airflow.contrib.operators.sagemaker_tuning_operator

Module Contents

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

Bases: airflow.contrib.operators.sagemaker_base_operator.SageMakerBaseOperator

Initiate a SageMaker hyperparameter tuning job.

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

Parameters

  • config (dict) –

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

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

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

  • wait_for_completion (bool) – Set to True to wait until the tuning job finishes.

  • check_interval (int) – If wait is set to True, the time interval, in seconds, that this operation waits to check the status of the tuning job.

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

integer_fields = [['HyperParameterTuningJobConfig', 'ResourceLimits', 'MaxNumberOfTrainingJobs'], ['HyperParameterTuningJobConfig', 'ResourceLimits', 'MaxParallelTrainingJobs'], ['TrainingJobDefinition', 'ResourceConfig', 'InstanceCount'], ['TrainingJobDefinition', 'ResourceConfig', 'VolumeSizeInGB'], ['TrainingJobDefinition', 'StoppingCondition', 'MaxRuntimeInSeconds']][source]
expand_role(self)[source]
execute(self, context)[source]

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