Merge branch 'master' into numpy_to_sparse

Author: andremoeller

setup.py

@@ -10,7 +10,7 @@ def read(fname):
 
 
 setup(name="sagemaker",
-      version="1.0.0",
+      version="1.0.1",
       description="Open source library for training and deploying models on Amazon SageMaker.",
       packages=find_packages('src'),
       package_dir={'': 'src'},

src/sagemaker/model.py

@@ -86,7 +86,7 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None):
         """
         container_def = self.prepare_container_def(instance_type)
         model_name = self.name or name_from_image(container_def['Image'])
-        self.sagemaker_session.create_model(model_name, self.role, container_def, [])
+        self.sagemaker_session.create_model(model_name, self.role, container_def)
         production_variant = sagemaker.production_variant(model_name, instance_type, initial_instance_count)
         self.endpoint_name = endpoint_name or model_name
         self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant])

src/sagemaker/predictor.py

@@ -246,7 +246,7 @@ def __call__(self, data):
         if hasattr(data, 'read'):
             return _json_serialize_from_buffer(data)
 
-        raise ValueError("Unable to handle input format: ".format(type(data)))
+        raise ValueError("Unable to handle input format: {}".format(type(data)))
 
 
 json_serializer = _JsonSerializer()

src/sagemaker/session.py

@@ -238,7 +238,7 @@ def train(self, image, input_mode, input_config, role, job_name, output_config,
         LOGGER.debug('train request: {}'.format(json.dumps(train_request, indent=4)))
         self.sagemaker_client.create_training_job(**train_request)
 
-    def create_model(self, name, role, primary_container, supplemental_containers=None):
+    def create_model(self, name, role, primary_container):
         """Create an Amazon SageMaker ``Model``.
 
         Specify the S3 location of the model artifacts and Docker image containing
@@ -253,36 +253,27 @@ def create_model(self, name, role, primary_container, supplemental_containers=No
             primary_container (str or dict[str, str]): Docker image which defines the inference code.
                 You can also specify the return value of ``sagemaker.container_def()``, which is used to create
                 more advanced container configurations, including model containers which need artifacts from S3.
-            supplemental_containers (list[str or dict[str, str]]): List of Docker images which define
-                additional containers that need to be run in addition to the primary container (default: None).
-                You can also specify the return values of ``sagemaker.container_def()``, which the API uses to create
-                more advanced container configurations, including model containers which need artifacts from S3.
 
         Returns:
             str: Name of the Amazon SageMaker ``Model`` created.
         """
         role = self.expand_role(role)
         primary_container = _expand_container_def(primary_container)
-        if supplemental_containers is None:
-            supplemental_containers = []
-        supplemental_containers = [_expand_container_def(sc) for sc in supplemental_containers]
         LOGGER.info('Creating model with name: {}'.format(name))
         LOGGER.debug("create_model request: {}".format({
             'name': name,
             'role': role,
-            'primary_container': primary_container,
-            'supplemental_containers': supplemental_containers
+            'primary_container': primary_container
         }))
 
         self.sagemaker_client.create_model(ModelName=name,
                                            PrimaryContainer=primary_container,
-                                           SupplementalContainers=supplemental_containers,
                                            ExecutionRoleArn=role)
 
         return name
 
     def create_model_from_job(self, training_job_name, name=None, role=None, primary_container_image=None,
-                              model_data_url=None, env={}, supplemental_containers=None):
+                              model_data_url=None, env={}):
         """Create an Amazon SageMaker ``Model`` from a SageMaker Training Job.
 
         Args:
@@ -296,8 +287,6 @@ def create_model_from_job(self, training_job_name, name=None, role=None, primary
             model_data_url (str): S3 location of the model data (default: None). If None, defaults to
                 the ``ModelS3Artifacts`` of ``training_job_name``.
             env (dict[string,string]): Model environment variables (default: {}).
-            supplemental_containers (list[dict[str, str]]): A list of supplemental Docker containers
-                (default: None). Defines the ``SupplementalContainers`` property on the created ``Model``.
 
