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Subject [GitHub] yangaws commented on a change in pull request #4091: [AIRFLOW-2524] Update SageMaker hook, operator and sensor for training, tuning and transform
Date Thu, 01 Nov 2018 00:36:21 GMT
yangaws commented on a change in pull request #4091: [AIRFLOW-2524] Update SageMaker hook,
operator and sensor for training, tuning and transform

 File path: airflow/contrib/operators/
 @@ -0,0 +1,112 @@
+# -*- 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
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# 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
+class SageMakerTransformOperator(SageMakerBaseOperator):
+    """
+    Initiate a SageMaker transform job.
+    This operator returns The ARN of the model created in Amazon SageMaker.
+    :param config: The configuration necessary to start a transform job (templated)
+    :type config: dict
+    :param model_config:
+        The configuration necessary to create a SageMaker model, the default is none
+        which means the SageMaker model used for the SageMaker transform job already exists.
+        If given, it will be used to create a SageMaker model before creating
+        the SageMaker transform job
+    :type model_config: dict
+    :param aws_conn_id: The AWS connection ID to use.
+    :type aws_conn_id: string
+    :param wait_for_completion: if the program should keep running until job finishes
+    :type wait_for_completion: 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 transform job
+    :type check_interval: int
+    :param max_ingestion_time: if wait is set to be true, the operator will fail
+        if the transform job hasn't finish within the max_ingestion_time in seconds
+        (Caution: be careful to set this parameters because transform can take very long)
+    :type max_ingestion_time: int
+    """
+    @apply_defaults
+    def __init__(self,
+                 config,
+                 wait_for_completion=True,
+                 check_interval=30,
+                 max_ingestion_time=None,
+                 *args, **kwargs):
+        super(SageMakerTransformOperator, 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
+        self.create_integer_fields()
+    def create_integer_fields(self):
+        self.integer_fields = [
+            ['Transform', 'TransformResources', 'InstanceCount'],
+            ['Transform', 'MaxConcurrentTransforms'],
+            ['Transform', 'MaxPayloadInMB']
+        ]
+        if 'Transform' not in self.config:
+            for field in self.integer_fields:
+                field.pop(0)
+    def expand_role(self):
+        if 'Model' not in self.config:
+            return
+        config = self.config['Model']
+        if 'ExecutionRoleArn' in config:
+            hook = AwsHook()
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