Add new APIs to clean up resources from predictor and transformer.

CHANGELOG.rst

@@ -8,6 +8,9 @@ CHANGELOG
 * doc-fix: update information about saving models in the MXNet README
 * doc-fix: change ReadTheDocs links from latest to stable
 * doc-fix: add ``transform_fn`` information and fix ``input_fn`` signature in the MXNet README
+* feature: Support for ``Predictor`` to delete endpoint configuration by default when calling ``delete_endpoint()``
+* feature: Support for ``model`` to delete SageMaker model
+* feature: Support for ``Transformer`` to delete SageMaker model
 
 1.18.2
 ======

README.rst

@@ -189,10 +189,16 @@ Here is an end to end example of how to use a SageMaker Estimator:
     # Serializes data and makes a prediction request to the SageMaker endpoint
     response = mxnet_predictor.predict(data)
 
-    # Tears down the SageMaker endpoint
-    mxnet_estimator.delete_endpoint()
+    # Tears down the SageMaker endpoint and endpoint configuration
+    mxnet_predictor.delete_endpoint()
 
 
+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 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.
 This can be done by specifying the existing endpoint name for the ``endpoint_name`` parameter along with the ``update_endpoint`` parameter as ``True`` within your ``deploy()`` call.
 For more `information <https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.update_endpoint>`__.
@@ -220,8 +226,8 @@ For more `information <https://boto3.amazonaws.com/v1/documentation/api/latest/r
     # Serializes data and makes a prediction request to the SageMaker endpoint
     response = mxnet_predictor.predict(data)
 
-    # Tears down the SageMaker endpoint
-    mxnet_estimator.delete_endpoint()
+    # Tears down the SageMaker endpoint and endpoint configuration
+    mxnet_predictor.delete_endpoint()
 
