Source code for airflow.contrib.operators.sagemaker_training_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 SageMakerTrainingOperator(SageMakerBaseOperator): """ Initiate a SageMaker training job. This operator returns The ARN of the training job created in Amazon SageMaker. :param config: The configuration necessary to start a training job (templated). For details of the configuration parameter see :py:meth:`SageMaker.Client.create_training_job` :type config: dict :param aws_conn_id: The AWS connection ID to use. :type aws_conn_id: str :param wait_for_completion: If wait is set to True, the time interval, in seconds, that the operation waits to check the status of the training job. :type wait_for_completion: bool :param print_log: if the operator should print the cloudwatch log during training :type print_log: bool :param check_interval: 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 :type check_interval: int :param max_ingestion_time: 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. :type max_ingestion_time: int """
[docs] integer_fields = [ ['ResourceConfig', 'InstanceCount'], ['ResourceConfig', 'VolumeSizeInGB'], ['StoppingCondition', 'MaxRuntimeInSeconds']
] @apply_defaults def __init__(self, config, wait_for_completion=True, print_log=True, check_interval=30, max_ingestion_time=None, *args, **kwargs): super(SageMakerTrainingOperator, self).__init__(config=config, *args, **kwargs) self.wait_for_completion = wait_for_completion self.print_log = print_log self.check_interval = check_interval self.max_ingestion_time = max_ingestion_time
[docs] def expand_role(self): if 'RoleArn' in self.config: hook = AwsHook(self.aws_conn_id) self.config['RoleArn'] = hook.expand_role(self.config['RoleArn'])
[docs] def execute(self, context): self.preprocess_config()'Creating SageMaker Training Job %s.', self.config['TrainingJobName']) response = self.hook.create_training_job( self.config, wait_for_completion=self.wait_for_completion, print_log=self.print_log, check_interval=self.check_interval, max_ingestion_time=self.max_ingestion_time ) if response['ResponseMetadata']['HTTPStatusCode'] != 200: raise AirflowException('Sagemaker Training Job creation failed: %s' % response) else: return { 'Training': self.hook.describe_training_job( self.config['TrainingJobName']
) }