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From GitBox <...@apache.org>
Subject [GitHub] troychen728 commented on a change in pull request #3658: [AIRFLOW-2524] Add Amazon SageMaker Training
Date Fri, 03 Aug 2018 17:30:44 GMT
troychen728 commented on a change in pull request #3658: [AIRFLOW-2524] Add Amazon SageMaker
Training
URL: https://github.com/apache/incubator-airflow/pull/3658#discussion_r207614985
 
 

 ##########
 File path: airflow/contrib/operators/sagemaker_create_training_job_operator.py
 ##########
 @@ -0,0 +1,98 @@
+# -*- 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.sagemaker_hook import SageMakerHook
+from airflow.models import BaseOperator
+from airflow.utils import apply_defaults
+from airflow.exceptions import AirflowException
+
+
+class SageMakerCreateTrainingJobOperator(BaseOperator):
+
+    """
+       Initiate a SageMaker training
+
+       This operator returns The ARN of the model created in Amazon SageMaker
+
+       :param training_job_config:
+       The configuration necessary to start a training job (templated)
+       :type training_job_config: dict
+       :param region_name: The AWS region_name
+       :type region_name: string
+       :param sagemaker_conn_id: The SageMaker connection ID to use.
+       :type aws_conn_id: string
 
 Review comment:
   @Fokko 
   Thank you very much for your reply. I agree with you that multiple jobs can still run in
parallel, but to my understanding, there's also an option to run in sequential.  More importantly,
I think one thing that is very essential is that, what if there's already a training job running
(whether initiated from Airflow or other means) before a dag is scheduled to run? Since a
training job can takes days to finish, I think it might be a potential use case for Airflow
users. As a result, a DAG can start with a sensor, and have other nodes dependent on the sensor.
It would not be possible to accomplish this if operator, and sensor are coupled together.

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