airflow.contrib.operators.sagemaker_training_operator

Module Contents

class airflow.contrib.operators.sagemaker_training_operator.SageMakerTrainingOperator(config, wait_for_completion=True, print_log=True, check_interval=30, max_ingestion_time=None, *args, **kwargs)[source]

Bases: airflow.contrib.operators.sagemaker_base_operator.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.

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