Address comments.

Author: Chuyang Deng

CHANGELOG.rst

@@ -7,7 +7,7 @@ CHANGELOG
 
 * doc-fix: update information about saving models in the MXNet README
 * doc-fix: change ReadTheDocs links from latest to stable
-* feature: Support for predictor to delete endpoint, and delete endpoint configuration with ``delete_endpoint()`` by default
+* feature: Make predictor to delete endpoint configuration by default when calling ``delete_endpoint()``
 * feature: Support for model class to delete SageMaker model
 * feature: Support for transformer to delete Sagemaker model
 

README.rst

@@ -196,7 +196,7 @@ Here is an end to end example of how to use a SageMaker Estimator:
 The example above will eventually delete both the SageMaker endpoint and endpoint configuration through `delete_endpoint()`. If you want to keep your SageMaker endpoint configuration, use the value False for the `delete_endpoint_config` parameter, as shown below.
 
 .. code:: python
-    # Only delete the endpoint and keep the endpoint endpoint configuration
+    # Only delete the SageMaker endpoint, while keeping the corresponding endpoint configuration.
     mxnet_predictor.delete_endpoint(delete_endpoint_config=False)
 
 Additionally, it is possible to deploy a different endpoint configuration, which links to your model, to an already existing SageMaker endpoint.

src/sagemaker/predictor.py

@@ -108,12 +108,9 @@ def _create_request_args(self, data, initial_args=None):
     def _delete_endpoint_config(self):
         """Delete the Amazon SageMaker endpoint configuration
         """
-        try:
-            endpoint_description = self.sagemaker_session.sagemaker_client.describe_endpoint(EndpointName=self.endpoint)
-            endpoint_config_name = endpoint_description['EndpointConfigName']
-            self.sagemaker_session.delete_endpoint_config(endpoint_config_name)
-        except Exception:
-            raise ValueError('The endpoint this config attached to does not exist.')
+        endpoint_description = self.sagemaker_session.sagemaker_client.describe_endpoint(EndpointName=self.endpoint)
+        endpoint_config_name = endpoint_description['EndpointConfigName']
+        self.sagemaker_session.delete_endpoint_config(endpoint_config_name)
 
     def delete_endpoint(self, delete_endpoint_config=True):
         """Delete the Amazon SageMaker endpoint backing this predictor. Also delete the endpoint configuration attached

tests/integ/test_transformer.py

@@ -106,46 +106,6 @@ def test_attach_transform_kmeans(sagemaker_session):
         attached_transformer.wait()
 
 
-# def test_transformer_delete_model(sagemaker_session):
-#     data_path = os.path.join(DATA_DIR, 'one_p_mnist')
-#     pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}
-#
-#     train_set_path = os.path.join(data_path, 'mnist.pkl.gz')
-#     with gzip.open(train_set_path, 'rb') as f:
-#         train_set, _, _ = pickle.load(f, **pickle_args)
-#
-#     kmeans = KMeans(role='SageMakerRole', train_instance_count=1,
-#                     train_instance_type='ml.c4.xlarge', k=10, sagemaker_session=sagemaker_session,
-#                     output_path='s3://{}/'.format(sagemaker_session.default_bucket()))
-#
-#     kmeans.init_method = 'random'
-#     kmeans.max_iterations = 1
-#     kmeans.tol = 1
-#     kmeans.num_trials = 1
-#     kmeans.local_init_method = 'kmeans++'
-#     kmeans.half_life_time_size = 1
-#     kmeans.epochs = 1
-#
-#     records = kmeans.record_set(train_set[0][:100])
-#     with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
-#         kmeans.fit(records)
-#
-#     transform_input_path = os.path.join(data_path, 'transform_input.csv')
-#     transform_input_key_prefix = 'integ-test-data/one_p_mnist/transform'
-#     transform_input = kmeans.sagemaker_session.upload_data(path=transform_input_path,
-#                                                            key_prefix=transform_input_key_prefix)
-#
-#     transformer = _create_transformer_and_transform_job(kmeans, transform_input)
-#     with timeout(minutes=TRANSFORM_DEFAULT_TIMEOUT_MINUTES):
-#         transformer.wait()
-#
-#     transformer.delete_model()
-#
-#     with pytest.raises(Exception) as exception:
-#         sagemaker_session.sagemaker_client.describe_model(ModelName=transformer.model_name)
-#         assert 'Could not find model' in exception.value.message
-
-
 def test_transform_mxnet_vpc(sagemaker_session, mxnet_full_version):
     data_path = os.path.join(DATA_DIR, 'mxnet_mnist')
     script_path = os.path.join(data_path, 'mnist.py')

tests/unit/test_predictor.py

@@ -459,16 +459,6 @@ def test_delete_endpoint_with_config():
     sagemaker_session.delete_endpoint_config.assert_called_with('endpoint-config')
 
 
-def test_delete_non_existing_endpoint():
-    sagemaker_session = empty_sagemaker_session()
-    expected_error_message = 'The endpoint this config attached to does not exist.'
-    sagemaker_session.sagemaker_client.describe_endpoint = Mock(side_effect=ValueError(expected_error_message))
-    predictor = RealTimePredictor(ENDPOINT, sagemaker_session=sagemaker_session)
-
-    with pytest.raises(ValueError, match=expected_error_message):
-        predictor.delete_endpoint()
-
-
 def test_delete_endpoint_only():
     sagemaker_session = empty_sagemaker_session()
     predictor = RealTimePredictor(ENDPOINT, sagemaker_session=sagemaker_session)