Source code for airflow.contrib.operators.sagemaker_tuning_operator

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from airflow.contrib.hooks.aws_hook import AwsHook
from airflow.contrib.operators.sagemaker_base_operator import SageMakerBaseOperator
from airflow.utils.decorators import apply_defaults
from airflow.exceptions import AirflowException

[docs]class SageMakerTuningOperator(SageMakerBaseOperator): """ Initiate a SageMaker hyperparameter tuning job. This operator returns The ARN of the tuning job created in Amazon SageMaker. :param config: The configuration necessary to start a tuning job (templated). For details of the configuration parameter see :py:meth:`SageMaker.Client.create_hyper_parameter_tuning_job` :type config: dict :param aws_conn_id: The AWS connection ID to use. :type aws_conn_id: str :param wait_for_completion: Set to True to wait until the tuning job finishes. :type wait_for_completion: bool :param check_interval: If wait is set to True, the time interval, in seconds, that this operation waits to check the status of the tuning job. :type check_interval: int :param max_ingestion_time: 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. :type max_ingestion_time: int """
[docs] integer_fields = [ ['HyperParameterTuningJobConfig', 'ResourceLimits', 'MaxNumberOfTrainingJobs'], ['HyperParameterTuningJobConfig', 'ResourceLimits', 'MaxParallelTrainingJobs'], ['TrainingJobDefinition', 'ResourceConfig', 'InstanceCount'], ['TrainingJobDefinition', 'ResourceConfig', 'VolumeSizeInGB'], ['TrainingJobDefinition', 'StoppingCondition', 'MaxRuntimeInSeconds']
] @apply_defaults def __init__(self, config, wait_for_completion=True, check_interval=30, max_ingestion_time=None, *args, **kwargs): super(SageMakerTuningOperator, self).__init__(config=config, *args, **kwargs) self.config = config self.wait_for_completion = wait_for_completion self.check_interval = check_interval self.max_ingestion_time = max_ingestion_time
[docs] def expand_role(self): if 'TrainingJobDefinition' in self.config: config = self.config['TrainingJobDefinition'] if 'RoleArn' in config: hook = AwsHook(self.aws_conn_id) config['RoleArn'] = hook.expand_role(config['RoleArn'])
[docs] def execute(self, context): self.preprocess_config() 'Creating SageMaker Hyper-Parameter Tuning Job %s', self.config['HyperParameterTuningJobName'] ) response = self.hook.create_tuning_job( self.config, wait_for_completion=self.wait_for_completion, check_interval=self.check_interval, max_ingestion_time=self.max_ingestion_time ) if response['ResponseMetadata']['HTTPStatusCode'] != 200: raise AirflowException('Sagemaker Tuning Job creation failed: %s' % response) else: return { 'Tuning': self.hook.describe_tuning_job( self.config['HyperParameterTuningJobName']
) }