Modify timeout class, README, CHANGELOG and model none check in session and transformer.

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: Make predictor to delete endpoint configuration by default when calling ``delete_endpoint()``
+* feature: Support for predictor class 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

@@ -280,7 +280,7 @@ We can take the example in  `Using Estimators <#using-estimators>`__ , and use e
     # Serializes data and makes a prediction request to the local endpoint
     response = mxnet_predictor.predict(data)
 
-    # Tears down the endpoint and endpoint configuration
+    # Tears down the endpoint container and deletes the corresponding endpoint configuration
     mxnet_predictor.delete_endpoint()
 
 
@@ -303,7 +303,7 @@ Here is an end-to-end example:
     data = numpy.zeros(shape=(1, 1, 28, 28))
     predictor.predict(data)
 
-    # Tear down the endpoint and endpoint configuration
+    # Tear down the endpoint container and delete the corresponding endpoint configuration
     predictor.delete_endpoint()
 
 

src/sagemaker/predictor.py

@@ -113,12 +113,11 @@ def _delete_endpoint_config(self):
         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
-           to it if delete_endpoint_config is True.
+        """Delete the Amazon SageMaker endpoint and endpoint configuration backing this predictor.
 
         Args:
-            delete_endpoint_config (bool): Flag to indicate whether to delete endpoint configuration together with
-                endpoint. If False, only endpoint will be deleted. Default: True.
+            delete_endpoint_config (bool): Flag to indicate whether to delete the corresponding SageMaker endpoint
+                configuration tied to the endpoint. If False, only the endpoint will be deleted. (default: True)
 
         """
         if delete_endpoint_config:

src/sagemaker/session.py

@@ -795,15 +795,9 @@ def delete_model(self, model_name):
         Args:
             model_name (str): Name of the Amazon SageMaker model to delete.
 
-        Raises: ValueError if model is not deployed yet.
-
         """
-        try:
-            self.sagemaker_client.describe_model(ModelName=model_name)
-            LOGGER.info('Deleting model with name: {}'.format(model_name))
-            self.sagemaker_client.delete_model(ModelName=model_name)
-        except Exception:
-            raise ValueError('The Sagemaker model must be deployed first before attempting to delete.')
+        LOGGER.info('Deleting model with name: {}'.format(model_name))
+        self.sagemaker_client.delete_model(ModelName=model_name)
 
     def wait_for_job(self, job, poll=5):
         """Wait for an Amazon SageMaker training job to complete.

src/sagemaker/transformer.py

@@ -52,8 +52,6 @@ def __init__(self, model_name, instance_count, instance_type, strategy=None, ass
                 using the default AWS configuration chain.
             volume_kms_key (str): Optional. KMS key ID for encrypting the volume attached to the ML
                 compute instance (default: None).
-            model (sagemaker.model.Model): A SageMaker Model object, used for SageMaker Model interactions
-                (default: None). If not specified, model object related activities will fail.
         """
         self.model_name = model_name
         self.strategy = strategy
@@ -115,7 +113,7 @@ def transform(self, data, data_type='S3Prefix', content_type=None, compression_t
                                                             split_type)
 
     def delete_model(self):
-        """Delete a SageMaker Model.
+        """Delete the corresponding SageMaker model for this Transformer.
 
         """
         self.sagemaker_session.delete_model(self.model_name)

tests/integ/timeout.py

@@ -75,9 +75,9 @@ def timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, second
                     sagemaker_session.delete_endpoint(endpoint_name)
                     LOGGER.info('deleted endpoint {}'.format(endpoint_name))
 
-                    _show_endpoint_logs(endpoint_name, sagemaker_session)
+                    _show_logs(endpoint_name, 'Endpoints', sagemaker_session)
                     if no_errors:
-                        _cleanup_endpoint_logs(endpoint_name, sagemaker_session)
+                        _cleanup_logs(endpoint_name, 'Endpoints', sagemaker_session)
                     return
                 except ClientError as ce:
                     if ce.response['Error']['Code'] == 'ValidationException':
@@ -102,41 +102,19 @@ def timeout_and_delete_model_with_transformer(transformer, sagemaker_session, se
                 try:
                     transformer.delete_model()
                     LOGGER.info('deleted SageMaker model {}'.format(transformer.model_name))
+
+                    _show_logs(transformer.model_name, 'Models', sagemaker_session)
                     if no_errors:
-                        _cleanup_model_logs(transformer.model_name, sagemaker_session)
+                        _cleanup_logs(transformer.model_name, 'Models', sagemaker_session)
                         return
                 except ClientError as ce:
                     if ce.response['Error']['Code'] == 'ValidationException':
                         pass
                 sleep(10)
 
