_details option;The Kubernetes configuration applied to this node, showing both the current and desired state of labels and taints. The cluster works to reconcile the actual state with the declared state.
*)ultra_server_info : ultra_server_info option;Contains information about the UltraServer.
*)image_version_status : cluster_image_version_status option;The status of the image version for the cluster node.
*)desired_image_release_version : image_release_version option;The desired version of the HyperPod-managed AMI for the node. This may differ from the current version when an update is pending.
*)current_image_release_version : image_release_version option;The version of the HyperPod-managed AMI currently running on the node.
*)desired_image_id : image_id option;The ID of the Amazon Machine Image (AMI) desired for the node.
*)current_image_id : image_id option;The ID of the Amazon Machine Image (AMI) currently in use by the node.
*)placement : cluster_instance_placement option;The placement details of the SageMaker HyperPod cluster node.
*)private_dns_hostname : cluster_private_dns_hostname option;The private DNS hostname of the SageMaker HyperPod cluster node.
*)private_primary_ipv6 : cluster_private_primary_ipv6 option;The private primary IPv6 address of the SageMaker HyperPod cluster node when configured with an Amazon VPC that supports IPv6 and includes subnets with IPv6 addressing enabled in either the cluster Amazon VPC configuration or the instance group Amazon VPC configuration.
*)private_primary_ip : cluster_private_primary_ip option;The private primary IP address of the SageMaker HyperPod cluster node.
*)instance_storage_configs : cluster_instance_storage_configs option;The configurations of additional storage specified to the instance group where the instance (node) is launched.
*)threads_per_core : cluster_threads_per_core option;The number of threads per CPU core you specified under CreateCluster.
override_vpc_config : vpc_config option;The customized Amazon VPC configuration at the instance group level that overrides the default Amazon VPC configuration of the SageMaker HyperPod cluster.
*)life_cycle_config : cluster_life_cycle_config option;The LifeCycle configuration applied to the instance.
*)last_software_update_time : timestamp option;The time when the cluster was last updated.
*)launch_time : timestamp option;The time when the instance is launched.
*)instance_type : cluster_instance_type option;The type of the instance.
*)instance_status : cluster_instance_status_details option;The status of the instance.
*)node_logical_id : cluster_node_logical_id option;A unique identifier for the node that persists throughout its lifecycle, from provisioning request to termination. This identifier can be used to track the node even before it has an assigned InstanceId.
instance_id : Smaws_Lib.Smithy_api.Types.string_ option;The ID of the instance.
*)instance_group_name : cluster_instance_group_name option;The instance group name in which the instance is.
*)}Details of an instance (also called a node interchangeably) in a SageMaker HyperPod cluster.
type nonrec describe_cluster_node_response = {node_details : cluster_node_details;The details of the SageMaker HyperPod cluster node.
*)}type nonrec describe_cluster_node_request = {node_logical_id : cluster_node_logical_id option;The logical identifier of the node to describe. You can specify either NodeLogicalId or InstanceId, but not both. NodeLogicalId can be used to describe nodes that are still being provisioned and don't yet have an InstanceId assigned.
node_id : cluster_node_id option;The ID of the SageMaker HyperPod cluster node.
*)cluster_name : cluster_name_or_arn;The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster in which the node is.
*)}type nonrec eks_role_access_entries = Smaws_Lib.Smithy_api.Types.string_ listtype nonrec cluster_metadata = {slr_access_entry : Smaws_Lib.Smithy_api.Types.string_ option;The Service-Linked Role (SLR) associated with the cluster. This is created by HyperPod on your behalf and only applies for EKS orchestrated clusters.
*)eks_role_access_entries : eks_role_access_entries option;A list of Amazon EKS IAM role ARNs associated with the cluster. This is created by HyperPod on your behalf and only applies for EKS orchestrated clusters.
*)failure_message : Smaws_Lib.Smithy_api.Types.string_ option;An error message describing why the cluster level operation (such as creating, updating, or deleting) failed.
*)}Metadata information about a HyperPod cluster showing information about the cluster level operations, such as creating, updating, and deleting.
type nonrec capacity_reservation = {type_ : capacity_reservation_type option;The type of Capacity Reservation. Valid values are ODCR (On-Demand Capacity Reservation) or CRG (Capacity Reservation Group).
arn : Smaws_Lib.Smithy_api.Types.string_ option;The Amazon Resource Name (ARN) of the Capacity Reservation.
*)}Information about the Capacity Reservation used by an instance or instance group.
type nonrec instance_group_metadata = {ami_override : Smaws_Lib.Smithy_api.Types.string_ option;If you use a custom Amazon Machine Image (AMI) for the instance group, this field shows the ID of the custom AMI.
*)security_group_ids : security_group_ids option;A list of security group IDs associated with the instance group.
*)subnet_id : Smaws_Lib.Smithy_api.Types.string_ option;The ID of the subnet where the instance group is located.
*)capacity_reservation : capacity_reservation option;Information about the Capacity Reservation used by the instance group.
*)availability_zone_id : Smaws_Lib.Smithy_api.Types.string_ option;The ID of the Availability Zone where the instance group is located.
*)failure_message : Smaws_Lib.Smithy_api.Types.string_ option;An error message describing why the instance group level operation (such as creating, scaling, or deleting) failed.
*)}Metadata information about an instance group in a HyperPod cluster.
type nonrec instance_group_scaling_metadata = {failure_message : Smaws_Lib.Smithy_api.Types.string_ option;An error message describing why the scaling operation failed, if applicable.
*)min_count : instance_count option;Minimum instance count of the instance group.
*)target_count : target_count option;The desired number of instances for the group after scaling.
*)instance_count : instance_count option;The current number of instances in the group.
*)}Metadata information about scaling operations for an instance group.
type nonrec efa_enis = Smaws_Lib.Smithy_api.Types.string_ listtype nonrec additional_enis = {efa_enis : efa_enis option;A list of Elastic Fabric Adapter (EFA) ENIs associated with the instance.
*)}Information about additional Elastic Network Interfaces (ENIs) associated with an instance.
type nonrec instance_requirements_eni_configuration = {additional_enis : additional_enis option;Information about additional Elastic Network Interfaces (ENIs) associated with the instance type category.
*)customer_eni : Smaws_Lib.Smithy_api.Types.string_ option;The ID of the customer-managed Elastic Network Interface (ENI) associated with the instance type category.
*)}The customer ENI and additional ENIs associated with a network interface category.
type nonrec instance_requirements_eni_configurations =
instance_requirements_eni_configuration listtype nonrec instance_metadata = {node_logical_id : cluster_node_logical_id option;The unique logical identifier of the node within the cluster. The ID used here is the same object as in the BatchAddClusterNodes API.
lcs_execution_state : Smaws_Lib.Smithy_api.Types.string_ option;The execution state of the Lifecycle Script (LCS) for the instance.
*)failure_message : Smaws_Lib.Smithy_api.Types.string_ option;An error message describing why the instance creation or update failed, if applicable.
*)capacity_reservation : capacity_reservation option;Information about the Capacity Reservation used by the instance.
*)instance_requirements_eni_configurations : instance_requirements_eni_configurations
option;The ENI configurations for the instance types in the instance requirements, grouped by network interface category (for example, ENI-only or EFA with ENIs). At most one configuration per category.
*)additional_enis : additional_enis option;Information about additional Elastic Network Interfaces (ENIs) associated with the instance.
*)customer_eni : Smaws_Lib.Smithy_api.Types.string_ option;The ID of the customer-managed Elastic Network Interface (ENI) associated with the instance.
*)}Metadata information about an instance in a HyperPod cluster.
type nonrec event_metadata = | Instance of instance_metadataMetadata specific to instance-level events.
*)| InstanceGroupScaling of instance_group_scaling_metadataMetadata related to instance group scaling events.
*)| InstanceGroup of instance_group_metadataMetadata specific to instance group-level events.
*)| Cluster of cluster_metadataMetadata specific to cluster-level events.
*)Metadata associated with a cluster event, which may include details about various resource types.
type nonrec event_details = {event_metadata : event_metadata option;Metadata specific to the event, which may include information about the cluster, instance group, or instance involved.
*)}Detailed information about a specific event, including event metadata.
type nonrec cluster_event_detail = {event_level : cluster_event_level option;The severity level of the event. Valid values are Info, Warn, and Error.
description : Smaws_Lib.Smithy_api.Types.string_ option;A human-readable description of the event.
*)event_details : event_details option;Additional details about the event, including event-specific metadata.
*)event_time : timestamp;The timestamp when the event occurred.
*)resource_type : cluster_event_resource_type;The type of resource associated with the event. Valid values are Cluster, InstanceGroup, or Instance.
instance_id : Smaws_Lib.Smithy_api.Types.string_ option;The EC2 instance ID associated with the event, if applicable.
*)instance_group_name : cluster_instance_group_name option;The name of the instance group associated with the event, if applicable.
*)cluster_name : cluster_name;The name of the HyperPod cluster associated with the event.
*)cluster_arn : cluster_arn;The Amazon Resource Name (ARN) of the HyperPod cluster associated with the event.
*)event_id : event_id;The unique identifier (UUID) of the event.
*)}Detailed information about a specific event in a HyperPod cluster.
type nonrec describe_cluster_event_response = {event_details : cluster_event_detail option;Detailed information about the requested cluster event, including event metadata for various resource types such as Cluster, InstanceGroup, Instance, and their associated attributes.
}type nonrec describe_cluster_event_request = {cluster_name : cluster_name_or_arn;The name or Amazon Resource Name (ARN) of the HyperPod cluster associated with the event.
*)event_id : event_id;The unique identifier (UUID) of the event to describe. This ID can be obtained from the ListClusterEvents operation.
}type nonrec cluster_instance_requirement_details = {desired_instance_types : cluster_instance_types option;The desired instance types for the instance group, as specified in the most recent update request.
*)current_instance_types : cluster_instance_types option;The instance types currently in use by the instance group.
*)}The instance requirement details for a flexible instance group, including the current and desired instance types.
type nonrec cluster_instance_type_detail = {threads_per_core : cluster_threads_per_core option;The number of threads per CPU core for this instance type.
*)current_count : cluster_non_negative_instance_count option;The number of instances of this type currently running in the instance group.
*)instance_type : cluster_instance_type option;The instance type.
*)}Details about a specific instance type within a flexible instance group, including the count and configuration.
type nonrec cluster_instance_type_details = cluster_instance_type_detail listtype nonrec cluster_patch_schedule_details = {next_patch_date : timestamp option;The date and time of the next scheduled automatic patch.
*)}The schedule details for automatic patching, including the next scheduled patch date.
type nonrec cluster_auto_patch_config_details = {deployment_config : deployment_configuration option;The deployment configuration for rolling patch updates.
*)desired_patch_schedule : cluster_patch_schedule_details option;The requested patch schedule. Differs from CurrentPatchSchedule when a reschedule request is pending.
*)current_patch_schedule : cluster_patch_schedule_details option;The currently active patch schedule that the system will execute.
*)patching_strategy : cluster_patching_strategy option;The strategy used for applying patches to instances in the group.
*)}The auto-patching configuration details for the instance group, including the patching strategy and schedule.
type nonrec active_operations =
(active_cluster_operation_name * active_cluster_operation_count) listtype nonrec cluster_kubernetes_config_details = {desired_taints : cluster_kubernetes_taints option;The desired taints to be applied to cluster nodes of an instance group.
*)current_taints : cluster_kubernetes_taints option;The current taints applied to cluster nodes of an instance group.
*)desired_labels : cluster_kubernetes_labels option;The desired labels to be applied to cluster nodes of an instance group.
*)current_labels : cluster_kubernetes_labels option;The current labels applied to cluster nodes of an instance group.
*)}Detailed Kubernetes configuration showing both the current and desired state of labels and taints for cluster nodes.
type nonrec cluster_slurm_config_details = {partition_names : cluster_partition_names option;The list of Slurm partition names that the instance group belongs to.
*)node_type : cluster_slurm_node_type;The type of Slurm node for the instance group. Valid values are Controller, Worker, and Login.
}The Slurm configuration details for an instance group in a SageMaker HyperPod cluster.
type nonrec cluster_instance_group_details = {network_interface : cluster_network_interface_details option;The network interface configuration for the instance group.
*)slurm_config : cluster_slurm_config_details option;The Slurm configuration for the instance group.
*)active_software_update_config : deployment_configuration option;software_update_status : software_update_status option;Status of the last software udpate request.
Status transitions follow these possible sequences:
target_state_count : cluster_instance_count option;Represents the number of running nodes using the desired Image ID.
capacity_requirements : cluster_capacity_requirements option;The instance capacity requirements for the instance group.
*)kubernetes_config : cluster_kubernetes_config_details option;The Kubernetes configuration for the instance group that contains labels and taints to be applied for the nodes in this instance group.
*)active_operations : active_operations option;A map indicating active operations currently in progress for the instance group of a SageMaker HyperPod cluster. When there is a scaling operation in progress, this map contains a key Scaling with value 1.
image_version_status : cluster_image_version_status option;The status of the image version for the instance group. Indicates whether the instance group is running the latest image version or if an update is available.
*)desired_image_release_version : image_release_version option;The desired version of the HyperPod-managed AMI for the instance group. This may differ from the current version when an update is pending.
*)current_image_release_version : image_release_version option;The version of the HyperPod-managed AMI currently running on the instance group.
*)desired_image_id : image_id option;The ID of the Amazon Machine Image (AMI) desired for the instance group.
*)current_image_id : image_id option;The ID of the Amazon Machine Image (AMI) currently in use by the instance group.
*)auto_patch_config : cluster_auto_patch_config_details option;The auto-patching configuration for the instance group, including the current patching strategy and next scheduled patch date.
*)scheduled_update_config : scheduled_update_config option;The configuration object of the schedule that SageMaker follows when updating the AMI.
*)override_vpc_config : vpc_config option;The customized Amazon VPC configuration at the instance group level that overrides the default Amazon VPC configuration of the SageMaker HyperPod cluster.
*)training_plan_status : instance_group_training_plan_status option;The current status of the training plan associated with this cluster instance group.
*)training_plan_arn : training_plan_arn option;The Amazon Resource Name (ARN); of the training plan associated with this cluster instance group.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingPlan.html}CreateTrainingPlan} .
*)status : instance_group_status option;The current status of the cluster instance group.
InService: The instance group is active and healthy.Creating: The instance group is being provisioned.Updating: The instance group is being updated.Failed: The instance group has failed to provision or is no longer healthy.Degraded: The instance group is degraded, meaning that some instances have failed to provision or are no longer healthy.Deleting: The instance group is being deleted.on_start_deep_health_checks : on_start_deep_health_checks option;A flag indicating whether deep health checks should be performed when the cluster instance group is created or updated.
*)instance_storage_configs : cluster_instance_storage_configs option;The additional storage configurations for the instances in the SageMaker HyperPod cluster instance group.
*)threads_per_core : cluster_threads_per_core option;The number you specified to TreadsPerCore in CreateCluster for enabling or disabling multithreading. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.
execution_role : role_arn option;The execution role for the instance group to assume.
*)life_cycle_config : cluster_life_cycle_config option;Details of LifeCycle configuration for the instance group.
*)instance_type_details : cluster_instance_type_details option;Details about the instance types in the instance group, including the count and configuration of each instance type. This field is present for flexible instance groups that support multiple instance types.
*)instance_requirements : cluster_instance_requirement_details option;The instance requirements for the instance group, including the current and desired instance types. This field is present for flexible instance groups that support multiple instance types.
*)instance_type : cluster_instance_type option;The instance type of the instance group of a SageMaker HyperPod cluster.
*)instance_group_name : cluster_instance_group_name option;The name of the instance group of a SageMaker HyperPod cluster.
*)min_count : cluster_instance_count option;The minimum number of instances that must be available in the instance group of a SageMaker HyperPod cluster before it transitions to InService status.
target_count : cluster_instance_count option;The number of instances you specified to add to the instance group of a SageMaker HyperPod cluster.
*)current_count : cluster_non_negative_instance_count option;The number of instances that are currently in the instance group of a SageMaker HyperPod cluster.
*)}Details of an instance group in a SageMaker HyperPod cluster.
type nonrec cluster_instance_group_details_list =
cluster_instance_group_details listtype nonrec environment_config_details = {s3_output_path : s3_uri option;The Amazon S3 path where output data from the restricted instance group (RIG) environment will be stored.
*)f_sx_lustre_config : f_sx_lustre_config option;Configuration settings for an Amazon FSx for Lustre file system to be used with the cluster.
*)}The configuration details for the restricted instance groups (RIG) environment.
type nonrec cluster_restricted_instance_group_details = {environment_config : environment_config_details option;The configuration for the restricted instance groups (RIG) environment.
*)scheduled_update_config : scheduled_update_config option;override_vpc_config : vpc_config option;training_plan_status : instance_group_training_plan_status option;The current status of the training plan associated with this cluster restricted instance group.
*)training_plan_arn : training_plan_arn option;The Amazon Resource Name (ARN) of the training plan to filter clusters by. For more information about reserving GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see
{{:https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingPlan.html}CreateTrainingPlan} .
*)status : instance_group_status option;The current status of the cluster's restricted instance group.
InService: The restricted instance group is active and healthy.Creating: The restricted instance group is being provisioned.Updating: The restricted instance group is being updated.Failed: The restricted instance group has failed to provision or is no longer healthy.Degraded: The restricted instance group is degraded, meaning that some instances have failed to provision or are no longer healthy.Deleting: The restricted instance group is being deleted.on_start_deep_health_checks : on_start_deep_health_checks option;A flag indicating whether deep health checks should be performed when the cluster's restricted instance group is created or updated.
*)instance_storage_configs : cluster_instance_storage_configs option;The additional storage configurations for the instances in the SageMaker HyperPod cluster restricted instance group.
*)threads_per_core : cluster_threads_per_core option;The number you specified to TreadsPerCore in CreateCluster for enabling or disabling multithreading. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.
execution_role : role_arn option;The execution role for the restricted instance group to assume.
*)instance_type : cluster_instance_type option;The instance type of the restricted instance group of a SageMaker HyperPod cluster.
*)instance_group_name : cluster_instance_group_name option;The name of the restricted instance group of a SageMaker HyperPod cluster.
*)target_count : cluster_instance_count option;The number of instances you specified to add to the restricted instance group of a SageMaker HyperPod cluster.
*)current_count : cluster_non_negative_instance_count option;The number of instances that are currently in the restricted instance group of a SageMaker HyperPod cluster.
*)}The instance group details of the restricted instance group (RIG).
type nonrec cluster_restricted_instance_group_details_list =
cluster_restricted_instance_group_details listThe shared environment configuration details for the restricted instance groups (RIG).
The output configuration for the restricted instance groups (RIG) in the SageMaker HyperPod cluster.
type nonrec cluster_auto_scaling_config_output = {failure_message : Smaws_Lib.Smithy_api.Types.string_ option;If the autoscaling status is Failed, this field contains a message describing the failure.
status : cluster_auto_scaling_status;The current status of the autoscaling configuration. Valid values are InService, Failed, Creating, and Deleting.
auto_scaler_type : cluster_auto_scaler_type option;The type of autoscaler configured for the cluster.
