sagemaker.mlops.workflow.automl_step

sagemaker.mlops.workflow.automl_step#

The AutoMLStep definition for SageMaker Pipelines Workflows

Classes

AutoMLStep(name, step_args[, display_name, ...])

AutoMLStep for SageMaker Pipelines Workflows.
class sagemaker.mlops.workflow.automl_step.AutoMLStep(name: str, step_args: _JobStepArguments, display_name: str | None = None, description: str | None = None, cache_config: CacheConfig | None = None, depends_on: List[str | Step] | None = None, retry_policies: List[RetryPolicy] | None = None)[source]#

Bases: ConfigurableRetryStep

AutoMLStep for SageMaker Pipelines Workflows.

property arguments: Dict[str, Any] | List[Dict[str, Any]]#

The arguments dictionary that is used to call create_auto_ml_job.

NOTE: The CreateAutoMLJob request is not quite the

args list that workflow needs.

ModelDeployConfig and GenerateCandidateDefinitionsOnly

attribute cannot be included.

get_best_auto_ml_model_builder(role, sagemaker_session=None)[source]#

Get the best candidate model artifacts, image uri and env variables for the best model.

Parameters:

  • role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker AutoML jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts.

  • sagemaker_session (sagemaker.core.helper.session.Session) –

    A SageMaker Session object, used for SageMaker interactions. If the best model will be used as part of ModelStep, then sagemaker_session should be class:~sagemaker.workflow.pipeline_context.PipelineSession. Example:

    model = Model(sagemaker_session=PipelineSession())
model_step = ModelStep(step_args=model.register())

property properties#

A Properties object representing the DescribeAutoMLJobResponse data model.

to_request() → Dict[str, Any] | List[Dict[str, Any]][source]#

Updates the dictionary with cache configuration.

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