airflow.providers.amazon.aws.operators.sagemaker_training

Module Contents

class airflow.providers.amazon.aws.operators.sagemaker_training.SageMakerTrainingOperator(*, config: dict, wait_for_completion: bool = True, print_log: bool = True, check_interval: int = 30, max_ingestion_time: Optional[int] = None, action_if_job_exists: str = 'increment', **kwargs)[source]

Bases: airflow.providers.amazon.aws.operators.sagemaker_base.SageMakerBaseOperator

Initiate a SageMaker training job.

This operator returns The ARN of the training job created in Amazon SageMaker.

Parameters

  • config (dict) --

    The configuration necessary to start a training job (templated).

    For details of the configuration parameter see SageMaker.Client.create_training_job()

  • aws_conn_id (str) -- The AWS connection ID to use.

  • wait_for_completion (bool) -- If wait is set to True, the time interval, in seconds, that the operation waits to check the status of the training job.

  • print_log (bool) -- if the operator should print the cloudwatch log during training

  • check_interval (int) -- if wait is set to be true, this is the time interval in seconds which the operator will check the status of the training job

  • max_ingestion_time (int) -- If wait is set to True, the operation fails if the training job doesn't finish within max_ingestion_time seconds. If you set this parameter to None, the operation does not timeout.

  • action_if_job_exists (str) -- Behaviour if the job name already exists. Possible options are "increment" (default) and "fail".

integer_fields = [['ResourceConfig', 'InstanceCount'], ['ResourceConfig', 'VolumeSizeInGB'], ['StoppingCondition', 'MaxRuntimeInSeconds']][source]
expand_role(self)[source]
execute(self, context)[source]

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