*)mode : cluster_auto_scaling_mode;Describes whether autoscaling is enabled or disabled for the cluster.
*)}The autoscaling configuration and status information for a HyperPod cluster.
type nonrec describe_cluster_response = {auto_scaling : cluster_auto_scaling_config_output option;The current autoscaling configuration and status for the autoscaler.
*)cluster_role : role_arn option;The Amazon Resource Name (ARN) of the IAM role that HyperPod uses for cluster autoscaling operations.
*)node_provisioning_mode : cluster_node_provisioning_mode option;The mode used for provisioning nodes in the cluster.
*)node_recovery : cluster_node_recovery option;The node recovery mode configured for the SageMaker HyperPod cluster.
*)tiered_storage_config : cluster_tiered_storage_config option;The current configuration for managed tier checkpointing on the HyperPod cluster. For example, this shows whether the feature is enabled and the percentage of cluster memory allocated for checkpoint storage.
*)orchestrator : cluster_orchestrator option;The type of orchestrator used for the SageMaker HyperPod cluster.
*)vpc_config : vpc_config option;restricted_instance_groups_config : cluster_restricted_instance_groups_config_output
option;The configuration for the restricted instance groups (RIG) in the SageMaker HyperPod cluster.
*)restricted_instance_groups : cluster_restricted_instance_group_details_list
option;The specialized instance groups for training models like Amazon Nova to be created in the SageMaker HyperPod cluster.
*)instance_groups : cluster_instance_group_details_list;The instance groups of the SageMaker HyperPod cluster.
*)failure_message : string_ option;The failure message of the SageMaker HyperPod cluster.
*)creation_time : timestamp option;The time when the SageMaker Cluster is created.
*)cluster_status : cluster_status;The status of the SageMaker HyperPod cluster.
*)cluster_name : cluster_name option;The name of the SageMaker HyperPod cluster.
*)cluster_arn : cluster_arn;The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.
*)}type nonrec describe_cluster_request = {cluster_name : cluster_name_or_arn;The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.
*)}type nonrec auto_mls3_data_source = {s3_uri : s3_uri;The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.
*)s3_data_type : auto_mls3_data_type;The data type.
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker AI uses all objects that match the specified key name prefix for model training.
The S3Prefix should have the following format:
s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker AI to use for model training.
A ManifestFile should have the format shown below:
[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},
"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",
"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",
... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]
If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2).
Here is a minimal, single-record example of an AugmentedManifestFile:
{"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg",
"label-metadata": {"class-name": "cat" }
For more information on AugmentedManifestFile, see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.
}Describes the Amazon S3 data source.
type nonrec auto_ml_data_source = {s3_data_source : auto_mls3_data_source;The Amazon S3 location of the input data.
*)}The data source for the Autopilot job.
type nonrec auto_ml_job_channel = {data_source : auto_ml_data_source option;The data source for an AutoML channel (Required).
*)compression_type : compression_type option;The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None. If no compression type is provided, we default to None.
content_type : content_type option;The content type of the data from the input source. The following are the allowed content types for different problems:
text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.image/png, image/jpeg, or image/*. The default value is image/*.text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.channel_type : auto_ml_channel_type option;The type of channel. Defines whether the data are used for training or validation. The default value is training. Channels for training and validation must share the same ContentType
The type of channel defaults to training for the time-series forecasting problem type.
}A channel is a named input source that training algorithms can consume. This channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2).
type nonrec auto_ml_job_input_data_config = auto_ml_job_channel listtype nonrec auto_ml_output_data_config = {s3_output_path : s3_uri;The Amazon S3 output path. Must be 512 characters or less.
*)kms_key_id : kms_key_id option;The Key Management Service encryption key ID.
*)}The output data configuration.
type nonrec auto_ml_job_objective = {metric_name : auto_ml_metric_enum;The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
For tabular problem types:
List of available metrics:
MAE, MSE, R2, RMSEAccuracy, AUC, BalancedAccuracy, F1, Precision, RecallAccuracy, BalancedAccuracy, F1macro, PrecisionMacro, RecallMacroFor a description of each metric, see Autopilot metrics for classification and regression.
Default objective metrics:
MSE.F1.Accuracy.For image or text classification problem types:
List of available metrics: Accuracy
For a description of each metric, see Autopilot metrics for text and image classification.
AccuracyFor time-series forecasting problem types:
List of available metrics: RMSE, wQL, Average wQL, MASE, MAPE, WAPE
For a description of each metric, see Autopilot metrics for time-series forecasting.
AverageWeightedQuantileLossAutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.}Specifies a metric to minimize or maximize as the objective of an AutoML job.
type nonrec image_classification_job_config = {completion_criteria : auto_ml_job_completion_criteria option;How long a job is allowed to run, or how many candidates a job is allowed to generate.
*)}The collection of settings used by an AutoML job V2 for the image classification problem type.
type nonrec auto_ml_problem_type_config = | TextGenerationJobConfig of text_generation_job_configSettings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type.
The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions.
*)| TabularJobConfig of tabular_job_configSettings used to configure an AutoML job V2 for the tabular problem type (regression, classification).
*)| TimeSeriesForecastingJobConfig of time_series_forecasting_job_configSettings used to configure an AutoML job V2 for the time-series forecasting problem type.
*)| TextClassificationJobConfig of text_classification_job_configSettings used to configure an AutoML job V2 for the text classification problem type.
*)| ImageClassificationJobConfig of image_classification_job_configSettings used to configure an AutoML job V2 for the image classification problem type.
*)A collection of settings specific to the problem type used to configure an AutoML job V2. There must be one and only one config of the following type.
type nonrec auto_ml_job_artifacts = {data_exploration_notebook_location : data_exploration_notebook_location option;The URL of the notebook location.
*)candidate_definition_notebook_location : candidate_definition_notebook_location
option;The URL of the notebook location.
*)}The artifacts that are generated during an AutoML job.
type nonrec auto_ml_problem_type_resolved_attributes = | TextGenerationResolvedAttributes of text_generation_resolved_attributesThe resolved attributes for the text generation problem type.
*)| TabularResolvedAttributes of tabular_resolved_attributesThe resolved attributes for the tabular problem type.
*)Stores resolved attributes specific to the problem type of an AutoML job V2.
type nonrec auto_ml_resolved_attributes = {auto_ml_problem_type_resolved_attributes : auto_ml_problem_type_resolved_attributes
option;Defines the resolved attributes specific to a problem type.
*)completion_criteria : auto_ml_job_completion_criteria option;auto_ml_job_objective : auto_ml_job_objective option;}The resolved attributes used to configure an AutoML job V2.
type nonrec model_deploy_config = {endpoint_name : endpoint_name option;Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
Specify the EndpointName if and only if you set AutoGenerateEndpointName to False; otherwise a 400 error is thrown.
auto_generate_endpoint_name : auto_generate_endpoint_name option;Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False.
If you set AutoGenerateEndpointName to True, do not specify the EndpointName; otherwise a 400 error is thrown.
}Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
type nonrec model_deploy_result = {endpoint_name : endpoint_name option;The name of the endpoint to which the model has been deployed.
If model deployment fails, this field is omitted from the response.
*)}Provides information about the endpoint of the model deployment.
type nonrec auto_ml_data_split_config = {validation_fraction : validation_fraction option;The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.
*)}This structure specifies how to split the data into train and validation datasets.
The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob, the validation dataset must be less than 2 GB in size.
type nonrec auto_ml_security_config = {vpc_config : vpc_config option;The VPC configuration.
*)enable_inter_container_traffic_encryption : boolean_ option;Whether to use traffic encryption between the container layers.
*)volume_kms_key_id : kms_key_id option;The key used to encrypt stored data.
*)}Security options.
type nonrec emr_serverless_compute_config = {execution_role_ar_n : role_arn;The ARN of the IAM role granting the AutoML job V2 the necessary permissions access policies to list, connect to, or manage EMR Serverless jobs. For detailed information about the required permissions of this role, see "How to configure AutoML to initiate a remote job on EMR Serverless for large datasets" in Create a regression or classification job for tabular data using the AutoML API or Create an AutoML job for time-series forecasting using the API.
*)}This data type is intended for use exclusively by SageMaker Canvas and cannot be used in other contexts at the moment.
Specifies the compute configuration for the EMR Serverless job.
type nonrec auto_ml_compute_config = {emr_serverless_compute_config : emr_serverless_compute_config option;The configuration for using EMR Serverless to run the AutoML job V2.
To allow your AutoML job V2 to automatically initiate a remote job on EMR Serverless when additional compute resources are needed to process large datasets, you need to provide an EmrServerlessComputeConfig object, which includes an ExecutionRoleARN attribute, to the AutoMLComputeConfig of the AutoML job V2 input request.
By seamlessly transitioning to EMR Serverless when required, the AutoML job can handle datasets that would otherwise exceed the initially provisioned resources, without any manual intervention from you.
EMR Serverless is available for the tabular and time series problem types. We recommend setting up this option for tabular datasets larger than 5 GB and time series datasets larger than 30 GB.
*)}This data type is intended for use exclusively by SageMaker Canvas and cannot be used in other contexts at the moment.
Specifies the compute configuration for an AutoML job V2.
type nonrec describe_auto_ml_job_v2_response = {auto_ml_compute_config : auto_ml_compute_config option;The compute configuration used for the AutoML job V2.
*)security_config : auto_ml_security_config option;Returns the security configuration for traffic encryption or Amazon VPC settings.
*)data_split_config : auto_ml_data_split_config option;Returns the configuration settings of how the data are split into train and validation datasets.
*)model_deploy_result : model_deploy_result option;Provides information about endpoint for the model deployment.
*)model_deploy_config : model_deploy_config option;Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.
*)resolved_attributes : auto_ml_resolved_attributes option;Returns the resolved attributes used by the AutoML job V2.
*)auto_ml_job_artifacts : auto_ml_job_artifacts option;auto_ml_job_secondary_status : auto_ml_job_secondary_status;Returns the secondary status of the AutoML job V2.
*)auto_ml_job_status : auto_ml_job_status;Returns the status of the AutoML job V2.
*)best_candidate : auto_ml_candidate option;Information about the candidate produced by an AutoML training job V2, including its status, steps, and other properties.
*)partial_failure_reasons : auto_ml_partial_failure_reasons option;Returns a list of reasons for partial failures within an AutoML job V2.
*)failure_reason : auto_ml_failure_reason option;Returns the reason for the failure of the AutoML job V2, when applicable.
*)last_modified_time : timestamp;Returns the job's last modified time.
*)end_time : timestamp option;Returns the end time of the AutoML job V2.
*)creation_time : timestamp;Returns the creation time of the AutoML job V2.
*)auto_ml_problem_type_config_name : auto_ml_problem_type_config_name option;Returns the name of the problem type configuration set for the AutoML job V2.
*)auto_ml_problem_type_config : auto_ml_problem_type_config option;Returns the configuration settings of the problem type set for the AutoML job V2.
*)auto_ml_job_objective : auto_ml_job_objective option;Returns the job's objective.
*)role_arn : role_arn;The ARN of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.
*)output_data_config : auto_ml_output_data_config;Returns the job's output data config.
*)auto_ml_job_input_data_config : auto_ml_job_input_data_config;Returns an array of channel objects describing the input data and their location.
*)auto_ml_job_arn : auto_ml_job_arn;Returns the Amazon Resource Name (ARN) of the AutoML job V2.
*)auto_ml_job_name : auto_ml_job_name;Returns the name of the AutoML job V2.
*)}type nonrec describe_auto_ml_job_v2_request = {auto_ml_job_name : auto_ml_job_name;Requests information about an AutoML job V2 using its unique name.
*)}type nonrec auto_ml_channel = {sample_weight_attribute_name : sample_weight_attribute_name option;If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
Support for sample weights is available in Ensembling mode only.
*)channel_type : auto_ml_channel_type option;The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.
content_type : content_type option;The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.
target_attribute_name : target_attribute_name;The name of the target variable in supervised learning, usually represented by 'y'.
*)compression_type : compression_type option;You can use Gzip or None. The default value is None.
data_source : auto_ml_data_source option;The data source for an AutoML channel.
*)}A channel is a named input source that training algorithms can consume. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see Channel.
A validation dataset must contain the same headers as the training dataset.
type nonrec auto_ml_input_data_config = auto_ml_channel listtype nonrec auto_ml_candidate_generation_config = {algorithms_config : auto_ml_algorithms_config option;Stores the configuration information for the selection of algorithms trained on tabular data.
The list of available algorithms to choose from depends on the training mode set in TabularJobConfig.Mode.
AlgorithmsConfig should not be set if the training mode is set on AUTO.When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.
If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.
AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.
*)feature_specification_s3_uri : s3_uri option;A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. You can input FeatureAttributeNames (optional) in JSON format as shown below:
{ "FeatureAttributeNames":["col1", "col2", ...] }.
You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
These column keys may not include the target column.
In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.
If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data.
If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.
The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.
}Stores the configuration information for how a candidate is generated (optional).
type nonrec auto_ml_job_config = {mode : auto_ml_mode option;The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.
The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
data_split_config : auto_ml_data_split_config option;The configuration for splitting the input training dataset.
Type: AutoMLDataSplitConfig
*)candidate_generation_config : auto_ml_candidate_generation_config option;The configuration for generating a candidate for an AutoML job (optional).
*)security_config : auto_ml_security_config option;The security configuration for traffic encryption or Amazon VPC settings.
*)completion_criteria : auto_ml_job_completion_criteria option;How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
*)}A collection of settings used for an AutoML job.
type nonrec resolved_attributes = {completion_criteria : auto_ml_job_completion_criteria option;problem_type : problem_type option;The problem type.
*)auto_ml_job_objective : auto_ml_job_objective option;}The resolved attributes.
type nonrec describe_auto_ml_job_response = {model_deploy_result : model_deploy_result option;Provides information about endpoint for the model deployment.
*)model_deploy_config : model_deploy_config option;Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.
*)resolved_attributes : resolved_attributes option;Contains ProblemType, AutoMLJobObjective, and CompletionCriteria. If you do not provide these values, they are inferred.
auto_ml_job_artifacts : auto_ml_job_artifacts option;Returns information on the job's artifacts found in AutoMLJobArtifacts.
generate_candidate_definitions_only : generate_candidate_definitions_only
option;Indicates whether the output for an AutoML job generates candidate definitions only.
*)auto_ml_job_secondary_status : auto_ml_job_secondary_status;Returns the secondary status of the AutoML job.
*)auto_ml_job_status : auto_ml_job_status;Returns the status of the AutoML job.
*)best_candidate : auto_ml_candidate option;The best model candidate selected by SageMaker AI Autopilot using both the best objective metric and lowest InferenceLatency for an experiment.
*)partial_failure_reasons : auto_ml_partial_failure_reasons option;Returns a list of reasons for partial failures within an AutoML job.
*)failure_reason : auto_ml_failure_reason option;Returns the failure reason for an AutoML job, when applicable.
*)last_modified_time : timestamp;Returns the job's last modified time.
*)end_time : timestamp option;Returns the end time of the AutoML job.
*)creation_time : timestamp;Returns the creation time of the AutoML job.
*)auto_ml_job_config : auto_ml_job_config option;Returns the configuration for the AutoML job.
*)problem_type : problem_type option;Returns the job's problem type.
*)auto_ml_job_objective : auto_ml_job_objective option;Returns the job's objective.
*)role_arn : role_arn;The ARN of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.
*)output_data_config : auto_ml_output_data_config;Returns the job's output data config.
*)input_data_config : auto_ml_input_data_config;Returns the input data configuration for the AutoML job.
*)auto_ml_job_arn : auto_ml_job_arn;Returns the ARN of the AutoML job.
*)auto_ml_job_name : auto_ml_job_name;Returns the name of the AutoML job.
*)}type nonrec describe_auto_ml_job_request = {auto_ml_job_name : auto_ml_job_name;Requests information about an AutoML job using its unique name.
*)}type nonrec describe_artifact_response = {lineage_group_arn : lineage_group_arn option;The Amazon Resource Name (ARN) of the lineage group.
*)metadata_properties : metadata_properties option;last_modified_by : user_context option;last_modified_time : timestamp option;When the artifact was last modified.
*)created_by : user_context option;creation_time : timestamp option;When the artifact was created.
*)properties : lineage_entity_parameters option;A list of the artifact's properties.
*)artifact_type : string256 option;The type of the artifact.
*)source : artifact_source option;The source of the artifact.
*)artifact_arn : artifact_arn option;The Amazon Resource Name (ARN) of the artifact.
*)artifact_name : experiment_entity_name_or_arn option;The name of the artifact.
*)}type nonrec describe_artifact_request = {artifact_arn : artifact_arn;The Amazon Resource Name (ARN) of the artifact to describe.
*)}type nonrec describe_app_image_config_response = {code_editor_app_image_config : code_editor_app_image_config option;The configuration of the Code Editor app.
*)jupyter_lab_app_image_config : jupyter_lab_app_image_config option;The configuration of the JupyterLab app.
*)kernel_gateway_image_config : kernel_gateway_image_config option;The configuration of a KernelGateway app.
*)last_modified_time : timestamp option;When the AppImageConfig was last modified.
*)creation_time : timestamp option;When the AppImageConfig was created.
*)app_image_config_name : app_image_config_name option;The name of the AppImageConfig.
*)app_image_config_arn : app_image_config_arn option;The ARN of the AppImageConfig.
*)}type nonrec describe_app_image_config_request = {app_image_config_name : app_image_config_name;The name of the AppImageConfig to describe.
*)}type nonrec describe_app_response = {built_in_lifecycle_config_arn : studio_lifecycle_config_arn option;The lifecycle configuration that runs before the default lifecycle configuration
*)resource_spec : resource_spec option;The instance type and the Amazon Resource Name (ARN) of the SageMaker AI image created on the instance.
*)failure_reason : failure_reason option;The failure reason.
*)creation_time : timestamp option;The creation time of the application.
After an application has been shut down for 24 hours, SageMaker AI deletes all metadata for the application. To be considered an update and retain application metadata, applications must be restarted within 24 hours after the previous application has been shut down. After this time window, creation of an application is considered a new application rather than an update of the previous application.
*)last_user_activity_timestamp : timestamp option;The timestamp of the last user's activity. LastUserActivityTimestamp is also updated when SageMaker AI performs health checks without user activity. As a result, this value is set to the same value as LastHealthCheckTimestamp.
last_health_check_timestamp : timestamp option;The timestamp of the last health check.
*)recovery_mode : boolean_ option;Indicates whether the application is launched in recovery mode.
*)effective_trusted_identity_propagation_status : feature_status option;The effective status of Trusted Identity Propagation (TIP) for this application. When enabled, user identities from IAM Identity Center are being propagated through the application to TIP enabled Amazon Web Services services. When disabled, standard IAM role-based access is used.
*)status : app_status option;The status.
*)space_name : space_name option;The name of the space. If this value is not set, then UserProfileName must be set.
user_profile_name : user_profile_name option;The user profile name.
*)domain_id : domain_id option;The domain ID.
*)app_name : app_name option;The name of the app.