 Training Metrics
 ~~~~~~~~~~~~~~~~
@@ -274,8 +280,8 @@ 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 container
-    mxnet_estimator.delete_endpoint()
+    # Tears down the endpoint container and deletes the corresponding endpoint configuration
+    mxnet_predictor.delete_endpoint()
 
 
 If you have an existing model and want to deploy it locally, don't specify a sagemaker_session argument to the ``MXNetModel`` constructor.
@@ -297,7 +303,7 @@ Here is an end-to-end example:
     data = numpy.zeros(shape=(1, 1, 28, 28))
     predictor.predict(data)
 
-    # Tear down the endpoint container
+    # Tear down the endpoint container and delete the corresponding endpoint configuration
     predictor.delete_endpoint()
 
 
@@ -322,6 +328,9 @@ Here is an end-to-end example:
     transformer.transform('s3://my/transform/data, content_type='text/csv', split_type='Line')
     transformer.wait()
 
+    # Deletes the SageMaker model
+    transformer.delete_model()
+
 
 For detailed examples of running Docker in local mode, see:
 

src/sagemaker/local/local_session.py

@@ -150,6 +150,14 @@ def delete_endpoint(self, EndpointName):
         if EndpointName in LocalSagemakerClient._endpoints:
             LocalSagemakerClient._endpoints[EndpointName].stop()
 
+    def delete_endpoint_config(self, EndpointConfigName):
+        if EndpointConfigName in LocalSagemakerClient._endpoint_configs:
+            del LocalSagemakerClient._endpoint_configs[EndpointConfigName]
+
+    def delete_model(self, ModelName):
+        if ModelName in LocalSagemakerClient._models:
+            del LocalSagemakerClient._models[ModelName]
+
 
 class LocalSagemakerRuntimeClient(object):
     """A SageMaker Runtime client that calls a local endpoint only.

src/sagemaker/model.py

@@ -302,6 +302,17 @@ def transformer(self, instance_count, instance_type, strategy=None, assemble_wit
                            env=env, tags=tags, base_transform_job_name=self.name,
                            volume_kms_key=volume_kms_key, sagemaker_session=self.sagemaker_session)
 
+    def delete_model(self):
+        """Delete an Amazon SageMaker Model.
+
+        Raises:
+            ValueError: if the model is not created yet.
+
+        """
+        if self.name is None:
+            raise ValueError('The SageMaker model must be created first before attempting to delete.')
+        self.sagemaker_session.delete_model(self.name)
+
 
 SCRIPT_PARAM_NAME = 'sagemaker_program'
 DIR_PARAM_NAME = 'sagemaker_submit_directory'

src/sagemaker/predictor.py

@@ -105,9 +105,25 @@ def _create_request_args(self, data, initial_args=None):
         args['Body'] = data
         return args
 
-    def delete_endpoint(self):
-        """Delete the Amazon SageMaker endpoint backing this predictor.
+    def _delete_endpoint_config(self):
+        """Delete the Amazon SageMaker endpoint configuration
+
         """
+        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 and endpoint configuration backing this predictor.
+
+        Args:
+            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:
+            self._delete_endpoint_config()
+
         self.sagemaker_session.delete_endpoint(self.endpoint)
 
 

src/sagemaker/session.py

@@ -780,6 +780,25 @@ def delete_endpoint(self, endpoint_name):
         LOGGER.info('Deleting endpoint with name: {}'.format(endpoint_name))
         self.sagemaker_client.delete_endpoint(EndpointName=endpoint_name)
 
+    def delete_endpoint_config(self, endpoint_config_name):
+        """Delete an Amazon SageMaker endpoint configuration.
+
+        Args:
+            endpoint_config_name (str): Name of the Amazon SageMaker endpoint configuration to delete.
+        """
+        LOGGER.info('Deleting endpoint configuration with name: {}'.format(endpoint_config_name))
+        self.sagemaker_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)
+
+    def delete_model(self, model_name):
+        """Delete an Amazon SageMaker Model.
+
+        Args:
+            model_name (str): Name of the Amazon SageMaker model 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

@@ -112,6 +112,12 @@ def transform(self, data, data_type='S3Prefix', content_type=None, compression_t
         self.latest_transform_job = _TransformJob.start_new(self, data, data_type, content_type, compression_type,
                                                             split_type)
 
+    def delete_model(self):
+        """Delete the corresponding SageMaker model for this Transformer.
+
