# -*- 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 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
"""
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
def expand_role(self):
if 'RoleArn' in self.config:
hook = AwsHook(self.aws_conn_id)
self.config['RoleArn'] = hook.expand_role(self.config['RoleArn'])
def execute(self, context):
self.preprocess_config()
self.log.info('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']
)
}