*)app_type : app_type option;The type of app.
*)app_arn : app_arn option;The Amazon Resource Name (ARN) of the app.
*)}type nonrec describe_app_request = {app_name : app_name;The name of the app.
*)app_type : app_type;The type of app.
*)space_name : space_name option;The name of the space.
*)user_profile_name : user_profile_name option;The user profile name. If this value is not set, then SpaceName must be set.
domain_id : domain_id;The domain ID.
*)}type nonrec algorithm_validation_profile = {transform_job_definition : transform_job_definition option;The TransformJobDefinition object that describes the transform job that SageMaker runs to validate your algorithm.
training_job_definition : training_job_definition;The TrainingJobDefinition object that describes the training job that SageMaker runs to validate your algorithm.
profile_name : entity_name;The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
*)}Defines a training job and a batch transform job that SageMaker runs to validate your algorithm.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
type nonrec algorithm_validation_profiles = algorithm_validation_profile listtype nonrec algorithm_validation_specification = {validation_profiles : algorithm_validation_profiles;An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.
validation_role : role_arn;The IAM roles that SageMaker uses to run the training jobs.
*)}Specifies configurations for one or more training jobs that SageMaker runs to test the algorithm.
type nonrec algorithm_status_item = {failure_reason : string_ option;if the overall status is Failed, the reason for the failure.
status : detailed_algorithm_status;The current status.
*)name : entity_name;The name of the algorithm for which the overall status is being reported.
*)}Represents the overall status of an algorithm.
type nonrec algorithm_status_item_list = algorithm_status_item listtype nonrec algorithm_status_details = {image_scan_statuses : algorithm_status_item_list option;The status of the scan of the algorithm's Docker image container.
*)validation_statuses : algorithm_status_item_list option;The status of algorithm validation.
*)}Specifies the validation and image scan statuses of the algorithm.
type nonrec describe_algorithm_output = {certify_for_marketplace : certify_for_marketplace option;Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.
*)product_id : product_id option;The product identifier of the algorithm.
*)algorithm_status_details : algorithm_status_details;Details about the current status of the algorithm.
*)algorithm_status : algorithm_status;The current status of the algorithm.
*)validation_specification : algorithm_validation_specification option;Details about configurations for one or more training jobs that SageMaker runs to test the algorithm.
*)inference_specification : inference_specification option;Details about inference jobs that the algorithm runs.
*)training_specification : training_specification;Details about training jobs run by this algorithm.
*)creation_time : creation_time;A timestamp specifying when the algorithm was created.
*)algorithm_description : entity_description option;A brief summary about the algorithm.
*)algorithm_arn : algorithm_arn;The Amazon Resource Name (ARN) of the algorithm.
*)algorithm_name : entity_name;The name of the algorithm being described.
*)}type nonrec describe_algorithm_input = {algorithm_name : arn_or_name;The name of the algorithm to describe.
*)}The Amazon S3 data source for an AI workload.
type nonrec ai_workload_data_source = {s3_data_source : ai_workload_s3_data_source option;The Amazon S3 data source configuration.
*)}The data source for an AI workload input data channel.
type nonrec ai_workload_input_data_config = {data_source : ai_workload_data_source;The data source for this channel.
*)channel_name : ai_channel_name;The logical name for the data channel.
*)}A channel of input data for an AI workload configuration. Each channel has a name and a data source.
type nonrec ai_workload_input_data_config_list =
ai_workload_input_data_config listtype nonrec ai_dataset_config = | InputDataConfig of ai_workload_input_data_config_listAn array of input data channel configurations for the workload.
*)The dataset configuration for an AI workload. This is a union type — specify one of the members.
type nonrec ai_workload_configs = {workload_spec : workload_spec;The workload specification that defines benchmark parameters.
*)}The benchmark tool configuration for an AI workload.
type nonrec describe_ai_workload_config_response = {creation_time : timestamp;A timestamp that indicates when the AI workload configuration was created.
*)ai_workload_configs : ai_workload_configs option;The benchmark tool configuration and workload specification.
*)dataset_config : ai_dataset_config option;The dataset configuration for the workload.
*)ai_workload_config_arn : ai_workload_config_arn;The Amazon Resource Name (ARN) of the AI workload configuration.
*)ai_workload_config_name : ai_entity_name;The name of the AI workload configuration.
*)}type nonrec describe_ai_workload_config_request = {ai_workload_config_name : ai_entity_name;The name of the AI workload configuration to describe.
*)}type nonrec ai_model_source_s3 = {s3_uri : s3_uri option;The Amazon S3 URI of the model artifacts.
*)}The Amazon S3 model source configuration.
type nonrec ai_model_source = | S3 of ai_model_source_s3The Amazon S3 location of the model artifacts.
*)The source of the model for an AI recommendation job. This is a union type.
type nonrec ai_mlflow_config = {mlflow_run_name : ai_mlflow_run_name option;The MLflow run name used for tracking.
*)mlflow_experiment_name : ai_mlflow_experiment_name option;The MLflow experiment name used for tracking.
*)mlflow_resource_arn : ai_mlflow_resource_arn;The Amazon Resource Name (ARN) of the SageMaker managed MLflow resource.
*)}The MLflow tracking configuration for logging metrics and parameters to a SageMaker managed MLflow tracking server.
type nonrec ai_recommendation_output_result = {mlflow_config : ai_mlflow_config option;The MLflow tracking configuration for the job.
*)model_package_group_identifier : ai_resource_identifier option;The name or Amazon Resource Name (ARN) of the model package group where deployment-ready model packages are registered.
*)s3_output_location : s3_uri;The Amazon S3 URI where the recommendation job writes its output results.
*)}The output configuration for an AI recommendation job, including the S3 location for results and the model package group for deployment.
type nonrec ai_recommendation_inference_specification = {framework : ai_recommendation_inference_framework option;The inference framework. Valid values are LMI and VLLM.
}The inference framework for an AI recommendation job.
type nonrec ai_recommendation_constraint = {metric : ai_recommendation_metric;The performance metric. Valid values are ttft-ms (time to first token in milliseconds), throughput, and cost.
}A performance constraint for an AI recommendation job.
type nonrec ai_recommendation_constraint_list =
ai_recommendation_constraint listtype nonrec ai_recommendation_performance_target = {constraints : ai_recommendation_constraint_list;An array of performance constraints that define the optimization objectives.
*)}The performance targets for an AI recommendation job.
type nonrec ai_recommendation_optimization_detail = {optimization_config : ai_recommendation_optimization_config_map option;A map of configuration parameters for the optimization technique.
*)optimization_type : ai_recommendation_optimization_type;The type of optimization. Valid values are SpeculativeDecoding and KernelTuning.
}Details about an optimization technique applied in a recommendation.
type nonrec ai_recommendation_optimization_detail_list =
ai_recommendation_optimization_detail listtype nonrec ai_recommendation_instance_type = | ML_P6_B200_48XLARGE| ML_P5EN_48XLARGE| ML_P5E_48XLARGE| ML_P5_48XLARGE| ML_P5_4XLARGE| ML_P4DE_24XLARGE| ML_P4D_24XLARGE| ML_P3_16XLARGE| ML_P3_8XLARGE| ML_P3_2XLARGE| ML_G7E_48XLARGE| ML_G7E_24XLARGE| ML_G7E_12XLARGE| ML_G7E_8XLARGE| ML_G7E_4XLARGE| ML_G7E_2XLARGE| ML_G6E_48XLARGE| ML_G6E_24XLARGE| ML_G6E_16XLARGE| ML_G6E_12XLARGE| ML_G6E_8XLARGE| ML_G6E_4XLARGE| ML_G6E_2XLARGE| ML_G6E_XLARGE| ML_G6_48XLARGE| ML_G6_24XLARGE| ML_G6_16XLARGE| ML_G6_12XLARGE| ML_G6_8XLARGE| ML_G6_4XLARGE| ML_G6_2XLARGE| ML_G6_XLARGE| ML_G5_48XLARGE| ML_G5_24XLARGE| ML_G5_16XLARGE| ML_G5_12XLARGE| ML_G5_8XLARGE| ML_G5_4XLARGE| ML_G5_2XLARGE| ML_G5_XLARGEtype nonrec ai_recommendation_instance_detail = {copy_count_per_instance : ai_recommendation_copy_count_per_instance option;The number of model copies per instance.
*)instance_count : ai_recommendation_instance_count option;The recommended number of instances.
*)instance_type : ai_recommendation_instance_type option;The recommended instance type.
*)}Instance details for a recommendation.
type nonrec ai_recommendation_instance_detail_list =
ai_recommendation_instance_detail listtype nonrec ai_recommendation_model_details = {instance_details : ai_recommendation_instance_detail_list option;The instance details for this recommendation, including instance type, count, and model copies per instance.
*)inference_specification_name : ai_inference_specification_name option;The name of the inference specification within the model package.
*)model_package_arn : model_package_arn option;The Amazon Resource Name (ARN) of the model package.
*)}Details about the model package in a recommendation.
type nonrec ai_recommendation_deployment_s3_channel = {uri : s3_uri option;The Amazon S3 URI of the data for this channel.
*)channel_name : ai_channel_name option;A custom name for this Amazon S3 data channel.
*)}An Amazon S3 data channel for a recommended deployment configuration, containing model artifacts or optimized model outputs.
type nonrec ai_recommendation_deployment_s3_channel_list =
ai_recommendation_deployment_s3_channel listtype nonrec ai_recommendation_deployment_configuration = {environment_variables : environment_map option;The environment variables for the deployment.
*)copy_count_per_instance : ai_recommendation_copy_count_per_instance option;The number of model copies per instance.
*)instance_count : ai_recommendation_instance_count option;The recommended number of instances for the deployment.
*)instance_type : ai_recommendation_instance_type option;The recommended instance type for the deployment.
*)image_uri : string_ option;The URI of the container image for the deployment.
*)s3 : ai_recommendation_deployment_s3_channel_list option;The Amazon S3 data channels for the deployment.
*)}The deployment configuration for a recommendation.
type nonrec ai_recommendation_performance_metric = {unit_ : string_ option;The unit of the metric value.
*)value : string_;The value of the metric.
*)stat : string_ option;The statistical measure for the metric.
*)metric : string_;The name of the performance metric.
*)}An expected performance metric for a recommendation.
type nonrec expected_performance_list =
ai_recommendation_performance_metric listtype nonrec ai_recommendation = {expected_performance : expected_performance_list option;The expected performance metrics for this recommendation.
*)ai_benchmark_job_arn : ai_benchmark_job_arn option;The Amazon Resource Name (ARN) of the benchmark job associated with this recommendation.
*)deployment_configuration : ai_recommendation_deployment_configuration option;The deployment configuration for this recommendation, including the container image, instance type, instance count, and environment variables.
*)model_details : ai_recommendation_model_details option;Details about the model package associated with this recommendation.
*)optimization_details : ai_recommendation_optimization_detail_list option;The optimization techniques applied in this recommendation.
*)recommendation_description : string_ option;A description of the recommendation.
*)}An optimization recommendation generated by an AI recommendation job.
type nonrec ai_recommendation_list = ai_recommendation listtype nonrec ai_recommendation_instance_type_list =
ai_recommendation_instance_type listtype nonrec ai_ml_reservation_arn_list = ai_ml_reservation_arn listtype nonrec ai_capacity_reservation_config = {ml_reservation_arns : ai_ml_reservation_arn_list option;The list of ML reservation ARNs to use.
*)capacity_reservation_preference : ai_capacity_reservation_preference option;The capacity reservation preference. The only valid value is capacity-reservations-only.
}The capacity reservation configuration for an AI recommendation job.
type nonrec ai_recommendation_compute_spec = {capacity_reservation_config : ai_capacity_reservation_config option;The capacity reservation configuration.
*)instance_types : ai_recommendation_instance_type_list option;The list of instance types to consider for recommendations. You can specify up to 3 instance types.
*)}The compute resource specification for an AI recommendation job.
type nonrec describe_ai_recommendation_job_response = {end_time : timestamp option;A timestamp that indicates when the recommendation job completed.
*)start_time : timestamp option;A timestamp that indicates when the recommendation job started running.
*)creation_time : timestamp;A timestamp that indicates when the recommendation job was created.
*)compute_spec : ai_recommendation_compute_spec option;The compute resource specification for the recommendation job.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of the IAM role used by the recommendation job.
*)recommendations : ai_recommendation_list option;The list of optimization recommendations generated by the job. Each recommendation includes optimization details, deployment configuration, expected performance metrics, and the associated benchmark job ARN.
*)performance_target : ai_recommendation_performance_target option;The performance targets specified for the recommendation job.
*)optimize_model : ai_recommendation_allow_optimization option;Whether model optimization techniques were allowed.
*)ai_workload_config_identifier : ai_resource_identifier;The name or Amazon Resource Name (ARN) of the AI workload configuration used for this recommendation job.
*)inference_specification : ai_recommendation_inference_specification option;The inference framework configuration.
*)output_config : ai_recommendation_output_result;The output configuration for the recommendation job.
*)model_source : ai_model_source;The source of the model that was analyzed.
*)failure_reason : failure_reason option;If the recommendation job failed, the reason it failed.
*)ai_recommendation_job_status : ai_recommendation_job_status;The status of the AI recommendation job.
*)ai_recommendation_job_arn : ai_recommendation_job_arn;The Amazon Resource Name (ARN) of the AI recommendation job.
*)ai_recommendation_job_name : ai_entity_name;The name of the AI recommendation job.
*)}type nonrec describe_ai_recommendation_job_request = {ai_recommendation_job_name : ai_entity_name;The name of the AI recommendation job to describe.
*)}type nonrec ai_benchmark_inference_component = {identifier : ai_resource_identifier;The name or Amazon Resource Name (ARN) of the inference component.
*)}An inference component to benchmark.
type nonrec ai_benchmark_inference_component_list =
ai_benchmark_inference_component listtype nonrec ai_benchmark_endpoint = {inference_components : ai_benchmark_inference_component_list option;The list of inference components to benchmark on the endpoint.
*)target_container_hostname : string_ option;The hostname of the specific container to target within a multi-container endpoint.
*)identifier : ai_resource_identifier;The name or Amazon Resource Name (ARN) of the SageMaker endpoint to benchmark.
*)}The SageMaker endpoint configuration for benchmarking.
type nonrec ai_benchmark_target = | Endpoint of ai_benchmark_endpointThe SageMaker endpoint to benchmark.
*)The target for an AI benchmark job. This is a union type — specify one of the members.
type nonrec ai_cloud_watch_logs = {log_stream_name : string_ option;The name of the CloudWatch log stream.
*)log_group_arn : string_ option;The Amazon Resource Name (ARN) of the CloudWatch log group.
*)}CloudWatch log information for an AI benchmark or recommendation job.
type nonrec ai_cloud_watch_logs_list = ai_cloud_watch_logs listtype nonrec ai_benchmark_output_result = {mlflow_config : ai_mlflow_config option;The MLflow tracking configuration for the job.
*)cloud_watch_logs : ai_cloud_watch_logs_list option;The CloudWatch log information for the benchmark job.
*)s3_output_location : s3_uri;The Amazon S3 URI where benchmark results are stored.
*)}The output result of an AI benchmark job, including the Amazon S3 location and CloudWatch log information.
type nonrec ai_benchmark_network_config = {vpc_config : vpc_config option;The VPC configuration, including security group IDs and subnet IDs.
*)}The network configuration for an AI benchmark job.
type nonrec describe_ai_benchmark_job_response = {end_time : timestamp option;A timestamp that indicates when the benchmark job completed.
*)start_time : timestamp option;A timestamp that indicates when the benchmark job started running.
*)creation_time : timestamp;A timestamp that indicates when the benchmark job was created.
*)network_config : ai_benchmark_network_config option;The network configuration for the benchmark job.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of the IAM role used by the benchmark job.
*)ai_workload_config_identifier : ai_resource_identifier;The name or Amazon Resource Name (ARN) of the AI workload configuration used for this benchmark job.
*)output_config : ai_benchmark_output_result;The output configuration for the benchmark job, including the Amazon S3 output location and CloudWatch log information.
*)benchmark_target : ai_benchmark_target;The target endpoint that was benchmarked.
*)failure_reason : failure_reason option;If the benchmark job failed, the reason it failed.
*)ai_benchmark_job_status : ai_benchmark_job_status;The status of the AI benchmark job.
*)ai_benchmark_job_arn : ai_benchmark_job_arn;The Amazon Resource Name (ARN) of the AI benchmark job.
*)ai_benchmark_job_name : ai_entity_name;The name of the AI benchmark job.
*)}type nonrec describe_ai_benchmark_job_request = {ai_benchmark_job_name : ai_entity_name;The name of the AI benchmark job to describe.
*)}type nonrec describe_action_response = {lineage_group_arn : lineage_group_arn option;The Amazon Resource Name (ARN) of the lineage group.
*)metadata_properties : metadata_properties option;last_modified_by : user_context option;last_modified_time : timestamp option;When the action was last modified.
*)created_by : user_context option;creation_time : timestamp option;When the action was created.
*)properties : lineage_entity_parameters option;A list of the action's properties.
*)status : action_status option;The status of the action.
*)description : experiment_description option;The description of the action.
*)action_type : string256 option;The type of the action.
*)source : action_source option;The source of the action.
*)action_arn : action_arn option;The Amazon Resource Name (ARN) of the action.
*)action_name : experiment_entity_name_or_arn option;The name of the action.
*)}type nonrec describe_action_request = {action_name : experiment_entity_name_or_arn;The name of the action to describe.
*)}type nonrec deregister_devices_request = {device_names : device_names;The unique IDs of the devices.
*)device_fleet_name : entity_name;The name of the fleet the devices belong to.
*)}type nonrec delete_workteam_response = {success : success;Returns true if the work team was successfully deleted; otherwise, returns false.
}type nonrec delete_workteam_request = {workteam_name : workteam_name;The name of the work team to delete.
*)}type nonrec delete_workforce_request = {workforce_name : workforce_name;The name of the workforce.
*)}type nonrec delete_user_profile_request = {user_profile_name : user_profile_name;The user profile name.
*)domain_id : domain_id;The domain ID.
*)}type nonrec delete_trial_component_response = {trial_component_arn : trial_component_arn option;The Amazon Resource Name (ARN) of the component is being deleted.
*)}type nonrec delete_trial_component_request = {trial_component_name : experiment_entity_name;The name of the component to delete.
*)}type nonrec delete_trial_response = {trial_arn : trial_arn option;The Amazon Resource Name (ARN) of the trial that is being deleted.
*)}type nonrec delete_trial_request = {trial_name : experiment_entity_name;The name of the trial to delete.
*)}type nonrec delete_training_job_request = {training_job_name : training_job_name;The name of the training job to delete.
*)}type nonrec delete_studio_lifecycle_config_request = {studio_lifecycle_config_name : studio_lifecycle_config_name;The name of the Amazon SageMaker AI Studio Lifecycle Configuration to delete.
*)}type nonrec delete_space_request = {space_name : space_name;The name of the space.
*)domain_id : domain_id;The ID of the associated domain.
*)}type nonrec delete_project_input = {project_name : project_entity_name;The name of the project to delete.