+        """
+        self.sagemaker_session.delete_model(self.model_name)
+
     def _retrieve_image_name(self):
         model_desc = self.sagemaker_session.sagemaker_client.describe_model(ModelName=self.model_name)
         return model_desc['PrimaryContainer']['Image']

tests/integ/test_transformer.py

@@ -24,7 +24,7 @@
 from sagemaker.transformer import Transformer
 from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES, TRANSFORM_DEFAULT_TIMEOUT_MINUTES
 from tests.integ.kms_utils import get_or_create_kms_key
-from tests.integ.timeout import timeout
+from tests.integ.timeout import timeout, timeout_and_delete_model_with_transformer
 from tests.integ.vpc_test_utils import get_or_create_vpc_resources
 
 
@@ -56,7 +56,8 @@ def test_transform_mxnet(sagemaker_session, mxnet_full_version):
     kms_key_arn = get_or_create_kms_key(kms_client, account_id)
 
     transformer = _create_transformer_and_transform_job(mx, transform_input, kms_key_arn)
-    with timeout(minutes=TRANSFORM_DEFAULT_TIMEOUT_MINUTES):
+    with timeout_and_delete_model_with_transformer(transformer, sagemaker_session,
+                                                   minutes=TRANSFORM_DEFAULT_TIMEOUT_MINUTES):
         transformer.wait()
 
     job_desc = transformer.sagemaker_session.sagemaker_client.describe_transform_job(
@@ -100,7 +101,8 @@ def test_attach_transform_kmeans(sagemaker_session):
 
     attached_transformer = Transformer.attach(transformer.latest_transform_job.name,
                                               sagemaker_session=sagemaker_session)
-    with timeout(minutes=TRANSFORM_DEFAULT_TIMEOUT_MINUTES):
+    with timeout_and_delete_model_with_transformer(transformer, sagemaker_session,
+                                                   minutes=TRANSFORM_DEFAULT_TIMEOUT_MINUTES):
         attached_transformer.wait()
 
 
@@ -135,12 +137,12 @@ def test_transform_mxnet_vpc(sagemaker_session, mxnet_full_version):
                                                        key_prefix=transform_input_key_prefix)
 
     transformer = _create_transformer_and_transform_job(mx, transform_input)
-    with timeout(minutes=TRANSFORM_DEFAULT_TIMEOUT_MINUTES):
+    with timeout_and_delete_model_with_transformer(transformer, sagemaker_session,
+                                                   minutes=TRANSFORM_DEFAULT_TIMEOUT_MINUTES):
         transformer.wait()
-
-    model_desc = sagemaker_session.sagemaker_client.describe_model(ModelName=transformer.model_name)
-    assert set(subnet_ids) == set(model_desc['VpcConfig']['Subnets'])
-    assert [security_group_id] == model_desc['VpcConfig']['SecurityGroupIds']
+        model_desc = sagemaker_session.sagemaker_client.describe_model(ModelName=transformer.model_name)
+        assert set(subnet_ids) == set(model_desc['VpcConfig']['Subnets'])
+        assert [security_group_id] == model_desc['VpcConfig']['SecurityGroupIds']
 
 
 def _create_transformer_and_transform_job(estimator, transform_input, volume_kms_key=None):

tests/integ/timeout.py

@@ -32,13 +32,9 @@ def timeout(seconds=0, minutes=0, hours=0):
     """
     Add a signal-based timeout to any block of code.
     If multiple time units are specified, they will be added together to determine time limit.
-
     Usage:
-
     with timeout(seconds=5):
         my_slow_function(...)
-
-
     Args:
         - seconds: The time limit, in seconds.
         - minutes: The time limit, in minutes.
@@ -75,9 +71,9 @@ 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':
@@ -87,8 +83,34 @@ def timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, second
                 sleep(10)
 
 
-def _show_endpoint_logs(endpoint_name, sagemaker_session):
-    log_group = '/aws/sagemaker/Endpoints/{}'.format(endpoint_name)
+@contextmanager
+def timeout_and_delete_model_with_transformer(transformer, sagemaker_session, seconds=0, minutes=0, hours=0):
+    with timeout(seconds=seconds, minutes=minutes, hours=hours) as t:
+        no_errors = False
+        try:
+            yield [t]
+            no_errors = True
+        finally:
+            attempts = 3
+
+            while attempts > 0:
+                attempts -= 1
+                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_logs(transformer.model_name, 'Models', sagemaker_session)
+                        return
+                except ClientError as ce:
+                    if ce.response['Error']['Code'] == 'ValidationException':
+                        pass
+                sleep(10)
+
+
