Update README docstring and predictor delete model error handling.

Author: Chuyang Deng

README.rst

@@ -192,7 +192,7 @@ Here is an end to end example of how to use a SageMaker Estimator:
     # Tears down the SageMaker endpoint and endpoint configuration
     mxnet_predictor.delete_endpoint()
 
-    # Deletes SageMaker model
+    # Deletes the SageMaker model
     mxnet_predictor.delete_model()
 
 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.
@@ -232,7 +232,7 @@ For more `information <https://boto3.amazonaws.com/v1/documentation/api/latest/r
     # Tears down the SageMaker endpoint and endpoint configuration
     mxnet_predictor.delete_endpoint()
 
-    # Deletes SageMaker model
+    # Deletes the SageMaker model
     mxnet_predictor.delete_model()
 
 Training Metrics

src/sagemaker/predictor.py

@@ -132,8 +132,11 @@ def delete_model(self):
         """Deletes the Amazon SageMaker models backing this predictor.
 
         """
-        for model_name in self._model_names:
-            self.sagemaker_session.delete_model(model_name)
+        try:
+            for model_name in self._model_names:
+                self.sagemaker_session.delete_model(model_name)
+        except Exception:
+            raise Exception('One or more models cannot be deleted, the deletion is incomplete.')
 
     def _get_endpoint_config_name(self):
         endpoint_desc = self.sagemaker_session.sagemaker_client.describe_endpoint(EndpointName=self.endpoint)

tests/unit/test_predictor.py

@@ -499,3 +499,15 @@ def test_delete_model():
     expected_call_args_list = [call('model-1'), call('model-2')]
     assert sagemaker_session.delete_model.call_count == expected_call_count
     assert sagemaker_session.delete_model.call_args_list == expected_call_args_list
+
+
+def test_delete_model_fail():
+    sagemaker_session = empty_sagemaker_session()
+    sagemaker_session.sagemaker_client.delete_model = Mock(side_effect='Could not find model.')
+    expected_error_message = 'One or more models cannot be deleted, the deletion is incomplete.'
+
+    predictor = RealTimePredictor(ENDPOINT, sagemaker_session=sagemaker_session)
+
+    with pytest.raises(Exception) as exception:
+        predictor.delete_model()
+        assert str(exception.val) == expected_error_message