*)}type nonrec delete_processing_job_request = {processing_job_name : processing_job_name;The name of the processing job to delete.
*)}type nonrec delete_pipeline_response = {pipeline_arn : pipeline_arn option;The Amazon Resource Name (ARN) of the pipeline to delete.
*)}type nonrec delete_pipeline_request = {client_request_token : idempotency_token;A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
*)pipeline_name : pipeline_name;The name of the pipeline to delete.
*)}type nonrec delete_partner_app_response = {arn : partner_app_arn option;The ARN of the SageMaker Partner AI App that was deleted.
*)}type nonrec delete_partner_app_request = {client_token : client_token option;A unique token that guarantees that the call to this API is idempotent.
*)arn : partner_app_arn;The ARN of the SageMaker Partner AI App to delete.
*)}type nonrec delete_optimization_job_request = {optimization_job_name : entity_name;The name that you assigned to the optimization job.
*)}type nonrec delete_notebook_instance_lifecycle_config_input = {notebook_instance_lifecycle_config_name : notebook_instance_lifecycle_config_name;The name of the lifecycle configuration to delete.
*)}type nonrec delete_notebook_instance_input = {notebook_instance_name : notebook_instance_name;The name of the SageMaker AI notebook instance to delete.
*)}type nonrec delete_monitoring_schedule_request = {monitoring_schedule_name : monitoring_schedule_name;The name of the monitoring schedule to delete.
*)}type nonrec delete_model_quality_job_definition_request = {job_definition_name : monitoring_job_definition_name;The name of the model quality monitoring job definition to delete.
*)}type nonrec delete_model_package_group_policy_input = {model_package_group_name : entity_name;The name of the model group for which to delete the policy.
*)}type nonrec delete_model_package_group_input = {model_package_group_name : arn_or_name;The name of the model group to delete.
*)}type nonrec delete_model_package_input = {model_package_name : versioned_arn_or_name;The name or Amazon Resource Name (ARN) of the model package to delete.
When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
*)}type nonrec delete_model_explainability_job_definition_request = {job_definition_name : monitoring_job_definition_name;The name of the model explainability job definition to delete.
*)}type nonrec delete_model_card_request = {model_card_name : entity_name;The name of the model card to delete.
*)}type nonrec delete_model_bias_job_definition_request = {job_definition_name : monitoring_job_definition_name;The name of the model bias job definition to delete.
*)}type nonrec delete_mlflow_tracking_server_response = {tracking_server_arn : tracking_server_arn option;A TrackingServerArn object, the ARN of the tracking server that is deleted if successfully found.
}type nonrec delete_mlflow_tracking_server_request = {tracking_server_name : tracking_server_name;The name of the the tracking server to delete.
*)}type nonrec delete_mlflow_app_response = {arn : mlflow_app_arn option;The ARN of the deleted MLflow App.
*)}type nonrec delete_mlflow_app_request = {arn : mlflow_app_arn;The ARN of the MLflow App to delete.
*)}type nonrec delete_job_request = {job_category : job_category;The category of the job to delete.
*)job_name : job_name;The name of the job to delete.
*)}type nonrec delete_inference_experiment_response = {inference_experiment_arn : inference_experiment_arn;The ARN of the deleted inference experiment.
*)}type nonrec delete_inference_experiment_request = {name : inference_experiment_name;The name of the inference experiment you want to delete.
*)}type nonrec delete_inference_component_input = {inference_component_name : inference_component_name;The name of the inference component to delete.
*)}type nonrec delete_image_version_request = {alias : sage_maker_image_version_alias option;The alias of the image to delete.
*)version : image_version_number option;The version to delete.
*)image_name : image_name;The name of the image to delete.
*)}type nonrec delete_hyper_parameter_tuning_job_request = {hyper_parameter_tuning_job_name : hyper_parameter_tuning_job_name;The name of the hyperparameter tuning job that you want to delete.
*)}type nonrec delete_human_task_ui_request = {human_task_ui_name : human_task_ui_name;The name of the human task user interface (work task template) you want to delete.
*)}type nonrec delete_hub_content_reference_request = {hub_content_name : hub_content_name;The name of the hub content to delete.
*)hub_content_type : hub_content_type;The type of hub content reference to delete. The only supported type of hub content reference to delete is ModelReference.
hub_name : hub_name_or_arn;The name of the hub to delete the hub content reference from.
*)}type nonrec delete_hub_content_request = {hub_content_version : hub_content_version;The version of the content that you want to delete from a hub.
*)hub_content_name : hub_content_name;The name of the content that you want to delete from a hub.
*)hub_content_type : hub_content_type;The type of content that you want to delete from a hub.
*)hub_name : hub_name_or_arn;The name of the hub that you want to delete content in.
*)}type nonrec delete_flow_definition_request = {flow_definition_name : flow_definition_name;The name of the flow definition you are deleting.
*)}type nonrec delete_feature_group_request = {feature_group_name : feature_group_name;The name of the FeatureGroup you want to delete. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
}type nonrec delete_experiment_response = {experiment_arn : experiment_arn option;The Amazon Resource Name (ARN) of the experiment that is being deleted.
*)}type nonrec delete_experiment_request = {experiment_name : experiment_entity_name;The name of the experiment to delete.
*)}type nonrec delete_endpoint_config_input = {endpoint_config_name : endpoint_config_name;The name of the endpoint configuration that you want to delete.
*)}type nonrec delete_endpoint_input = {endpoint_name : endpoint_name;The name of the endpoint that you want to delete.
*)}type nonrec delete_edge_deployment_stage_request = {stage_name : entity_name;The name of the stage.
*)edge_deployment_plan_name : entity_name;The name of the edge deployment plan from which the stage will be deleted.
*)}type nonrec delete_edge_deployment_plan_request = {edge_deployment_plan_name : entity_name;The name of the edge deployment plan to delete.
*)}type nonrec retention_policy = {home_efs_file_system : retention_type option;The default is Retain, which specifies to keep the data stored on the Amazon EFS volume.
Specify Delete to delete the data stored on the Amazon EFS volume.
}The retention policy for data stored on an Amazon Elastic File System volume.
type nonrec delete_domain_request = {retention_policy : retention_policy option;The retention policy for this domain, which specifies whether resources will be retained after the Domain is deleted. By default, all resources are retained (not automatically deleted).
*)domain_id : domain_id;The domain ID.
*)}type nonrec delete_device_fleet_request = {device_fleet_name : entity_name;The name of the fleet to delete.
*)}type nonrec delete_data_quality_job_definition_request = {job_definition_name : monitoring_job_definition_name;The name of the data quality monitoring job definition to delete.
*)}type nonrec delete_context_response = {context_arn : context_arn option;The Amazon Resource Name (ARN) of the context.
*)}type nonrec delete_context_request = {context_name : context_name;The name of the context to delete.
*)}type nonrec delete_compute_quota_request = {compute_quota_id : compute_quota_id;ID of the compute allocation definition.
*)}type nonrec delete_compilation_job_request = {compilation_job_name : entity_name;The name of the compilation job to delete.
*)}type nonrec delete_code_repository_input = {code_repository_name : entity_name;The name of the Git repository to delete.
*)}type nonrec delete_cluster_scheduler_config_request = {cluster_scheduler_config_id : cluster_scheduler_config_id;ID of the cluster policy.
*)}type nonrec delete_cluster_response = {cluster_arn : cluster_arn;The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster to delete.
*)}type nonrec delete_cluster_request = {cluster_name : cluster_name_or_arn;The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster to delete.
*)}type nonrec delete_association_response = {destination_arn : association_entity_arn option;The Amazon Resource Name (ARN) of the destination.
*)source_arn : association_entity_arn option;The ARN of the source.
*)}type nonrec delete_association_request = {destination_arn : association_entity_arn;The Amazon Resource Name (ARN) of the destination.
*)source_arn : association_entity_arn;The ARN of the source.
*)}type nonrec delete_artifact_response = {artifact_arn : artifact_arn option;The Amazon Resource Name (ARN) of the artifact.
*)}type nonrec delete_artifact_request = {source : artifact_source option;The URI of the source.
*)artifact_arn : artifact_arn option;The Amazon Resource Name (ARN) of the artifact to delete.
*)}type nonrec delete_app_image_config_request = {app_image_config_name : app_image_config_name;The name of the AppImageConfig to delete.
*)}type nonrec delete_app_request = {app_name : app_name;The name of the app.
*)app_type : app_type;The type of app.
*)space_name : space_name option;The name of the space. If this value is not set, then UserProfileName must be set.
user_profile_name : user_profile_name option;The user profile name. If this value is not set, then SpaceName must be set.
domain_id : domain_id;The domain ID.
*)}type nonrec delete_algorithm_input = {algorithm_name : entity_name;The name of the algorithm to delete.
*)}type nonrec delete_ai_workload_config_response = {ai_workload_config_arn : ai_workload_config_arn option;The Amazon Resource Name (ARN) of the deleted AI workload configuration.
*)}type nonrec delete_ai_workload_config_request = {ai_workload_config_name : ai_entity_name;The name of the AI workload configuration to delete.
*)}type nonrec delete_ai_recommendation_job_response = {ai_recommendation_job_arn : ai_recommendation_job_arn option;The Amazon Resource Name (ARN) of the deleted recommendation job.
*)}type nonrec delete_ai_recommendation_job_request = {ai_recommendation_job_name : ai_entity_name;The name of the AI recommendation job to delete.
*)}type nonrec delete_ai_benchmark_job_response = {ai_benchmark_job_arn : ai_benchmark_job_arn option;The Amazon Resource Name (ARN) of the deleted benchmark job.
*)}type nonrec delete_ai_benchmark_job_request = {ai_benchmark_job_name : ai_entity_name;The name of the AI benchmark job to delete.
*)}type nonrec delete_action_response = {action_arn : action_arn option;The Amazon Resource Name (ARN) of the action.
*)}type nonrec delete_action_request = {action_name : experiment_entity_name;The name of the action to delete.
*)}type nonrec create_workteam_response = {workteam_arn : workteam_arn option;The Amazon Resource Name (ARN) of the work team. You can use this ARN to identify the work team.
*)}type nonrec create_workteam_request = {worker_access_configuration : worker_access_configuration option;Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.
*)notification_configuration : notification_configuration option;Configures notification of workers regarding available or expiring work items.
*)description : string200;A description of the work team.
*)member_definitions : member_definitions;A list of MemberDefinition objects that contains objects that identify the workers that make up the work team.
Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition. For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition. Do not provide input for both of these parameters in a single request.
For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups within the user pool used to create a workforce. All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more information about user pools, see Amazon Cognito User Pools.
For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in OidcMemberDefinition by listing those groups in Groups.
workforce_name : workforce_name option;The name of the workforce.
*)workteam_name : workteam_name;The name of the work team. Use this name to identify the work team.
*)}type nonrec create_workforce_response = {workforce_arn : workforce_arn;The Amazon Resource Name (ARN) of the workforce.
*)}type nonrec create_workforce_request = {ip_address_type : workforce_ip_address_type option;Use this parameter to specify whether you want IPv4 only or dualstack (IPv4 and IPv6) to support your labeling workforce.
workforce_vpc_config : workforce_vpc_config_request option;Use this parameter to configure a workforce using VPC.
*)workforce_name : workforce_name;The name of the private workforce.
*)source_ip_config : source_ip_config option;oidc_config : oidc_config option;Use this parameter to configure a private workforce using your own OIDC Identity Provider.
Do not use CognitoConfig if you specify values for OidcConfig.
cognito_config : cognito_config option;Use this parameter to configure an Amazon Cognito private workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.
Do not use OidcConfig if you specify values for CognitoConfig.
}type nonrec create_user_profile_response = {user_profile_arn : user_profile_arn option;The user profile Amazon Resource Name (ARN).
*)}type nonrec create_user_profile_request = {user_settings : user_settings option;A collection of settings.
*)single_sign_on_user_value : string256 option;The username of the associated Amazon Web Services Single Sign-On User for this UserProfile. If the Domain's AuthMode is IAM Identity Center, this field is required, and must match a valid username of a user in your directory. If the Domain's AuthMode is not IAM Identity Center, this field cannot be specified.
*)single_sign_on_user_identifier : single_sign_on_user_identifier option;A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is "UserName". If the Domain's AuthMode is IAM Identity Center, this field is required. If the Domain's AuthMode is not IAM Identity Center, this field cannot be specified.
*)user_profile_name : user_profile_name;A name for the UserProfile. This value is not case sensitive.
*)domain_id : domain_id;The ID of the associated Domain.
*)}type nonrec create_trial_component_response = {trial_component_arn : trial_component_arn option;The Amazon Resource Name (ARN) of the trial component.
*)}type nonrec create_trial_component_request = {metadata_properties : metadata_properties option;output_artifacts : trial_component_artifacts option;The output artifacts for the component. Examples of output artifacts are metrics, snapshots, logs, and images.
*)input_artifacts : trial_component_artifacts option;The input artifacts for the component. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types.
*)parameters : trial_component_parameters option;The hyperparameters for the component.
*)end_time : timestamp option;When the component ended.
*)start_time : timestamp option;When the component started.
*)status : trial_component_status option;The status of the component. States include:
display_name : experiment_entity_name option;The name of the component as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialComponentName is displayed.
trial_component_name : experiment_entity_name;The name of the component. The name must be unique in your Amazon Web Services account and is not case-sensitive.
*)}type nonrec create_trial_response = {trial_arn : trial_arn option;The Amazon Resource Name (ARN) of the trial.
*)}type nonrec create_trial_request = {metadata_properties : metadata_properties option;experiment_name : experiment_entity_name;The name of the experiment to associate the trial with.
*)display_name : experiment_entity_name option;The name of the trial as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialName is displayed.
trial_name : experiment_entity_name;The name of the trial. The name must be unique in your Amazon Web Services account and is not case-sensitive.
*)}type nonrec create_transform_job_response = {transform_job_arn : transform_job_arn;The Amazon Resource Name (ARN) of the transform job.
*)}type nonrec create_transform_job_request = {experiment_config : experiment_config option;data_processing : data_processing option;The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.
*)transform_resources : transform_resources;Describes the resources, including ML instance types and ML instance count, to use for the transform job.
*)data_capture_config : batch_data_capture_config option;Configuration to control how SageMaker captures inference data.
*)transform_output : transform_output;Describes the results of the transform job.
*)transform_input : transform_input;Describes the input source and the way the transform job consumes it.
*)environment : transform_environment_map option;The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables. We support up to 16 key and values entries in the map.
*)batch_strategy : batch_strategy option;Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
To enable the batch strategy, you must set the SplitType property to Line, RecordIO, or TFRecord. To use only one record when making an HTTP invocation request to a container, set BatchStrategy to SingleRecord and SplitType to Line. To fit as many records in a mini-batch as can fit within the MaxPayloadInMB limit, set BatchStrategy to MultiRecord and SplitType to Line.
max_payload_in_m_b : max_payload_in_m_b option;The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB.
The value of MaxPayloadInMB cannot be greater than 100 MB. If you specify the MaxConcurrentTransforms parameter, the value of (MaxConcurrentTransforms * MaxPayloadInMB) also cannot exceed 100 MB.
For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.
model_client_config : model_client_config option;Configures the timeout and maximum number of retries for processing a transform job invocation.
*)max_concurrent_transforms : max_concurrent_transforms option;The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, Amazon SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms.
model_name : model_name;The name of the model that you want to use for the transform job. ModelName must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.
transform_job_name : transform_job_name;The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
*)}type nonrec create_training_plan_response = {training_plan_arn : training_plan_arn;The Amazon Resource Name (ARN); of the created training plan.
*)}type nonrec create_training_plan_request = {spare_instance_count_per_ultra_server : spare_instance_count_per_ultra_server
option;Number of spare instances to reserve per UltraServer for enhanced resiliency. Default is 1.
*)training_plan_offering_id : training_plan_offering_id;The unique identifier of the training plan offering to use for creating this plan.
*)training_plan_name : training_plan_name;The name of the training plan to create.
*)}type nonrec create_training_job_response = {training_job_arn : training_job_arn;The Amazon Resource Name (ARN) of the training job.
*)}type nonrec create_training_job_request = {model_package_config : model_package_config option;The configuration for the model package.
*)mlflow_config : mlflow_config option;The MLflow configuration using SageMaker managed MLflow.
*)serverless_job_config : serverless_job_config option;The configuration for serverless training jobs.
*)session_chaining_config : session_chaining_config option;Contains information about attribute-based access control (ABAC) for the training job.
*)infra_check_config : infra_check_config option;Contains information about the infrastructure health check configuration for the training job.
*)remote_debug_config : remote_debug_config option;Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
*)retry_strategy : retry_strategy option;The number of times to retry the job when the job fails due to an InternalServerError.
environment : training_environment_map option;The environment variables to set in the Docker container.
Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.
*)profiler_rule_configurations : profiler_rule_configurations option;Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
*)profiler_config : profiler_config option;experiment_config : experiment_config option;tensor_board_output_config : tensor_board_output_config option;debug_rule_configurations : debug_rule_configurations option;Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
*)debug_hook_config : debug_hook_config option;checkpoint_config : checkpoint_config option;Contains information about the output location for managed spot training checkpoint data.
*)enable_managed_spot_training : boolean_ option;To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.
The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
*)enable_inter_container_traffic_encryption : boolean_ option;To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.
enable_network_isolation : boolean_ option;Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
*)stopping_condition : stopping_condition option;Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
vpc_config : vpc_config option;A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
*)resource_config : resource_config option;The resources, including the ML compute instances and ML storage volumes, to use for model training.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
output_data_config : output_data_config;Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
*)input_data_config : input_data_config option;An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.
Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.
Depending on the input mode that the algorithm supports, SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files are available as input streams. They do not need to be downloaded.
Your input must be in the same Amazon Web Services region as your training job.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.
During model training, SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see SageMaker Roles.
To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission.
algorithm_specification : algorithm_specification option;The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
*)hyper_parameters : hyper_parameters option;Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.
You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.
Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request hyperparameter variable or plain text fields.
*)training_job_name : training_job_name;The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
*)}type nonrec create_studio_lifecycle_config_response = {studio_lifecycle_config_arn : studio_lifecycle_config_arn option;The ARN of your created Lifecycle Configuration.
*)}type nonrec create_studio_lifecycle_config_request = {studio_lifecycle_config_app_type : studio_lifecycle_config_app_type;The App type that the Lifecycle Configuration is attached to.
*)studio_lifecycle_config_content : studio_lifecycle_config_content;The content of your Amazon SageMaker AI Studio Lifecycle Configuration script. This content must be base64 encoded.
*)studio_lifecycle_config_name : studio_lifecycle_config_name;The name of the Amazon SageMaker AI Studio Lifecycle Configuration to create.
*)}type nonrec create_space_response = {space_arn : space_arn option;The space's Amazon Resource Name (ARN).
*)}type nonrec create_space_request = {space_display_name : non_empty_string64 option;The name of the space that appears in the SageMaker Studio UI.
*)space_sharing_settings : space_sharing_settings option;A collection of space sharing settings.
*)ownership_settings : ownership_settings option;A collection of ownership settings.
