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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()
self.log.info(
'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']
)
}