+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))
@@ -100,8 +122,8 @@ 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_local_session.py

@@ -27,6 +27,12 @@
 BAD_RESPONSE = urllib3.HTTPResponse()
 BAD_RESPONSE.status = 502
 
+ENDPOINT_CONFIG_NAME = 'test-endpoint-config'
+PRODUCTION_VARIANTS = [{'InstanceType': 'ml.c4.99xlarge', 'InitialInstanceCount': 10}]
+
+MODEL_NAME = 'test-model'
+PRIMARY_CONTAINER = {'ModelDataUrl': '/some/model/path', 'Environment': {'env1': 1, 'env2': 'b'}}
+
 
 @patch('sagemaker.local.image._SageMakerContainer.train', return_value="/some/path/to/model")
 @patch('sagemaker.local.local_session.LocalSession')
@@ -148,25 +154,32 @@ def test_create_training_job_not_fully_replicated(train, LocalSession):
 @patch('sagemaker.local.local_session.LocalSession')
 def test_create_model(LocalSession):
     local_sagemaker_client = sagemaker.local.local_session.LocalSagemakerClient()
-    model_name = 'my-model'
-    primary_container = {'ModelDataUrl': '/some/model/path', 'Environment': {'env1': 1, 'env2': 'b'}}
 
-    local_sagemaker_client.create_model(model_name, primary_container)
+    local_sagemaker_client.create_model(MODEL_NAME, PRIMARY_CONTAINER)
+
+    assert MODEL_NAME in sagemaker.local.local_session.LocalSagemakerClient._models
+
+
+@patch('sagemaker.local.local_session.LocalSession')
+def test_delete_model(LocalSession):
+    local_sagemaker_client = sagemaker.local.local_session.LocalSagemakerClient()
+
+    local_sagemaker_client.create_model(MODEL_NAME, PRIMARY_CONTAINER)
+    assert MODEL_NAME in sagemaker.local.local_session.LocalSagemakerClient._models
 
-    assert 'my-model' in sagemaker.local.local_session.LocalSagemakerClient._models
+    local_sagemaker_client.delete_model(MODEL_NAME)
+    assert MODEL_NAME not in sagemaker.local.local_session.LocalSagemakerClient._models
 
 
 @patch('sagemaker.local.local_session.LocalSession')
 def test_describe_model(LocalSession):
     local_sagemaker_client = sagemaker.local.local_session.LocalSagemakerClient()
-    model_name = 'test-model'
-    primary_container = {'ModelDataUrl': '/some/model/path', 'Environment': {'env1': 1, 'env2': 'b'}}
 
     with pytest.raises(ClientError):
         local_sagemaker_client.describe_model('model-does-not-exist')
 
-    local_sagemaker_client.create_model(model_name, primary_container)
-    response = local_sagemaker_client.describe_model('test-model')
+    local_sagemaker_client.create_model(MODEL_NAME, PRIMARY_CONTAINER)
+    response = local_sagemaker_client.describe_model(MODEL_NAME)
 
     assert response['ModelName'] == 'test-model'
     assert response['PrimaryContainer']['ModelDataUrl'] == '/some/model/path'
@@ -212,10 +225,20 @@ def test_describe_endpoint_config(LocalSession):
 @patch('sagemaker.local.local_session.LocalSession')
 def test_create_endpoint_config(LocalSession):
     local_sagemaker_client = sagemaker.local.local_session.LocalSagemakerClient()
-    production_variants = [{'InstanceType': 'ml.c4.99xlarge', 'InitialInstanceCount': 10}]
-    local_sagemaker_client.create_endpoint_config('my-endpoint-config', production_variants)
+    local_sagemaker_client.create_endpoint_config(ENDPOINT_CONFIG_NAME, PRODUCTION_VARIANTS)
+
+    assert ENDPOINT_CONFIG_NAME in sagemaker.local.local_session.LocalSagemakerClient._endpoint_configs
+
+
+@patch('sagemaker.local.local_session.LocalSession')
+def test_delete_endpoint_config(LocalSession):
+    local_sagemaker_client = sagemaker.local.local_session.LocalSagemakerClient()
+
+    local_sagemaker_client.create_endpoint_config(ENDPOINT_CONFIG_NAME, PRODUCTION_VARIANTS)
+    assert ENDPOINT_CONFIG_NAME in sagemaker.local.local_session.LocalSagemakerClient._endpoint_configs
 
-    assert 'my-endpoint-config' in sagemaker.local.local_session.LocalSagemakerClient._endpoint_configs
+    local_sagemaker_client.delete_endpoint_config(ENDPOINT_CONFIG_NAME)
+    assert ENDPOINT_CONFIG_NAME not in sagemaker.local.local_session.LocalSagemakerClient._endpoint_configs
 
 
 @patch('sagemaker.local.image._SageMakerContainer.serve')
@@ -316,7 +339,7 @@ def test_update_endpoint(LocalSession):
     endpoint_name = 'my-endpoint'
     endpoint_config = 'my-endpoint-config'
     expected_error_message = 'Update endpoint name is not supported in local session.'
-    with pytest.raises(NotImplementedError, message=expected_error_message):
+    with pytest.raises(NotImplementedError, match=expected_error_message):
         local_sagemaker_client.update_endpoint(endpoint_name, endpoint_config)