*)space_settings : space_settings option;A collection of space settings.
*)space_name : space_name;The name of the space.
*)domain_id : domain_id;The ID of the associated domain.
*)}type nonrec create_project_output = {project_id : project_id;The ID of the new project.
*)project_arn : project_arn;The Amazon Resource Name (ARN) of the project.
*)}type nonrec cfn_stack_create_parameter = {value : cfn_stack_parameter_value option;The value of the CloudFormation parameter.
*)key : cfn_stack_parameter_key;The name of the CloudFormation parameter.
*)}A key-value pair that represents a parameter for the CloudFormation stack.
type nonrec cfn_stack_create_parameters = cfn_stack_create_parameter listtype nonrec cfn_create_template_provider = {parameters : cfn_stack_create_parameters option;An array of CloudFormation stack parameters.
*)role_ar_n : role_arn option;The IAM role that CloudFormation assumes when creating the stack.
*)template_ur_l : cfn_template_ur_l;The Amazon S3 URL of the CloudFormation template.
*)template_name : cfn_template_name;A unique identifier for the template within the project.
*)}The CloudFormation template provider configuration for creating infrastructure resources.
type nonrec create_template_provider = {cfn_template_provider : cfn_create_template_provider option;The CloudFormation template provider configuration for creating infrastructure resources.
*)}Contains configuration details for a template provider. Only one type of template provider can be specified.
type nonrec create_template_provider_list = create_template_provider listtype nonrec create_project_input = {template_providers : create_template_provider_list option;An array of template provider configurations for creating infrastructure resources for the project.
*)service_catalog_provisioning_details : service_catalog_provisioning_details
option;The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don't provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.
*)project_description : entity_description option;A description for the project.
*)project_name : project_entity_name;The name of the project.
*)}type nonrec create_processing_job_response = {processing_job_arn : processing_job_arn;The Amazon Resource Name (ARN) of the processing job.
*)}type nonrec create_processing_job_request = {experiment_config : experiment_config option;role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
*)network_config : network_config option;Networking options for a processing job, such as whether to allow inbound and outbound network calls to and from processing containers, and the VPC subnets and security groups to use for VPC-enabled processing jobs.
*)environment : processing_environment_map option;The environment variables to set in the Docker container. Up to 100 key and values entries in the map are supported.
Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.
*)app_specification : app_specification;Configures the processing job to run a specified Docker container image.
*)stopping_condition : processing_stopping_condition option;The time limit for how long the processing job is allowed to run.
*)processing_resources : processing_resources;Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
*)processing_job_name : processing_job_name;The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
*)processing_output_config : processing_output_config option;Output configuration for the processing job.
*)processing_inputs : processing_inputs option;An array of inputs configuring the data to download into the processing container.
*)}type nonrec create_presigned_notebook_instance_url_input = {session_expiration_duration_in_seconds : session_expiration_duration_in_seconds
option;The duration of the session, in seconds. The default is 12 hours.
*)notebook_instance_name : notebook_instance_name;The name of the notebook instance.
*)}type nonrec create_presigned_mlflow_tracking_server_url_request = {session_expiration_duration_in_seconds : session_expiration_duration_in_seconds
option;The duration in seconds that your MLflow UI session is valid.
*)expires_in_seconds : expires_in_seconds option;The duration in seconds that your presigned URL is valid. The presigned URL can be used only once.
*)tracking_server_name : tracking_server_name;The name of the tracking server to connect to your MLflow UI.
*)}type nonrec create_presigned_mlflow_app_url_request = {session_expiration_duration_in_seconds : session_expiration_duration_in_seconds
option;The duration in seconds that your presigned URL is valid. The presigned URL can be used only once.
*)expires_in_seconds : expires_in_seconds option;The duration in seconds that your presigned URL is valid. The presigned URL can be used only once.
*)arn : mlflow_app_arn;The ARN of the MLflow App to connect to your MLflow UI.
*)}type nonrec create_presigned_domain_url_request = {landing_uri : landing_uri option;The landing page that the user is directed to when accessing the presigned URL. Using this value, users can access Studio or Studio Classic, even if it is not the default experience for the domain. The supported values are:
studio::relative/path: Directs users to the relative path in Studio.app:JupyterServer:relative/path: Directs users to the relative path in the Studio Classic application.app:JupyterLab:relative/path: Directs users to the relative path in the JupyterLab application.app:RStudioServerPro:relative/path: Directs users to the relative path in the RStudio application.app:CodeEditor:relative/path: Directs users to the relative path in the Code Editor, based on Code-OSS, Visual Studio Code - Open Source application.app:Canvas:relative/path: Directs users to the relative path in the Canvas application.space_name : space_name option;The name of the space.
*)expires_in_seconds : expires_in_seconds option;The number of seconds until the pre-signed URL expires. This value defaults to 300.
*)session_expiration_duration_in_seconds : session_expiration_duration_in_seconds
option;The session expiration duration in seconds. This value defaults to 43200.
*)user_profile_name : user_profile_name;The name of the UserProfile to sign-in as.
*)domain_id : domain_id;The domain ID.
*)}type nonrec create_pipeline_response = {pipeline_arn : pipeline_arn option;The Amazon Resource Name (ARN) of the created pipeline.
*)}type nonrec create_pipeline_request = {parallelism_configuration : parallelism_configuration option;This is the configuration that controls the parallelism of the pipeline. If specified, it applies to all runs of this pipeline by default.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.
*)client_request_token : idempotency_token;A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
*)pipeline_description : pipeline_description option;A description of the pipeline.
*)pipeline_definition_s3_location : pipeline_definition_s3_location option;The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.
*)pipeline_definition : pipeline_definition option;pipeline_display_name : pipeline_name option;The display name of the pipeline.
*)pipeline_name : pipeline_name;The name of the pipeline.
*)}type nonrec create_partner_app_presigned_url_response = {url : string2048 option;The presigned URL that you can use to access the SageMaker Partner AI App.
*)}type nonrec create_partner_app_presigned_url_request = {session_expiration_duration_in_seconds : session_expiration_duration_in_seconds
option;Indicates how long the Amazon SageMaker Partner AI App session can be accessed for after logging in.
*)expires_in_seconds : expires_in_seconds option;The time that will pass before the presigned URL expires.
*)arn : partner_app_arn;The ARN of the SageMaker Partner AI App to create the presigned URL for.
*)}type nonrec create_partner_app_response = {arn : partner_app_arn option;The ARN of the SageMaker Partner AI App.
*)}type nonrec create_partner_app_request = {client_token : client_token option;A unique token that guarantees that the call to this API is idempotent.
*)enable_auto_minor_version_upgrade : boolean_ option;When set to TRUE, the SageMaker Partner AI App is automatically upgraded to the latest minor version during the next scheduled maintenance window, if one is available. Default is FALSE.
enable_iam_session_based_identity : boolean_ option;When set to TRUE, the SageMaker Partner AI App sets the Amazon Web Services IAM session name or the authenticated IAM user as the identity of the SageMaker Partner AI App user.
auth_type : partner_app_auth_type;The authorization type that users use to access the SageMaker Partner AI App.
*)application_config : partner_app_config option;Configuration settings for the SageMaker Partner AI App.
*)tier : non_empty_string64;Indicates the instance type and size of the cluster attached to the SageMaker Partner AI App.
*)maintenance_config : partner_app_maintenance_config option;Maintenance configuration settings for the SageMaker Partner AI App.
*)kms_key_id : kms_key_id option;SageMaker Partner AI Apps uses Amazon Web Services KMS to encrypt data at rest using an Amazon Web Services managed key by default. For more control, specify a customer managed key.
*)execution_role_arn : role_arn;The ARN of the IAM role that the partner application uses.
*)type_ : partner_app_type;The type of SageMaker Partner AI App to create. Must be one of the following: lakera-guard, comet, deepchecks-llm-evaluation, or fiddler.
name : partner_app_name;The name to give the SageMaker Partner AI App.
*)}type nonrec create_optimization_job_response = {optimization_job_arn : optimization_job_arn;The Amazon Resource Name (ARN) of the optimization job.
*)}type nonrec create_optimization_job_request = {vpc_config : optimization_vpc_config option;A VPC in Amazon VPC that your optimized model has access to.
*)stopping_condition : stopping_condition;output_config : optimization_job_output_config;Details for where to store the optimized model that you create with the optimization job.
*)optimization_configs : optimization_configs;Settings for each of the optimization techniques that the job applies.
*)optimization_environment : optimization_job_environment_variables option;The environment variables to set in the model container.
*)max_instance_count : optimization_job_max_instance_count option;The maximum number of instances to use for the optimization job.
*)deployment_instance_type : optimization_job_deployment_instance_type;The type of instance that hosts the optimized model that you create with the optimization job.
*)model_source : optimization_job_model_source;The location of the source model to optimize with an optimization job.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.
During model optimization, Amazon SageMaker AI needs your permission to:
You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.
optimization_job_name : entity_name;A custom name for the new optimization job.
*)}type nonrec create_notebook_instance_lifecycle_config_output = {notebook_instance_lifecycle_config_arn : notebook_instance_lifecycle_config_arn
option;The Amazon Resource Name (ARN) of the lifecycle configuration.
*)}type nonrec create_notebook_instance_lifecycle_config_input = {on_start : notebook_instance_lifecycle_config_list option;A shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
*)on_create : notebook_instance_lifecycle_config_list option;A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
*)notebook_instance_lifecycle_config_name : notebook_instance_lifecycle_config_name;The name of the lifecycle configuration.
*)}type nonrec create_notebook_instance_output = {notebook_instance_arn : notebook_instance_arn option;The Amazon Resource Name (ARN) of the notebook instance.
*)}type nonrec create_notebook_instance_input = {instance_metadata_service_configuration : instance_metadata_service_configuration
option;Information on the IMDS configuration of the notebook instance
*)platform_identifier : platform_identifier option;The platform identifier of the notebook instance runtime environment. The default value is notebook-al2023-v1.
root_access : root_access option;Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.
Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.
*)additional_code_repositories : additional_code_repository_names_or_urls option;An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.
*)default_code_repository : code_repository_name_or_url option;A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.
*)accelerator_types : notebook_instance_accelerator_types option;This parameter is no longer supported. Elastic Inference (EI) is no longer available.
This parameter was used to specify a list of EI instance types to associate with this notebook instance.
*)volume_size_in_g_b : notebook_instance_volume_size_in_g_b option;The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.
*)direct_internet_access : direct_internet_access option;Sets whether SageMaker AI provides internet access to the notebook instance. If you set this to Disabled this notebook instance is able to access resources only in your VPC, and is not be able to connect to SageMaker AI training and endpoint services unless you configure a NAT Gateway in your VPC.
For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.
lifecycle_config_name : notebook_instance_lifecycle_config_name option;The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
*)kms_key_id : kms_key_id option;The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker AI uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the Amazon Web Services Key Management Service Developer Guide.
*)role_arn : role_arn;When you send any requests to Amazon Web Services resources from the notebook instance, SageMaker AI assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so SageMaker AI can perform these tasks. The policy must allow the SageMaker AI service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see SageMaker AI Roles.
To be able to pass this role to SageMaker AI, the caller of this API must have the iam:PassRole permission.
ip_address_type : ip_address_type option;The IP address type for the notebook instance. Specify ipv4 for IPv4-only connectivity or dualstack for both IPv4 and IPv6 connectivity. When you specify dualstack, the subnet must support IPv6 CIDR blocks. If not specified, defaults to ipv4.
security_group_ids : security_group_ids option;The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
*)subnet_id : subnet_id option;The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.
*)instance_type : instance_type;The type of ML compute instance to launch for the notebook instance.
*)notebook_instance_name : notebook_instance_name;The name of the new notebook instance.
*)}type nonrec create_monitoring_schedule_response = {monitoring_schedule_arn : monitoring_schedule_arn;The Amazon Resource Name (ARN) of the monitoring schedule.
*)}type nonrec create_monitoring_schedule_request = {monitoring_schedule_config : monitoring_schedule_config;The configuration object that specifies the monitoring schedule and defines the monitoring job.
*)monitoring_schedule_name : monitoring_schedule_name;The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
*)}type nonrec create_model_quality_job_definition_response = {job_definition_arn : monitoring_job_definition_arn;The Amazon Resource Name (ARN) of the model quality monitoring job.
*)}type nonrec create_model_quality_job_definition_request = {stopping_condition : monitoring_stopping_condition option;role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.
*)network_config : monitoring_network_config option;Specifies the network configuration for the monitoring job.
*)job_resources : monitoring_resources;model_quality_job_output_config : monitoring_output_config;model_quality_job_input : model_quality_job_input;A list of the inputs that are monitored. Currently endpoints are supported.
*)model_quality_app_specification : model_quality_app_specification;The container that runs the monitoring job.
*)model_quality_baseline_config : model_quality_baseline_config option;Specifies the constraints and baselines for the monitoring job.
*)job_definition_name : monitoring_job_definition_name;The name of the monitoring job definition.
*)}type nonrec create_model_package_group_output = {model_package_group_arn : model_package_group_arn;The Amazon Resource Name (ARN) of the model group.
*)}type nonrec create_model_package_group_input = {managed_configuration : managed_configuration option;The managed configuration of the model package group.
*)model_package_group_description : entity_description option;A description for the model group.
*)model_package_group_name : entity_name;The name of the model group.
*)}type nonrec create_model_package_output = {model_package_arn : model_package_arn;The Amazon Resource Name (ARN) of the new model package.
*)}type nonrec create_model_package_input = {managed_storage_type : managed_storage_type option;The storage type of the model package.
*)model_life_cycle : model_life_cycle option;A structure describing the current state of the model in its life cycle.
*)model_card : model_package_model_card option;The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.
security_config : model_package_security_config option;The KMS Key ID (KMSKeyId) used for encryption of model package information.
source_uri : model_package_source_uri option;The URI of the source for the model package. If you want to clone a model package, set it to the model package Amazon Resource Name (ARN). If you want to register a model, set it to the model ARN.
*)skip_model_validation : skip_model_validation option;Indicates if you want to skip model validation.
*)additional_inference_specifications : additional_inference_specifications
option;An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
*)drift_check_baselines : drift_check_baselines option;Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.
*)customer_metadata_properties : customer_metadata_map option;The metadata properties associated with the model package versions.
*)sample_payload_url : s3_uri option;The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.
*)task : string_ option;The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: "IMAGE_CLASSIFICATION" | "OBJECT_DETECTION" | "TEXT_GENERATION" |"IMAGE_SEGMENTATION" | "FILL_MASK" | "CLASSIFICATION" | "REGRESSION" | "OTHER".
Specify "OTHER" if none of the tasks listed fit your use case.
*)domain : string_ option;The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
*)client_token : client_token option;A unique token that guarantees that the call to this API is idempotent.
*)model_metrics : model_metrics option;A structure that contains model metrics reports.
*)metadata_properties : metadata_properties option;model_approval_status : model_approval_status option;Whether the model is approved for deployment.
This parameter is optional for versioned models, and does not apply to unversioned models.
For versioned models, the value of this parameter must be set to Approved to deploy the model.
certify_for_marketplace : certify_for_marketplace option;Whether to certify the model package for listing on Amazon Web Services Marketplace.
This parameter is optional for unversioned models, and does not apply to versioned models.
*)source_algorithm_specification : source_algorithm_specification option;Details about the algorithm that was used to create the model package.
*)validation_specification : model_package_validation_specification option;Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.
*)inference_specification : inference_specification option;Specifies details about inference jobs that you can run with models based on this model package, including the following information:
model_package_registration_type : model_package_registration_type option;The package registration type of the model package input.
*)model_package_description : entity_description option;A description of the model package.
*)model_package_group_name : arn_or_name option;The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.
This parameter is required for versioned models, and does not apply to unversioned models.
*)model_package_name : entity_name option;The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
This parameter is required for unversioned models. It is not applicable to versioned models.
*)}type nonrec create_model_explainability_job_definition_response = {job_definition_arn : monitoring_job_definition_arn;The Amazon Resource Name (ARN) of the model explainability job.
*)}type nonrec create_model_explainability_job_definition_request = {stopping_condition : monitoring_stopping_condition option;role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.
*)network_config : monitoring_network_config option;Networking options for a model explainability job.
*)job_resources : monitoring_resources;model_explainability_job_output_config : monitoring_output_config;model_explainability_job_input : model_explainability_job_input;Inputs for the model explainability job.
*)model_explainability_app_specification : model_explainability_app_specification;Configures the model explainability job to run a specified Docker container image.
*)model_explainability_baseline_config : model_explainability_baseline_config
option;The baseline configuration for a model explainability job.
*)job_definition_name : monitoring_job_definition_name;The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
*)}type nonrec create_model_card_export_job_response = {model_card_export_job_arn : model_card_export_job_arn;The Amazon Resource Name (ARN) of the model card export job.
*)}type nonrec create_model_card_export_job_request = {output_config : model_card_export_output_config;The model card output configuration that specifies the Amazon S3 path for exporting.
*)model_card_export_job_name : entity_name;The name of the model card export job.
*)model_card_version : integer option;The version of the model card to export. If a version is not provided, then the latest version of the model card is exported.
*)model_card_name : model_card_name_or_arn;The name or Amazon Resource Name (ARN) of the model card to export.
*)}type nonrec create_model_card_response = {model_card_arn : model_card_arn;The Amazon Resource Name (ARN) of the successfully created model card.
*)}type nonrec create_model_card_request = {model_card_status : model_card_status;The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
Draft: The model card is a work in progress.PendingReview: The model card is pending review.Approved: The model card is approved.Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.content : model_card_content;The content of the model card. Content must be in model card JSON schema and provided as a string.
*)security_config : model_card_security_config option;An optional Key Management Service key to encrypt, decrypt, and re-encrypt model card content for regulated workloads with highly sensitive data.
*)model_card_name : entity_name;The unique name of the model card.
*)}type nonrec create_model_bias_job_definition_response = {job_definition_arn : monitoring_job_definition_arn;The Amazon Resource Name (ARN) of the model bias job.
*)}type nonrec create_model_bias_job_definition_request = {stopping_condition : monitoring_stopping_condition option;role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.
*)network_config : monitoring_network_config option;Networking options for a model bias job.
*)job_resources : monitoring_resources;model_bias_job_output_config : monitoring_output_config;model_bias_job_input : model_bias_job_input;Inputs for the model bias job.
*)model_bias_app_specification : model_bias_app_specification;Configures the model bias job to run a specified Docker container image.
*)model_bias_baseline_config : model_bias_baseline_config option;The baseline configuration for a model bias job.
*)job_definition_name : monitoring_job_definition_name;The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
*)}type nonrec create_model_output = {model_arn : model_arn;The ARN of the model created in SageMaker.
*)}type nonrec create_model_input = {enable_network_isolation : boolean_ option;Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
*)vpc_config : vpc_config option;A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.
execution_role_arn : role_arn option;The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see SageMaker Roles.
To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission.
inference_execution_config : inference_execution_config option;Specifies details of how containers in a multi-container endpoint are called.
*)containers : container_definition_list option;Specifies the containers in the inference pipeline.
