Source code for airflow.contrib.operators.sagemaker_tuning_operator

# -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

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: https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#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 """ # noqa: E501 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 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'] ) }