Smaws_Client_SageMaker.CreateCompilationJobStarts a model compilation job. After the model has been compiled, Amazon SageMaker AI saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
If you choose to host your model using Amazon SageMaker AI hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.
To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
val error_to_string :
[ Smaws_Lib.Protocols.AwsJson.error
| `ResourceInUse of Types.resource_in_use
| `ResourceLimitExceeded of Types.resource_limit_exceeded ] ->
stringval request :
'http_type Smaws_Lib.Context.t ->
Types.create_compilation_job_request ->
(Types.create_compilation_job_response,
[> Smaws_Lib.Protocols.AwsJson.error
| `ResourceInUse of Types.resource_in_use
| `ResourceLimitExceeded of Types.resource_limit_exceeded ])
Stdlib.resultval request_with_metadata :
'http_type Smaws_Lib.Context.t ->
Types.create_compilation_job_request ->
(Types.create_compilation_job_response Smaws_Lib.Response.t,
[> Smaws_Lib.Protocols.AwsJson.error
| `ResourceInUse of Types.resource_in_use
| `ResourceLimitExceeded of Types.resource_limit_exceeded ]
* Smaws_Lib.Response.metadata)
Stdlib.result