*)primary_container : container_definition option;The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.
*)model_name : model_name;The name of the new model.
*)}type nonrec create_mlflow_tracking_server_response = {tracking_server_arn : tracking_server_arn option;The ARN of the tracking server.
*)}type nonrec create_mlflow_tracking_server_request = {s3_bucket_owner_verification : boolean_ option;Enable Amazon S3 Ownership checks when interacting with Amazon S3 buckets from a SageMaker Managed MLflow Tracking Server. Defaults to True if not provided.
s3_bucket_owner_account_id : account_id option;Expected Amazon Web Services account ID that owns the Amazon S3 bucket for artifact storage. Defaults to caller's account ID if not provided.
*)weekly_maintenance_window_start : weekly_maintenance_window_start option;The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.
*)automatic_model_registration : boolean_ option;Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to True. To disable automatic model registration, set this value to False. If not specified, AutomaticModelRegistration defaults to False.
role_arn : role_arn;The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the artifact store in Amazon S3. The role should have AmazonS3FullAccess permissions. For more information on IAM permissions for tracking server creation, see Set up IAM permissions for MLflow.
mlflow_version : mlflow_version option;The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see How it works.
*)tracking_server_size : tracking_server_size option;The size of the tracking server you want to create. You can choose between "Small", "Medium", and "Large". The default MLflow Tracking Server configuration size is "Small". You can choose a size depending on the projected use of the tracking server such as the volume of data logged, number of users, and frequency of use.
We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.
*)artifact_store_uri : s3_uri;The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.
*)tracking_server_name : tracking_server_name;A unique string identifying the tracking server name. This string is part of the tracking server ARN.
*)}type nonrec create_mlflow_app_response = {arn : mlflow_app_arn option;The ARN of the MLflow App.
*)}type nonrec create_mlflow_app_request = {default_domain_id_list : default_domain_id_list option;List of SageMaker domain IDs for which this MLflow App is used as the default.
*)account_default_status : account_default_status option;Indicates whether this MLflow app is the default for the entire account.
*)weekly_maintenance_window_start : weekly_maintenance_window_start option;The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.
*)model_registration_mode : model_registration_mode option;Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to AutoModelRegistrationEnabled. To disable automatic model registration, set this value to AutoModelRegistrationDisabled. If not specified, AutomaticModelRegistration defaults to AutoModelRegistrationDisabled.
role_arn : role_arn;The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow App uses to access the artifact store in Amazon S3. The role should have the AmazonS3FullAccess permission.
artifact_store_uri : s3_uri;The S3 URI for a general purpose bucket to use as the MLflow App artifact store.
*)name : mlflow_app_name;A string identifying the MLflow app name. This string is not part of the tracking server ARN.
*)}type nonrec create_labeling_job_response = {labeling_job_arn : labeling_job_arn;The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the labeling job.
*)}type nonrec create_labeling_job_request = {human_task_config : human_task_config;Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
*)labeling_job_algorithms_config : labeling_job_algorithms_config option;Configures the information required to perform automated data labeling.
*)stopping_conditions : labeling_job_stopping_conditions option;A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.
*)label_category_config_s3_uri : s3_uri option;The S3 URI of the file, referred to as a label category configuration file, that defines the categories used to label the data objects.
For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.
For named entity recognition jobs, in addition to "labels", you must provide worker instructions in the label category configuration file using the "instructions" parameter: "instructions": {"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}. For details and an example, see Create a Named Entity Recognition Labeling Job (API).
For all other built-in task types and custom tasks, your label category configuration file must be a JSON file in the following format. Identify the labels you want to use by replacing label_1, label_2,...,label_n with your label categories.
{
"document-version": "2018-11-28",
"labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]
}
Note the following about the label category configuration file:
auditLabelAttributeName in the label category configuration. Use this parameter to enter the LabelAttributeName of the labeling job you want to adjust or verify annotations of.role_arn : role_arn;The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.
*)output_config : labeling_job_output_config;The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
*)input_config : labeling_job_input_config;Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
You must specify at least one of the following: S3DataSource or SnsDataSource.
SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all data objects in the input manifest file have been labeled.S3DataSource to specify an input manifest file for both streaming and one-time labeling jobs. Adding an S3DataSource is optional if you use SnsDataSource to create a streaming labeling job.If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information, personal information or protected health information. Use ContentClassifiers to specify that your data is free of personally identifiable information and adult content.
label_attribute_name : label_attribute_name;The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The LabelAttributeName must meet the following requirements.
If you are using one of the built-in task types or one of the following, the attribute name must end with "-ref".
SemanticSegmentation) and adjustment (AdjustmentSemanticSegmentation) labeling jobs for this task type. One exception is that verification (VerificationSemanticSegmentation) must not end with -"ref".VideoObjectDetection), and adjustment and verification (AdjustmentVideoObjectDetection) labeling jobs for this task type.VideoObjectTracking), and adjustment and verification (AdjustmentVideoObjectTracking) labeling jobs for this task type.3DPointCloudSemanticSegmentation), and adjustment and verification (Adjustment3DPointCloudSemanticSegmentation) labeling jobs for this task type.3DPointCloudObjectTracking), and adjustment and verification (Adjustment3DPointCloudObjectTracking) labeling jobs for this task type.If you are creating an adjustment or verification labeling job, you must use a different LabelAttributeName than the one used in the original labeling job. The original labeling job is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about adjustment and verification labeling jobs, see Verify and Adjust Labels.
labeling_job_name : labeling_job_name;The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an Amazon Web Services account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
}type nonrec create_job_response = {job_arn : job_arn;The Amazon Resource Name (ARN) of the job.
*)}type nonrec create_job_request = {job_config_document : job_config_document;The JSON configuration document for the job. The document must conform to the schema specified by JobConfigSchemaVersion. Use DescribeJobSchemaVersion to retrieve the schema for validation.
job_config_schema_version : job_schema_version;The version of the configuration schema to use for the job configuration document. Use ListJobSchemaVersions to get available schema versions for a job category.
job_category : job_category;The category of the job. The category determines the type of workload that the job runs.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the job. The role must have the necessary permissions to access the resources required by the job configuration.
*)job_name : job_name;The name of the job. The name must be unique within your account and Amazon Web Services Region.
*)}type nonrec create_inference_recommendations_job_response = {job_arn : recommendation_job_arn;The Amazon Resource Name (ARN) of the recommendation job.
*)}type nonrec recommendation_job_compiled_output_config = {s3_output_uri : s3_uri option;Identifies the Amazon S3 bucket where you want SageMaker to store the compiled model artifacts.
*)}Provides information about the output configuration for the compiled model.
type nonrec recommendation_job_output_config = {compiled_output_config : recommendation_job_compiled_output_config option;Provides information about the output configuration for the compiled model.
*)kms_key_id : kms_key_id option;The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt your output artifacts with Amazon S3 server-side encryption. The SageMaker execution role must have kms:GenerateDataKey permission.
The KmsKeyId can be any of the following formats:
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"
// KMS Key Alias
"alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"
For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
*)}Provides information about the output configuration for the compiled model.
type nonrec create_inference_recommendations_job_request = {output_config : recommendation_job_output_config option;Provides information about the output artifacts and the KMS key to use for Amazon S3 server-side encryption.
*)stopping_conditions : recommendation_job_stopping_conditions option;A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped.
*)job_description : recommendation_job_description option;Description of the recommendation job.
*)input_config : recommendation_job_input_config;Provides information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
*)job_type : recommendation_job_type;Defines the type of recommendation job. Specify Default to initiate an instance recommendation and Advanced to initiate a load test. If left unspecified, Amazon SageMaker Inference Recommender will run an instance recommendation (DEFAULT) job.
job_name : recommendation_job_name;A name for the recommendation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account. The job name is passed down to the resources created by the recommendation job. The names of resources (such as the model, endpoint configuration, endpoint, and compilation) that are prefixed with the job name are truncated at 40 characters.
*)}type nonrec create_inference_experiment_response = {inference_experiment_arn : inference_experiment_arn;The ARN for your inference experiment.
*)}type nonrec create_inference_experiment_request = {kms_key : kms_key_id option;The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The KmsKey can be any of the following formats:
KMS key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KMS key Alias
"alias/ExampleAlias"
Amazon Resource Name (ARN) of a KMS key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
shadow_mode_config : shadow_mode_config;The configuration of ShadowMode inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.
data_storage_config : inference_experiment_data_storage_config option;The Amazon S3 location and configuration for storing inference request and response data.
This is an optional parameter that you can use for data capture. For more information, see Capture data.
*)model_variants : model_variant_config_list;An array of ModelVariantConfig objects. There is one for each variant in the inference experiment. Each ModelVariantConfig object in the array describes the infrastructure configuration for the corresponding variant.
endpoint_name : endpoint_name;The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.
*)role_arn : role_arn;The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
*)description : inference_experiment_description option;A description for the inference experiment.
*)schedule : inference_experiment_schedule option;The duration for which you want the inference experiment to run. If you don't specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days.
*)type_ : inference_experiment_type;The type of the inference experiment that you want to run. The following types of experiments are possible:
ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.name : inference_experiment_name;The name for the inference experiment.
*)}type nonrec create_inference_component_output = {inference_component_arn : inference_component_arn;The Amazon Resource Name (ARN) of the inference component.
*)}type nonrec create_inference_component_input = {runtime_config : inference_component_runtime_config option;Runtime settings for a model that is deployed with an inference component.
*)specifications : inference_component_specification_list option;A list of specification objects for the inference component, one per instance type. Use this parameter when you want to deploy a different model or resource configuration for the inference component on each instance type. You can use either this parameter or the singular Specification parameter, but not both.
specification : inference_component_specification option;Details about the resources to deploy with this inference component, including the model, container, and compute resources.
*)variant_name : variant_name option;The name of an existing production variant where you host the inference component.
*)endpoint_name : endpoint_name;The name of an existing endpoint where you host the inference component.
*)inference_component_name : inference_component_name;A unique name to assign to the inference component.
*)}type nonrec create_image_version_response = {image_version_arn : image_version_arn option;The ARN of the image version.
*)}type nonrec create_image_version_request = {release_notes : release_notes option;The maintainer description of the image version.
*)horovod : horovod option;Indicates Horovod compatibility.
*)processor : processor option;Indicates CPU or GPU compatibility.
CPU: The image version is compatible with CPU.GPU: The image version is compatible with GPU.programming_lang : programming_lang option;The supported programming language and its version.
*)ml_framework : ml_framework option;The machine learning framework vended in the image version.
*)job_type : job_type option;Indicates SageMaker AI job type compatibility.
TRAINING: The image version is compatible with SageMaker AI training jobs.INFERENCE: The image version is compatible with SageMaker AI inference jobs.NOTEBOOK_KERNEL: The image version is compatible with SageMaker AI notebook kernels.vendor_guidance : vendor_guidance option;The stability of the image version, specified by the maintainer.
NOT_PROVIDED: The maintainers did not provide a status for image version stability.STABLE: The image version is stable.TO_BE_ARCHIVED: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months.ARCHIVED: The image version is archived. Archived image versions are not searchable and are no longer actively supported.aliases : sage_maker_image_version_aliases option;A list of aliases created with the image version.
*)image_name : image_name;The ImageName of the Image to create a version of.
client_token : client_token;A unique ID. If not specified, the Amazon Web Services CLI and Amazon Web Services SDKs, such as the SDK for Python (Boto3), add a unique value to the call.
*)base_image : image_base_image;The registry path of the container image to use as the starting point for this version. The path is an Amazon ECR URI in the following format:
<acct-id>.dkr.ecr.<region>.amazonaws.com/<repo-name[:tag] or [@digest]>
}type nonrec create_image_request = {role_arn : role_arn;The ARN of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.
*)image_name : image_name;The name of the image. Must be unique to your account.
*)display_name : image_display_name option;The display name of the image. If not provided, ImageName is displayed.
description : image_description option;The description of the image.
*)}type nonrec create_hyper_parameter_tuning_job_response = {hyper_parameter_tuning_job_arn : hyper_parameter_tuning_job_arn;The Amazon Resource Name (ARN) of the tuning job. SageMaker assigns an ARN to a hyperparameter tuning job when you create it.
*)}type nonrec create_hyper_parameter_tuning_job_request = {autotune : autotune option;Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:
warm_start_config : hyper_parameter_tuning_job_warm_start_config option;Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.
All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.
*)training_job_definitions : hyper_parameter_training_job_definitions option;training_job_definition : hyper_parameter_training_job_definition option;The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
*)hyper_parameter_tuning_job_config : hyper_parameter_tuning_job_config;The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.
*)hyper_parameter_tuning_job_name : hyper_parameter_tuning_job_name;The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
*)}type nonrec create_human_task_ui_response = {human_task_ui_arn : human_task_ui_arn;The Amazon Resource Name (ARN) of the human review workflow user interface you create.
*)}type nonrec create_human_task_ui_request = {ui_template : ui_template;human_task_ui_name : human_task_ui_name;The name of the user interface you are creating.
*)}type nonrec create_hub_content_reference_response = {hub_content_arn : hub_content_arn;The ARN of the hub content.
*)hub_arn : hub_arn;The ARN of the hub that the hub content reference was added to.
*)}type nonrec create_hub_content_reference_request = {min_version : hub_content_version option;The minimum version of the hub content to reference.
*)hub_content_name : hub_content_name option;The name of the hub content to reference.
*)sage_maker_public_hub_content_arn : sage_maker_public_hub_content_arn;The ARN of the public hub content to reference.
*)hub_name : hub_name_or_arn;The name of the hub to add the hub content reference to.
*)}Contains a presigned URL and its associated local file path for downloading hub content artifacts.
type nonrec authorized_url_configs = authorized_url listtype nonrec create_hub_content_presigned_urls_response = {next_token : next_token option;A token for pagination. If present, indicates that more presigned URLs are available. Use this token in a subsequent request to retrieve additional URLs.
*)}type nonrec presigned_url_access_config = {expected_s3_url : s3_model_uri option;The expected S3 URL prefix for validation purposes. This parameter helps ensure consistency between the resolved S3 URIs and the deployment configuration, reducing potential compatibility issues.
*)accept_eula : boolean_ option;Indicates acceptance of the End User License Agreement (EULA) for gated models. Set to true to acknowledge acceptance of the license terms required for accessing gated content.
*)}Configuration for accessing hub content through presigned URLs, including license agreement acceptance and URL validation settings.
type nonrec create_hub_content_presigned_urls_request = {next_token : next_token option;A token for pagination. Use this token to retrieve the next set of presigned URLs when the response is truncated.
*)max_results : max_results option;The maximum number of presigned URLs to return in the response. Default value is 100. Large models may contain hundreds of files, requiring pagination to retrieve all URLs.
*)access_config : presigned_url_access_config option;Configuration settings for accessing the hub content, including end-user license agreement acceptance for gated models and expected S3 URL validation.
*)hub_content_version : hub_content_version option;The version of the hub content. If not specified, the latest version is used.
*)hub_content_name : hub_content_name;The name of the hub content for which to generate presigned URLs. This identifies the specific model or content within the hub.
*)hub_content_type : hub_content_type;The type of hub content to access. Valid values include Model, Notebook, and ModelReference.
hub_name : hub_name_or_arn;The name or Amazon Resource Name (ARN) of the hub that contains the content. For public content, use SageMakerPublicHub.
}type nonrec create_hub_response = {hub_arn : hub_arn;The Amazon Resource Name (ARN) of the hub.
*)}type nonrec create_hub_request = {s3_storage_config : hub_s3_storage_config option;The Amazon S3 storage configuration for the hub.
*)hub_search_keywords : hub_search_keyword_list option;The searchable keywords for the hub.
*)hub_display_name : hub_display_name option;The display name of the hub.
*)hub_description : hub_description;A description of the hub.
*)hub_name : hub_name;The name of the hub to create.
*)}type nonrec create_flow_definition_response = {flow_definition_arn : flow_definition_arn;The Amazon Resource Name (ARN) of the flow definition you create.
*)}type nonrec create_flow_definition_request = {role_arn : role_arn;The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example, arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298.
output_config : flow_definition_output_config;An object containing information about where the human review results will be uploaded.
*)human_loop_config : human_loop_config option;An object containing information about the tasks the human reviewers will perform.
*)human_loop_activation_config : human_loop_activation_config option;An object containing information about the events that trigger a human workflow.
*)human_loop_request_source : human_loop_request_source option;Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
*)flow_definition_name : flow_definition_name;The name of your flow definition.
*)}type nonrec create_feature_group_response = {feature_group_arn : feature_group_arn;The Amazon Resource Name (ARN) of the FeatureGroup. This is a unique identifier for the feature group.
}type nonrec create_feature_group_request = {description : description option;A free-form description of a FeatureGroup.
role_arn : role_arn option;The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.
throughput_config : throughput_config option;offline_store_config : offline_store_config option;Use this to configure an OfflineFeatureStore. This parameter allows you to specify:
OfflineStore.OfflineStore. If KMS encryption key is not specified, by default we encrypt all data at rest using Amazon Web Services KMS key. By defining your bucket-level key for SSE, you can reduce Amazon Web Services KMS requests costs by up to 99 percent.To learn more about this parameter, see OfflineStoreConfig.
*)online_store_config : online_store_config option;You can turn the OnlineStore on or off by specifying True for the EnableOnlineStore flag in OnlineStoreConfig.
You can also include an Amazon Web Services KMS key ID (KMSKeyId) for at-rest encryption of the OnlineStore.
The default value is False.
feature_definitions : feature_definitions;A list of Feature names and types. Name and Type is compulsory per Feature.
Valid feature FeatureTypes are Integral, Fractional and String.
FeatureNames cannot be any of the following: is_deleted, write_time, api_invocation_time
You can create up to 2,500 FeatureDefinitions per FeatureGroup.
event_time_feature_name : feature_name;The name of the feature that stores the EventTime of a Record in a FeatureGroup.
An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup. All Records in the FeatureGroup must have a corresponding EventTime.
An EventTime can be a String or Fractional.
Fractional: EventTime feature values must be a Unix timestamp in seconds.String: EventTime feature values must be an ISO-8601 string in the format. The following formats are supported yyyy-MM-dd'T'HH:mm:ssZ and yyyy-MM-dd'T'HH:mm:ss.SSSZ where yyyy, MM, and dd represent the year, month, and day respectively and HH, mm, ss, and if applicable, SSS represent the hour, month, second and milliseconds respsectively. 'T' and Z are constants.record_identifier_feature_name : feature_name;The name of the Feature whose value uniquely identifies a Record defined in the FeatureStore. Only the latest record per identifier value will be stored in the OnlineStore. RecordIdentifierFeatureName must be one of feature definitions' names.
You use the RecordIdentifierFeatureName to access data in a FeatureStore.
This name:
feature_group_name : feature_group_name;The name of the FeatureGroup. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
The name:
}type nonrec create_experiment_response = {experiment_arn : experiment_arn option;The Amazon Resource Name (ARN) of the experiment.
*)}type nonrec create_experiment_request = {description : experiment_description option;The description of the experiment.
