Smaws_Client_SageMaker.CreateTrainingJobStarts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.
In the request body, you provide the following:
AlgorithmSpecification - Identifies the training algorithm to use.HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.
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 security-sensitive information included in the request hyperparameter variable or plain text fields.
InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored.OutputDataConfig - Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training.ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training.RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training.StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete.Environment - 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.
RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.For more information about SageMaker, see How It Works.
val error_to_string :
[ Smaws_Lib.Protocols.AwsJson.error
| `ResourceInUse of Types.resource_in_use
| `ResourceLimitExceeded of Types.resource_limit_exceeded
| `ResourceNotFound of Types.resource_not_found ] ->
stringval request :
'http_type Smaws_Lib.Context.t ->
Types.create_training_job_request ->
(Types.create_training_job_response,
[> Smaws_Lib.Protocols.AwsJson.error
| `ResourceInUse of Types.resource_in_use
| `ResourceLimitExceeded of Types.resource_limit_exceeded
| `ResourceNotFound of Types.resource_not_found ])
Stdlib.resultval request_with_metadata :
'http_type Smaws_Lib.Context.t ->
Types.create_training_job_request ->
(Types.create_training_job_response Smaws_Lib.Response.t,
[> Smaws_Lib.Protocols.AwsJson.error
| `ResourceInUse of Types.resource_in_use
| `ResourceLimitExceeded of Types.resource_limit_exceeded
| `ResourceNotFound of Types.resource_not_found ]
* Smaws_Lib.Response.metadata)
Stdlib.result