 
-def _show_model_logs(model_name, sagemaker_session):
-    log_group = '/aws/sagemaker/Models/{}'.format(model_name)
-    try:
-        LOGGER.info('cloudwatch logs for log group {}'.format(log_group))
-        logs = AWSLogs(log_group_name=log_group, log_stream_name='ALL', start='1d',
-                       aws_region=sagemaker_session.boto_session.region_name)
-        logs.list_logs()
-    except Exception:
-        LOGGER.exception('Failure occurred while listing cloudwatch log group %s. Swallowing exception but printing '
-                         'stacktrace for debugging.', log_group)
-
-
-def _cleanup_model_logs(model_name, sagemaker_session):
-    log_group = '/aws/sagemaker/Models/{}'.format(model_name)
-    try:
-        LOGGER.info('deleting cloudwatch log group {}:'.format(log_group))
-        cwl_client = sagemaker_session.boto_session.client('logs')
-        cwl_client.delete_log_group(logGroupName=log_group)
-        LOGGER.info('deleted cloudwatch log group: {}'.format(log_group))
-    except Exception:
-        LOGGER.exception('Failure occurred while cleaning up cloudwatch log group %s. '
-                         'Swallowing exception but printing stacktrace for debugging.', log_group)
-
-
-def _show_endpoint_logs(endpoint_name, sagemaker_session):
-    log_group = '/aws/sagemaker/Endpoints/{}'.format(endpoint_name)
+def _show_logs(resource_name, resource_type, sagemaker_session):
+    log_group = '/aws/sagemaker/{}/{}'.format(resource_type, resource_name)
     try:
         # print out logs before deletion for debuggability
         LOGGER.info('cloudwatch logs for log group {}:'.format(log_group))
@@ -148,8 +126,8 @@ def _show_endpoint_logs(endpoint_name, sagemaker_session):
                          'stacktrace for debugging.', log_group)
 
 
-def _cleanup_endpoint_logs(endpoint_name, sagemaker_session):
-    log_group = '/aws/sagemaker/Endpoints/{}'.format(endpoint_name)
+def _cleanup_logs(resource_name, resource_type, sagemaker_session):
+    log_group = '/aws/sagemaker/{}/{}'.format(resource_type, resource_name)
     try:
         # print out logs before deletion for debuggability
         LOGGER.info('deleting cloudwatch log group {}:'.format(log_group))

tests/unit/test_session.py

@@ -156,22 +156,11 @@ def test_delete_model(boto_session):
     sess = Session(boto_session)
 
     model_name = 'my_model'
-    boto_session.client().describe_model = Mock(return_value={'ModelName': model_name})
     sess.delete_model(model_name)
 
     boto_session.client().delete_model.assert_called_with(ModelName=model_name)
 
 
-def test_delete_non_existing_model(boto_session):
-    sess = Session(boto_session)
-
-    expected_error_message = 'The Sagemaker model must be deployed first before attempting to delete.'
-    boto_session.client().describe_model = Mock(side_effect=ValueError(expected_error_message))
-
-    with pytest.raises(ValueError, match=expected_error_message):
-        sess.delete_model('non_existing_model')
-
-
 def test_user_agent_injected(boto_session):
     assert 'AWS-SageMaker-Python-SDK' not in boto_session.client('sagemaker')._client_config.user_agent
 

tests/unit/test_transformer.py

@@ -60,8 +60,7 @@ def transformer(sagemaker_session):
 
 
 def test_delete_model(sagemaker_session):
-    transformer = Transformer(MODEL_NAME, INSTANCE_COUNT, INSTANCE_TYPE, output_path=OUTPUT_PATH,
-                              sagemaker_session=sagemaker_session, volume_kms_key=KMS_KEY_ID)
+    transformer = Transformer(MODEL_NAME, INSTANCE_COUNT, INSTANCE_TYPE, sagemaker_session=sagemaker_session)
     transformer.delete_model()
     sagemaker_session.delete_model.assert_called_with(MODEL_NAME)