*)display_name : experiment_entity_name option;The name of the experiment as displayed. The name doesn't need to be unique. If you don't specify DisplayName, the value in ExperimentName is displayed.
experiment_name : experiment_entity_name;The name of the experiment. The name must be unique in your Amazon Web Services account and is not case-sensitive.
*)}type nonrec create_endpoint_config_output = {endpoint_config_arn : endpoint_config_arn;The Amazon Resource Name (ARN) of the endpoint configuration.
*)}type nonrec create_endpoint_config_input = {metrics_config : metrics_config option;The configuration parameters for utilization metrics.
*)enable_network_isolation : boolean_ option;Sets whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.
*)vpc_config : vpc_config option;execution_role_arn : role_arn option;The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform actions on your behalf. For more information, see SageMaker AI Roles.
To be able to pass this role to Amazon SageMaker AI, the caller of this action must have the iam:PassRole permission.
shadow_production_variants : production_variant_list option;An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants. If you use this field, you can only specify one variant for ProductionVariants and one variant for ShadowProductionVariants.
explainer_config : explainer_config option;A member of CreateEndpointConfig that enables explainers.
async_inference_config : async_inference_config option;Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order for your Endpoint to be invoked using InvokeEndpointAsync.
*)kms_key_id : kms_key_id option;The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.
The KmsKeyId can be any of the following formats:
1234abcd-12ab-34cd-56ef-1234567890abarn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890abalias/ExampleAliasarn:aws:kms:us-west-2:111122223333:alias/ExampleAliasThe KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint, UpdateEndpoint requests. For more information, refer to the Amazon Web Services Key Management Service sectionUsing Key Policies in Amazon Web Services KMS
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. If any of the models that you specify in the ProductionVariants parameter use nitro-based instances with local storage, the KmsKeyId parameter does not encrypt instance local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes.
For more information about local instance storage encryption, see SSD Instance Store Volumes.
*)data_capture_config : data_capture_config option;production_variants : production_variant_list;An array of ProductionVariant objects, one for each model that you want to host at this endpoint.
endpoint_config_name : endpoint_config_name;}type nonrec create_endpoint_output = {endpoint_arn : endpoint_arn;The Amazon Resource Name (ARN) of the endpoint.
*)}type nonrec create_endpoint_input = {deployment_config : deployment_config option;endpoint_config_name : endpoint_config_name;endpoint_name : endpoint_name;The name of the endpoint.The name must be unique within an Amazon Web Services Region in your Amazon Web Services account. The name is case-insensitive in CreateEndpoint, but the case is preserved and must be matched in InvokeEndpoint.
}type nonrec create_edge_packaging_job_request = {resource_key : kms_key_id option;The Amazon Web Services KMS key to use when encrypting the EBS volume the edge packaging job runs on.
*)output_config : edge_output_config;Provides information about the output location for the packaged model.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact SageMaker Neo.
*)model_version : edge_version;The version of the model.
*)model_name : entity_name;The name of the model.
*)compilation_job_name : entity_name;The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.
*)edge_packaging_job_name : entity_name;The name of the edge packaging job.
*)}type nonrec deployment_stage = {deployment_config : edge_deployment_config option;Configuration of the deployment details.
*)device_selection_config : device_selection_config;Configuration of the devices in the stage.
*)stage_name : entity_name;The name of the stage.
*)}Contains information about a stage in an edge deployment plan.
type nonrec deployment_stages = deployment_stage listtype nonrec create_edge_deployment_stage_request = {stages : deployment_stages;List of stages to be added to the edge deployment plan.
*)edge_deployment_plan_name : entity_name;The name of the edge deployment plan.
*)}type nonrec create_edge_deployment_plan_response = {edge_deployment_plan_arn : edge_deployment_plan_arn;The ARN of the edge deployment plan.
*)}type nonrec create_edge_deployment_plan_request = {stages : deployment_stages option;List of stages of the edge deployment plan. The number of stages is limited to 10 per deployment.
*)device_fleet_name : entity_name;The device fleet used for this edge deployment plan.
*)model_configs : edge_deployment_model_configs;List of models associated with the edge deployment plan.
*)edge_deployment_plan_name : entity_name;The name of the edge deployment plan.
*)}type nonrec create_domain_response = {url : string1024 option;The URL to the created domain.
*)domain_id : domain_id option;The ID of the created domain.
*)domain_arn : domain_arn option;The Amazon Resource Name (ARN) of the created domain.
*)}type nonrec create_domain_request = {default_space_settings : default_space_settings option;The default settings for shared spaces that users create in the domain.
*)tag_propagation : tag_propagation option;Indicates whether custom tag propagation is supported for the domain. Defaults to DISABLED.
home_efs_file_system_creation : home_efs_file_system_creation option;Indicates whether to create a home EFS file system for the domain. Defaults to Enabled. Set to Disabled to skip EFS creation and reduce domain creation time. You can enable EFS later by calling UpdateDomain.
app_security_group_management : app_security_group_management option;The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided. If setting up the domain for use with RStudio, this value must be set to Service.
kms_key_id : kms_key_id option;SageMaker AI uses Amazon Web Services KMS to encrypt EFS and EBS volumes attached to the domain with an Amazon Web Services managed key by default. For more control, specify a customer managed key.
*)home_efs_file_system_kms_key_id : kms_key_id option;Use KmsKeyId.
app_network_access_type : app_network_access_type option;Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly.
PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker AI, which allows direct internet accessVpcOnly - All traffic is through the specified VPC and subnetsvpc_id : vpc_id option;The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
The field is optional when the AppNetworkAccessType parameter is set to PublicInternetOnly for domains created from Amazon SageMaker Unified Studio.
subnet_ids : subnets option;The VPC subnets that the domain uses for communication.
The field is optional when the AppNetworkAccessType parameter is set to PublicInternetOnly for domains created from Amazon SageMaker Unified Studio.
domain_settings : domain_settings option;A collection of Domain settings.
default_user_settings : user_settings;The default settings to use to create a user profile when UserSettings isn't specified in the call to the CreateUserProfile API.
SecurityGroups is aggregated when specified in both calls. For all other settings in UserSettings, the values specified in CreateUserProfile take precedence over those specified in CreateDomain.
auth_mode : auth_mode;The mode of authentication that members use to access the domain.
*)domain_name : domain_name;A name for the domain.
*)}type nonrec create_device_fleet_request = {enable_iot_role_alias : enable_iot_role_alias option;Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-{DeviceFleetName}".
For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".
*)output_config : edge_output_config;The output configuration for storing sample data collected by the fleet.
*)description : device_fleet_description option;A description of the fleet.
*)role_arn : role_arn option;The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
*)device_fleet_name : entity_name;The name of the fleet that the device belongs to.
*)}type nonrec create_data_quality_job_definition_response = {job_definition_arn : monitoring_job_definition_arn;The Amazon Resource Name (ARN) of the job definition.
*)}type nonrec create_data_quality_job_definition_request = {stopping_condition : monitoring_stopping_condition option;role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.
*)network_config : monitoring_network_config option;Specifies networking configuration for the monitoring job.
*)job_resources : monitoring_resources;data_quality_job_output_config : monitoring_output_config;data_quality_job_input : data_quality_job_input;A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.
*)data_quality_app_specification : data_quality_app_specification;Specifies the container that runs the monitoring job.
*)data_quality_baseline_config : data_quality_baseline_config option;Configures the constraints and baselines for the monitoring job.
*)job_definition_name : monitoring_job_definition_name;The name for the monitoring job definition.
*)}type nonrec create_context_response = {context_arn : context_arn option;The Amazon Resource Name (ARN) of the context.
*)}type nonrec create_context_request = {properties : lineage_entity_parameters option;A list of properties to add to the context.
*)description : experiment_description option;The description of the context.
*)context_type : string256;The context type.
*)source : context_source;The source type, ID, and URI.
*)context_name : context_name;The name of the context. Must be unique to your account in an Amazon Web Services Region.
*)}type nonrec create_compute_quota_response = {compute_quota_id : compute_quota_id;ID of the compute allocation definition.
*)compute_quota_arn : compute_quota_arn;ARN of the compute allocation definition.
*)}type nonrec create_compute_quota_request = {activation_state : activation_state option;The state of the compute allocation being described. Use to enable or disable compute allocation.
Default is Enabled.
compute_quota_target : compute_quota_target;The target entity to allocate compute resources to.
*)compute_quota_config : compute_quota_config;Configuration of the compute allocation definition. This includes the resource sharing option, and the setting to preempt low priority tasks.
*)cluster_arn : cluster_arn;ARN of the cluster.
*)description : entity_description option;Description of the compute allocation definition.
*)name : entity_name;Name to the compute allocation definition.
*)}type nonrec create_compilation_job_response = {compilation_job_arn : compilation_job_arn;If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker AI returns the following data in JSON format:
CompilationJobArn: The Amazon Resource Name (ARN) of the compiled job.}type nonrec create_compilation_job_request = {stopping_condition : stopping_condition;Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker AI ends the compilation job. Use this API to cap model training costs.
*)vpc_config : neo_vpc_config option;A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.
*)output_config : output_config;Provides information about the output location for the compiled model and the target device the model runs on.
*)input_config : input_config option;Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
*)model_package_version_arn : model_package_arn option;The Amazon Resource Name (ARN) of a versioned model package. Provide either a ModelPackageVersionArn or an InputConfig object in the request syntax. The presence of both objects in the CreateCompilationJob request will return an exception.
role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.
During model compilation, Amazon SageMaker AI needs your permission to:
You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.
compilation_job_name : entity_name;A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.
*)}type nonrec create_code_repository_output = {code_repository_arn : code_repository_arn;The Amazon Resource Name (ARN) of the new repository.
*)}type nonrec create_code_repository_input = {git_config : git_config;Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.
*)code_repository_name : entity_name;The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
*)}type nonrec create_cluster_scheduler_config_response = {cluster_scheduler_config_id : cluster_scheduler_config_id;ID of the cluster policy.
*)cluster_scheduler_config_arn : cluster_scheduler_config_arn;ARN of the cluster policy.
*)}type nonrec create_cluster_scheduler_config_request = {description : entity_description option;Description of the cluster policy.
*)scheduler_config : scheduler_config;Configuration about the monitoring schedule.
*)cluster_arn : cluster_arn;ARN of the cluster.
*)name : entity_name;Name for the cluster policy.
*)}type nonrec create_cluster_response = {cluster_arn : cluster_arn;The Amazon Resource Name (ARN) of the cluster.
*)}type nonrec create_cluster_request = {auto_scaling : cluster_auto_scaling_config option;The autoscaling configuration for the cluster. Enables automatic scaling of cluster nodes based on workload demand using a Karpenter-based system.
*)cluster_role : role_arn option;The Amazon Resource Name (ARN) of the IAM role that HyperPod assumes to perform cluster autoscaling operations. This role must have permissions for sagemaker:BatchAddClusterNodes and sagemaker:BatchDeleteClusterNodes. This is only required when autoscaling is enabled and when HyperPod is performing autoscaling operations.
node_provisioning_mode : cluster_node_provisioning_mode option;The mode for provisioning nodes in the cluster. You can specify the following modes:
InService while scaling. This mode is only supported for EKS orchestrated clusters.tiered_storage_config : cluster_tiered_storage_config option;The configuration for managed tier checkpointing on the HyperPod cluster. When enabled, this feature uses a multi-tier storage approach for storing model checkpoints, providing faster checkpoint operations and improved fault tolerance across cluster nodes.
*)node_recovery : cluster_node_recovery option;The node recovery mode for the SageMaker HyperPod cluster. When set to Automatic, SageMaker HyperPod will automatically reboot or replace faulty nodes when issues are detected. When set to None, cluster administrators will need to manually manage any faulty cluster instances.
orchestrator : cluster_orchestrator option;The type of orchestrator to use for the SageMaker HyperPod cluster. Currently, supported values are "Eks" and "Slurm", which is to use an Amazon Elastic Kubernetes Service or Slurm cluster as the orchestrator.
If you specify the Orchestrator field, you must provide exactly one orchestrator configuration: either Eks or Slurm. Specifying both or providing an empty configuration returns a validation error.
vpc_config : vpc_config option;Specifies the Amazon Virtual Private Cloud (VPC) that is associated with the Amazon SageMaker HyperPod cluster. You can control access to and from your resources by configuring your VPC. For more information, see Give SageMaker access to resources in your Amazon VPC.
When your Amazon VPC and subnets support IPv6, network communications differ based on the cluster orchestration platform:
Additional resources for IPv6 configuration:
restricted_instance_groups_config : cluster_restricted_instance_groups_config
option;The configuration for the restricted instance groups (RIG) in the SageMaker HyperPod cluster.
*)restricted_instance_groups : cluster_restricted_instance_group_specifications
option;The specialized instance groups for training models like Amazon Nova to be created in the SageMaker HyperPod cluster.
*)instance_groups : cluster_instance_group_specifications option;The instance groups to be created in the SageMaker HyperPod cluster.
*)cluster_name : cluster_name;The name for the new SageMaker HyperPod cluster.
*)}type nonrec create_auto_ml_job_v2_response = {auto_ml_job_arn : auto_ml_job_arn;The unique ARN assigned to the AutoMLJob when it is created.
*)}type nonrec create_auto_ml_job_v2_request = {auto_ml_compute_config : auto_ml_compute_config option;Specifies the compute configuration for the AutoML job V2.
*)data_split_config : auto_ml_data_split_config option;This structure specifies how to split the data into train and validation datasets.
The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob, the validation dataset must be less than 2 GB in size.
This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.
*)model_deploy_config : model_deploy_config option;Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
*)auto_ml_job_objective : auto_ml_job_objective option;Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective.
AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType), or none at all.AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.security_config : auto_ml_security_config option;The security configuration for traffic encryption or Amazon VPC settings.
*)role_arn : role_arn;The ARN of the role that is used to access the data.
*)auto_ml_problem_type_config : auto_ml_problem_type_config;Defines the configuration settings of one of the supported problem types.
*)output_data_config : auto_ml_output_data_config;Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
*)auto_ml_job_input_data_config : auto_ml_job_input_data_config;An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend on the problem type:
S3Prefix, ManifestFile.S3Prefix, ManifestFile, AugmentedManifestFile.S3Prefix.S3Prefix.S3Prefix.auto_ml_job_name : auto_ml_job_name;Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
*)}type nonrec create_auto_ml_job_response = {auto_ml_job_arn : auto_ml_job_arn;The unique ARN assigned to the AutoML job when it is created.
*)}type nonrec create_auto_ml_job_request = {model_deploy_config : model_deploy_config option;Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
*)generate_candidate_definitions_only : generate_candidate_definitions_only
option;Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
*)role_arn : role_arn;The ARN of the role that is used to access the data.
*)auto_ml_job_config : auto_ml_job_config option;A collection of settings used to configure an AutoML job.
*)auto_ml_job_objective : auto_ml_job_objective option;Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.
*)problem_type : problem_type option;Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.
*)output_data_config : auto_ml_output_data_config;Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
*)input_data_config : auto_ml_input_data_config;An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.
auto_ml_job_name : auto_ml_job_name;Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
*)}type nonrec create_artifact_response = {artifact_arn : artifact_arn option;The Amazon Resource Name (ARN) of the artifact.
*)}type nonrec create_artifact_request = {metadata_properties : metadata_properties option;properties : artifact_properties option;A list of properties to add to the artifact.
*)artifact_type : string256;The artifact type.
*)source : artifact_source;The ID, ID type, and URI of the source.
*)artifact_name : experiment_entity_name option;The name of the artifact. Must be unique to your account in an Amazon Web Services Region.
*)}type nonrec create_app_image_config_response = {app_image_config_arn : app_image_config_arn option;The ARN of the AppImageConfig.
*)}type nonrec create_app_image_config_request = {code_editor_app_image_config : code_editor_app_image_config option;The CodeEditorAppImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in Code Editor.
jupyter_lab_app_image_config : jupyter_lab_app_image_config option;The JupyterLabAppImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in JupyterLab.
kernel_gateway_image_config : kernel_gateway_image_config option;The KernelGatewayImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel will be shown to users before the image starts. Once the image runs, all kernels are visible in JupyterLab.
*)app_image_config_name : app_image_config_name;The name of the AppImageConfig. Must be unique to your account.
*)}type nonrec create_app_response = {app_arn : app_arn option;The Amazon Resource Name (ARN) of the app.
*)}type nonrec create_app_request = {recovery_mode : boolean_ option;Indicates whether the application is launched in recovery mode.
*)resource_spec : resource_spec option;The instance type and the Amazon Resource Name (ARN) of the SageMaker AI image created on the instance.
The value of InstanceType passed as part of the ResourceSpec in the CreateApp call overrides the value passed as part of the ResourceSpec configured for the user profile or the domain. If InstanceType is not specified in any of those three ResourceSpec values for a KernelGateway app, the CreateApp call fails with a request validation error.
app_name : app_name;The name of the app.
*)app_type : app_type;The type of app.
*)space_name : space_name option;The name of the space. If this value is not set, then UserProfileName must be set.
user_profile_name : user_profile_name option;The user profile name. If this value is not set, then SpaceName must be set.
domain_id : domain_id;The domain ID.
*)}type nonrec create_algorithm_output = {algorithm_arn : algorithm_arn;The Amazon Resource Name (ARN) of the new algorithm.
*)}type nonrec create_algorithm_input = {certify_for_marketplace : certify_for_marketplace option;Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.
*)validation_specification : algorithm_validation_specification option;Specifies configurations for one or more training jobs and that SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that SageMaker runs to test the algorithm's inference code.
*)inference_specification : inference_specification option;Specifies details about inference jobs that the algorithm runs, including the following:
training_specification : training_specification;Specifies details about training jobs run by this algorithm, including the following:
train, validation, and test channels.algorithm_description : entity_description option;A description of the algorithm.
*)algorithm_name : entity_name;The name of the algorithm.
*)}type nonrec create_ai_workload_config_response = {ai_workload_config_arn : ai_workload_config_arn;The Amazon Resource Name (ARN) of the created AI workload configuration.
*)}type nonrec create_ai_workload_config_request = {ai_workload_configs : ai_workload_configs option;The benchmark tool configuration and workload specification. Provide the specification as an inline YAML or JSON string.
*)dataset_config : ai_dataset_config option;The dataset configuration for the workload. Specify input data channels with their data sources for benchmark workloads.
*)ai_workload_config_name : ai_entity_name;The name of the AI workload configuration. The name must be unique within your Amazon Web Services account in the current Amazon Web Services Region.
*)}type nonrec create_ai_recommendation_job_response = {ai_recommendation_job_arn : ai_recommendation_job_arn;The Amazon Resource Name (ARN) of the created recommendation job.
*)}type nonrec ai_recommendation_output_config = {mlflow_config : ai_mlflow_config option;The MLflow tracking configuration for the job. If you don't specify this parameter, MLflow tracking is disabled.
*)model_package_group_identifier : ai_resource_identifier option;The name or Amazon Resource Name (ARN) of the model package group where the optimized model is registered as a new model package version.
*)s3_output_location : s3_uri option;The Amazon S3 URI where recommendation results are stored.
