Starts 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.
See https://www.paws-r-sdk.com/docs/sagemaker_create_compilation_job/ for full documentation.
sagemaker_create_compilation_job(
CompilationJobName,
RoleArn,
ModelPackageVersionArn = NULL,
InputConfig = NULL,
OutputConfig,
VpcConfig = NULL,
StoppingCondition,
Tags = NULL
)[required] 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.
[required] 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:
Read input data from an S3 bucket
Write model artifacts to an S3 bucket
Write logs to Amazon CloudWatch Logs
Publish metrics to Amazon CloudWatch
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.
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
create_compilation_job request
will return an exception.
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.
[required] Provides information about the output location for the compiled model and the target device the model runs on.
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.
[required] 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.
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.