         Returns:
             str: The name of the created ``Model``.
@@ -309,7 +298,7 @@ def create_model_from_job(self, training_job_name, name=None, role=None, primary
             primary_container_image or training_job['AlgorithmSpecification']['TrainingImage'],
             model_data_url=model_data_url or training_job['ModelArtifacts']['S3ModelArtifacts'],
             env=env)
-        return self.create_model(name, role, primary_container, supplemental_containers)
+        return self.create_model(name, role, primary_container)
 
     def create_endpoint_config(self, name, model_name, initial_instance_count, instance_type):
         """Create an Amazon SageMaker endpoint configuration.

tests/unit/test_create_deploy_entities.py

@@ -1,15 +1,15 @@
-# Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-#     http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file 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.
+# Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file 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.
 import pytest
 from mock import Mock
 
@@ -36,46 +36,23 @@ def sagemaker_session():
 
 
 def test_create_model(sagemaker_session):
-    supplemental_containers = [FULL_CONTAINER_DEF, FULL_CONTAINER_DEF]
 
-    returned_name = sagemaker_session.create_model(name=MODEL_NAME, role=ROLE, primary_container=FULL_CONTAINER_DEF,
-                                                   supplemental_containers=supplemental_containers)
+    returned_name = sagemaker_session.create_model(name=MODEL_NAME, role=ROLE, primary_container=FULL_CONTAINER_DEF)
 
     assert returned_name == MODEL_NAME
     sagemaker_session.sagemaker_client.create_model.assert_called_once_with(
         ModelName=MODEL_NAME,
         PrimaryContainer=FULL_CONTAINER_DEF,
-        SupplementalContainers=supplemental_containers,
         ExecutionRoleArn=EXPANDED_ROLE)
 
 
-def test_create_model_no_supplemental_containers(sagemaker_session):
-    sagemaker_session.create_model(name=MODEL_NAME, role=ROLE, primary_container=FULL_CONTAINER_DEF)
-
-    _1, _2, create_model_kwargs = sagemaker_session.sagemaker_client.create_model.mock_calls[0]
-    assert create_model_kwargs['SupplementalContainers'] == []
-
-
 def test_create_model_expand_primary_container(sagemaker_session):
     sagemaker_session.create_model(name=MODEL_NAME, role=ROLE, primary_container=IMAGE)
 
     _1, _2, create_model_kwargs = sagemaker_session.sagemaker_client.create_model.mock_calls[0]
     assert create_model_kwargs['PrimaryContainer'] == {'Environment': {}, 'Image': IMAGE}
 
 
-def test_create_model_expand_supplemental_containers(sagemaker_session):
-    supp_image1 = 'suppimage1'
-    supp_image2 = 'suppimage2'
-    supplemental_containers = [supp_image1, supp_image2]
-
-    sagemaker_session.create_model(name=MODEL_NAME, role=ROLE, primary_container=IMAGE,
-                                   supplemental_containers=supplemental_containers)
-
-    expected = [{'Environment': {}, 'Image': supp_image1}, {'Environment': {}, 'Image': supp_image2}]
-    _1, _2, create_model_kwargs = sagemaker_session.sagemaker_client.create_model.mock_calls[0]
-    assert create_model_kwargs['SupplementalContainers'] == expected
-
-
 def test_create_endpoint_config(sagemaker_session):
     returned_name = sagemaker_session.create_endpoint_config(name=ENDPOINT_CONFIG_NAME, model_name=MODEL_NAME,
                                                              initial_instance_count=INITIAL_INSTANCE_COUNT,

tests/unit/test_session.py

@@ -331,8 +331,7 @@ def test_create_model_from_job(sagemaker_session):
         ModelName='jobname',
         PrimaryContainer={
             'Environment': {}, 'ModelDataUrl': 's3://sagemaker-123/output/jobname/model/model.tar.gz',
-            'Image': 'myimage'},
-        SupplementalContainers=[])
+            'Image': 'myimage'})
 
 
 def test_create_model_from_job_with_image(sagemaker_session):
@@ -355,15 +354,6 @@ def test_create_model_from_job_with_container_def(sagemaker_session):
     assert c_def['Environment'] == {'a': 'b'}
 
 
-def test_create_model_from_job_with_supplemental_containers(sagemaker_session):
-    ims = sagemaker_session
-    ims.sagemaker_client.describe_training_job.return_value = COMPLETED_DESCRIBE_JOB_RESULT
-    ims.create_model_from_job(JOB_NAME, supplemental_containers=[sagemaker.container_def('some-image')])
-    [create_model_call] = ims.sagemaker_client.create_model.call_args_list
-    [c_def] = create_model_call[1]['SupplementalContainers']
-    assert c_def['Image'] == 'some-image'
-
-
 def test_endpoint_from_production_variants(sagemaker_session):
     ims = sagemaker_session
     ims.sagemaker_client.describe_endpoint = Mock(return_value={'EndpointStatus': 'InService'})