*)}The output configuration for an AI recommendation job.
type nonrec create_ai_recommendation_job_request = {compute_spec : ai_recommendation_compute_spec option;The compute resource specification for the recommendation job. You can specify up to 3 instance types to consider, and optionally provide capacity reservation configuration.
*)optimize_model : ai_recommendation_allow_optimization option;Whether to allow model optimization techniques such as quantization, speculative decoding, and kernel tuning. The default is true.
inference_specification : ai_recommendation_inference_specification option;The inference framework configuration. Specify the framework (such as LMI or vLLM) for the recommendation job.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.
*)performance_target : ai_recommendation_performance_target;The performance targets for the recommendation job. Specify constraints on metrics such as time to first token (ttft-ms), throughput, or cost.
ai_workload_config_identifier : ai_resource_identifier;The name or Amazon Resource Name (ARN) of the AI workload configuration to use for this recommendation job.
*)output_config : ai_recommendation_output_config;The output configuration for the recommendation job, including the Amazon S3 location for results and an optional model package group where the optimized model is registered.
*)model_source : ai_model_source;The source of the model to optimize. Specify the Amazon S3 location of the model artifacts.
*)ai_recommendation_job_name : ai_entity_name;The name of the AI recommendation job. The name must be unique within your Amazon Web Services account in the current Amazon Web Services Region.
*)}type nonrec create_ai_benchmark_job_response = {ai_benchmark_job_arn : ai_benchmark_job_arn;The Amazon Resource Name (ARN) of the created benchmark job.
*)}type nonrec ai_benchmark_output_config = {mlflow_config : ai_mlflow_config option;The MLflow tracking configuration for the job. If you don't specify this parameter, MLflow tracking is disabled.
*)s3_output_location : s3_uri;The Amazon S3 URI where benchmark results are stored.
*)}The output configuration for an AI benchmark job.
type nonrec create_ai_benchmark_job_request = {network_config : ai_benchmark_network_config option;The network configuration for the benchmark job, including VPC settings.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.
*)ai_workload_config_identifier : ai_resource_identifier;The name or Amazon Resource Name (ARN) of the AI workload configuration to use for this benchmark job.
*)output_config : ai_benchmark_output_config;The output configuration for the benchmark job, including the Amazon S3 location where benchmark results are stored.
*)benchmark_target : ai_benchmark_target;The target endpoint to benchmark. Specify a SageMaker endpoint by providing its name or Amazon Resource Name (ARN).
*)ai_benchmark_job_name : ai_entity_name;The name of the AI benchmark job. The name must be unique within your Amazon Web Services account in the current Amazon Web Services Region.
*)}type nonrec create_action_response = {action_arn : action_arn option;The Amazon Resource Name (ARN) of the action.
*)}type nonrec create_action_request = {metadata_properties : metadata_properties option;properties : lineage_entity_parameters option;A list of properties to add to the action.
*)status : action_status option;The status of the action.
*)description : experiment_description option;The description of the action.
*)action_type : string256;The action type.
*)source : action_source;The source type, ID, and URI.
*)action_name : experiment_entity_name;The name of the action. Must be unique to your account in an Amazon Web Services Region.
*)}type nonrec cluster_node_ids = cluster_node_id listtype nonrec batch_replace_cluster_nodes_error = {message : string_;A human-readable message describing the error encountered when replacing a node.
*)error_code : batch_replace_cluster_nodes_error_code;The error code associated with the error encountered when replacing a node.
Possible values:
InstanceIdNotFound: The instance does not exist in the specified cluster.InvalidInstanceStatus: The instance is in a state that does not allow replacement. Wait for the instance to finish any ongoing changes before retrying.InstanceIdInUse: Another operation is already in progress for this node. Wait for the operation to complete before retrying.InternalServerError: An internal error occurred while processing this node.node_id : cluster_node_id;The EC2 instance ID of the node that encountered an error during the replacement operation.
*)}Represents an error encountered when replacing a node in a SageMaker HyperPod cluster.
type nonrec batch_replace_cluster_nodes_errors =
batch_replace_cluster_nodes_error listtype nonrec batch_replace_cluster_node_logical_ids_error = {message : string_;A human-readable message describing the error encountered when replacing a node by logical node ID.
*)error_code : batch_replace_cluster_nodes_error_code;The error code associated with the error encountered when replacing a node by logical node ID.
Possible values:
InstanceIdNotFound: The node does not exist in the specified cluster.InvalidInstanceStatus: The node is in a state that does not allow replacement. Wait for the node to finish any ongoing changes before retrying.InstanceIdInUse: Another operation is already in progress for this node. Wait for the operation to complete before retrying.InternalServerError: An internal error occurred while processing this node.node_logical_id : cluster_node_logical_id;The logical node ID of the node that encountered an error during the replacement operation.
*)}Represents an error encountered when replacing a node (identified by its logical node ID) in a SageMaker HyperPod cluster.
type nonrec batch_replace_cluster_node_logical_ids_errors =
batch_replace_cluster_node_logical_ids_error listtype nonrec cluster_node_logical_id_list = cluster_node_logical_id listtype nonrec batch_replace_cluster_nodes_response = {successful_node_logical_ids : cluster_node_logical_id_list option;A list of logical node IDs for which the replacement operation was successfully initiated. This field is only present when NodeLogicalIds were provided in the request.
failed_node_logical_ids : batch_replace_cluster_node_logical_ids_errors option;A list of errors encountered for logical node IDs that could not be replaced. Each error includes the logical node ID, an error code, and a descriptive message. This field is only present when NodeLogicalIds were provided in the request.
failed : batch_replace_cluster_nodes_errors option;A list of errors encountered for EC2 instance IDs that could not be replaced. Each error includes the instance ID, an error code, and a descriptive message.
*)successful : cluster_node_ids option;A list of EC2 instance IDs for which the replacement operation was successfully initiated.
*)}type nonrec batch_replace_cluster_nodes_request = {node_logical_ids : cluster_node_logical_id_list option;A list of logical node IDs to replace with new hardware. You can specify between 1 and 25 logical node IDs.
The NodeLogicalId is a unique identifier that persists throughout the node's lifecycle and can be used to track nodes that are still being provisioned and don't yet have an EC2 instance ID assigned.
Continuous as the NodeProvisioningMode. For clusters using the default provisioning mode, use NodeIds instead.NodeIds or NodeLogicalIds must be provided (or both), but at least one is required.node_ids : cluster_node_ids option;A list of EC2 instance IDs to replace with new hardware. You can specify between 1 and 25 instance IDs.
Replace operations destroy all instance volumes (root and secondary). Ensure you have backed up any important data before proceeding.
NodeIds or NodeLogicalIds must be provided (or both), but at least one is required.i- followed by 17 hexadecimal characters (for example, i-0123456789abcdef0).cluster_name : cluster_name_or_arn;The name or Amazon Resource Name (ARN) of the SageMaker HyperPod cluster containing the nodes to replace.
*)}type nonrec batch_reboot_cluster_nodes_error = {message : string_;A human-readable message describing the error encountered when rebooting a node.
*)error_code : batch_reboot_cluster_nodes_error_code;The error code associated with the error encountered when rebooting a node.
Possible values:
InstanceIdNotFound: The instance does not exist in the specified cluster.InvalidInstanceStatus: The instance is in a state that does not allow rebooting. Wait for the instance to finish any ongoing changes before retrying.InstanceIdInUse: Another operation is already in progress for this node. Wait for the operation to complete before retrying.InternalServerError: An internal error occurred while processing this node.node_id : cluster_node_id;The EC2 instance ID of the node that encountered an error during the reboot operation.
*)}Represents an error encountered when rebooting a node from a SageMaker HyperPod cluster.
type nonrec batch_reboot_cluster_nodes_errors =
batch_reboot_cluster_nodes_error listtype nonrec batch_reboot_cluster_node_logical_ids_error = {message : string_;A human-readable message describing the error encountered when rebooting a node by logical node ID.
*)error_code : batch_reboot_cluster_nodes_error_code;The error code associated with the error encountered when rebooting a node by logical node ID.
Possible values:
InstanceIdNotFound: The node does not exist in the specified cluster.InvalidInstanceStatus: The node is in a state that does not allow rebooting. Wait for the node to finish any ongoing changes before retrying.InstanceIdInUse: Another operation is already in progress for this node. Wait for the operation to complete before retrying.InternalServerError: An internal error occurred while processing this node.node_logical_id : cluster_node_logical_id;The logical node ID of the node that encountered an error during the reboot operation.
*)}Represents an error encountered when rebooting a node (identified by its logical node ID) from a SageMaker HyperPod cluster.
type nonrec batch_reboot_cluster_node_logical_ids_errors =
batch_reboot_cluster_node_logical_ids_error listtype nonrec batch_reboot_cluster_nodes_response = {successful_node_logical_ids : cluster_node_logical_id_list option;A list of logical node IDs for which the reboot operation was successfully initiated. This field is only present when NodeLogicalIds were provided in the request.
failed_node_logical_ids : batch_reboot_cluster_node_logical_ids_errors option;A list of errors encountered for logical node IDs that could not be rebooted. Each error includes the logical node ID, an error code, and a descriptive message. This field is only present when NodeLogicalIds were provided in the request.
failed : batch_reboot_cluster_nodes_errors option;A list of errors encountered for EC2 instance IDs that could not be rebooted. Each error includes the instance ID, an error code, and a descriptive message.
*)successful : cluster_node_ids option;A list of EC2 instance IDs for which the reboot operation was successfully initiated.
*)}type nonrec batch_reboot_cluster_nodes_request = {node_logical_ids : cluster_node_logical_id_list option;A list of logical node IDs to reboot using soft recovery. You can specify between 1 and 25 logical node IDs.
The NodeLogicalId is a unique identifier that persists throughout the node's lifecycle and can be used to track nodes that are still being provisioned and don't yet have an EC2 instance ID assigned.
Continuous as the NodeProvisioningMode. For clusters using the default provisioning mode, use NodeIds instead.NodeIds or NodeLogicalIds must be provided (or both), but at least one is required.node_ids : cluster_node_ids option;A list of EC2 instance IDs to reboot using soft recovery. You can specify between 1 and 25 instance IDs.
NodeIds or NodeLogicalIds must be provided (or both), but at least one is required.i- followed by 17 hexadecimal characters (for example, i-0123456789abcdef0).cluster_name : cluster_name_or_arn;The name or Amazon Resource Name (ARN) of the SageMaker HyperPod cluster containing the nodes to reboot.
*)}type nonrec batch_describe_model_package_summary = {model_package_registration_type : model_package_registration_type option;The package registration type of the model package summary.
*)model_approval_status : model_approval_status option;The approval status of the model.
*)model_package_status : model_package_status;The status of the mortgage package.
*)inference_specification : inference_specification;creation_time : creation_time;The creation time of the mortgage package summary.
*)model_package_description : entity_description option;The description of the model package.
*)model_package_arn : model_package_arn;The Amazon Resource Name (ARN) of the model package.
*)model_package_version : model_package_version option;The version number of a versioned model.
*)model_package_group_name : entity_name;The group name for the model package
*)}Provides summary information about the model package.
type nonrec model_package_summaries =
(model_package_arn * batch_describe_model_package_summary) listThe error code and error description associated with the resource.
type nonrec batch_describe_model_package_error_map =
(model_package_arn * batch_describe_model_package_error) listtype nonrec batch_describe_model_package_output = {batch_describe_model_package_error_map : batch_describe_model_package_error_map
option;A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package.
*)model_package_summaries : model_package_summaries option;The summaries for the model package versions
*)}type nonrec model_package_arn_list = model_package_arn listtype nonrec batch_describe_model_package_input = {model_package_arn_list : model_package_arn_list;The list of Amazon Resource Name (ARN) of the model package groups.
*)}type nonrec batch_delete_cluster_nodes_error = {node_id : cluster_node_id;The ID of the node that encountered an error during the deletion process.
*)message : Smaws_Lib.Smithy_api.Types.string_;A message describing the error encountered when deleting a node.
*)code : batch_delete_cluster_nodes_error_code;The error code associated with the error encountered when deleting a node.
The code provides information about the specific issue encountered, such as the node not being found, the node's status being invalid for deletion, or the node ID being in use by another process.
*)}Represents an error encountered when deleting a node from a SageMaker HyperPod cluster.
type nonrec batch_delete_cluster_nodes_error_list =
batch_delete_cluster_nodes_error listtype nonrec batch_delete_cluster_node_logical_ids_error = {node_logical_id : cluster_node_logical_id;The NodeLogicalId of the node that could not be deleted.
message : string_;A descriptive message providing additional details about the error.
*)code : batch_delete_cluster_nodes_error_code;The error code associated with the failure. Possible values include NodeLogicalIdNotFound, InvalidNodeStatus, and InternalError.
}Information about an error that occurred when attempting to delete a node identified by its NodeLogicalId.
type nonrec batch_delete_cluster_node_logical_ids_error_list =
batch_delete_cluster_node_logical_ids_error listtype nonrec batch_delete_cluster_nodes_response = {successful_node_logical_ids : cluster_node_logical_id_list option;A list of NodeLogicalIds that were successfully deleted from the cluster.
failed_node_logical_ids : batch_delete_cluster_node_logical_ids_error_list
option;A list of NodeLogicalIds that could not be deleted, along with error information explaining why the deletion failed.
successful : cluster_node_ids option;A list of node IDs that were successfully deleted from the specified cluster.
*)failed : batch_delete_cluster_nodes_error_list option;A list of errors encountered when deleting the specified nodes.
*)}type nonrec batch_delete_cluster_nodes_request = {node_logical_ids : cluster_node_logical_id_list option;A list of NodeLogicalIds identifying the nodes to be deleted. You can specify up to 50 NodeLogicalIds. You must specify either NodeLogicalIds, InstanceIds, or both, with a combined maximum of 50 identifiers.
node_ids : cluster_node_ids option;A list of node IDs to be deleted from the specified cluster.
cluster_name : cluster_name_or_arn;The name of the SageMaker HyperPod cluster from which to delete the specified nodes.
*)}type nonrec cluster_availability_zones = cluster_availability_zone listtype nonrec node_addition_result = {instance_types : cluster_instance_types option;The instance types associated with the successfully added node.
*)availability_zones : cluster_availability_zones option;The availability zones associated with the successfully added node.
*)status : cluster_instance_status;The current status of the node. Possible values include Pending, Running, Failed, ShuttingDown, SystemUpdating, DeepHealthCheckInProgress, and NotFound.
instance_group_name : cluster_instance_group_name;The name of the instance group to which the node was added.
*)node_logical_id : cluster_node_logical_id;A unique identifier assigned to the node that can be used to track its provisioning status through the DescribeClusterNode operation.
}Information about a node that was successfully added to the cluster.
type nonrec node_addition_result_list = node_addition_result listtype nonrec batch_add_cluster_nodes_error = {message : string_ option;A descriptive message providing additional details about the error.
*)instance_types : cluster_instance_types option;The instance types associated with the failed node addition request.
*)availability_zones : cluster_availability_zones option;The availability zones associated with the failed node addition request.
*)failed_count : batch_add_failure_count;The number of nodes that failed to be added to the specified instance group.
*)error_code : batch_add_cluster_nodes_error_code;The error code associated with the failure. Possible values include InstanceGroupNotFound and InvalidInstanceGroupState.
instance_group_name : instance_group_name;The name of the instance group for which the error occurred.
*)}Information about an error that occurred during the node addition operation.
type nonrec batch_add_cluster_nodes_error_list =
batch_add_cluster_nodes_error listtype nonrec batch_add_cluster_nodes_response = {failed : batch_add_cluster_nodes_error_list;A list of errors that occurred during the node addition operation. Each entry includes the instance group name, error code, number of failed additions, and an error message.
*)successful : node_addition_result_list;A list of NodeLogicalIDs that were successfully added to the cluster. The NodeLogicalID is unique per cluster and does not change between instance replacements. Each entry includes a NodeLogicalId that can be used to track the node's provisioning status (with DescribeClusterNode), the instance group name, and the current status of the node.
}type nonrec add_cluster_node_specification = {instance_types : cluster_instance_types option;The instance types to use when adding nodes. Use this to target specific instance types within a flexible instance group.
*)availability_zones : cluster_availability_zones option;The availability zones in which to add nodes. Use this to target node placement in specific availability zones within a flexible instance group.
*)increment_target_count_by : batch_add_increment_count;The number of nodes to add to the specified instance group. The total number of nodes across all instance groups in a single request cannot exceed 50.
*)instance_group_name : cluster_instance_group_name;The name of the instance group to which you want to add nodes.
*)}Specifies an instance group and the number of nodes to add to it.
type nonrec add_cluster_node_specification_list =
add_cluster_node_specification listtype nonrec batch_add_cluster_nodes_request = {nodes_to_add : add_cluster_node_specification_list;A list of instance groups and the number of nodes to add to each. You can specify up to 5 instance groups in a single request, with a maximum of 50 nodes total across all instance groups.
*)client_token : string_ option;A unique, case-sensitive identifier that you provide to ensure the idempotency of the request. This token is valid for 8 hours. If you retry the request with the same client token within this timeframe and the same parameters, the API returns the same set of NodeLogicalIds with their latest status.
cluster_name : cluster_name_or_arn;The name of the HyperPod cluster to which you want to add nodes.
*)}type nonrec attach_cluster_node_volume_response = {device_name : volume_device_name;The device name assigned to your attached volume on the target instance.
*)status : volume_attachment_status;The current status of your volume attachment operation.
*)attach_time : timestamp;The timestamp when the volume attachment operation was initiated by the SageMaker HyperPod service.
*)volume_id : volume_id;The unique identifier of your EBS volume that was attached.
*)node_id : cluster_node_id;The unique identifier of the cluster node where your volume was attached.
*)cluster_arn : cluster_arn;The Amazon Resource Name (ARN) of your SageMaker HyperPod cluster where the volume attachment operation was performed.
*)}type nonrec attach_cluster_node_volume_request = {volume_id : volume_id;The unique identifier of your EBS volume to attach. The volume must be in the available state.
node_id : cluster_node_id;The unique identifier of the cluster node to which you want to attach the volume. The node must belong to your specified HyperPod cluster and cannot be part of a Restricted Instance Group (RIG).
*)cluster_arn : cluster_arn;The Amazon Resource Name (ARN) of your SageMaker HyperPod cluster containing the target node. Your cluster must use EKS as the orchestration and be in the InService state.
}type nonrec associate_trial_component_response = {trial_arn : trial_arn option;The Amazon Resource Name (ARN) of the trial.
*)trial_component_arn : trial_component_arn option;The Amazon Resource Name (ARN) of the trial component.
*)}type nonrec associate_trial_component_request = {trial_name : experiment_entity_name;The name of the trial to associate with.
*)trial_component_name : experiment_entity_name;The name of the component to associated with the trial.
*)}type nonrec add_association_response = {destination_arn : association_entity_arn option;The Amazon Resource Name (ARN) of the destination.
*)source_arn : association_entity_arn option;The ARN of the source.
*)}type nonrec add_association_request = {association_type : association_edge_type option;The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use.
destination_arn : association_entity_arn;The Amazon Resource Name (ARN) of the destination.
*)source_arn : association_entity_arn;The ARN of the source.
*)}