breaking: deprecate delete_endpoint() for estimators and HyperparameterTuner (#1651)

Author: laurenyu

doc/overview.rst

@@ -116,7 +116,7 @@ Here is an end to end example of how to use a SageMaker Estimator:
     # 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.
+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
 
@@ -180,10 +180,10 @@ Here is an example:
         train_input = algo.sagemaker_session.upload_data(path='/path/to/your/data')
 
         algo.fit({'training': train_input})
-        algo.deploy(1, 'ml.m4.xlarge')
+        predictor = algo.deploy(1, 'ml.m4.xlarge')
 
         # When you are done using your endpoint
-        algo.delete_endpoint()
+        predictor.delete_endpoint()
 
 Use Scripts Stored in a Git Repository
 --------------------------------------
@@ -609,7 +609,7 @@ Here is a basic example of how to use it:
     response = my_predictor.predict(my_prediction_data)
 
     # Tear down the SageMaker endpoint
-    my_tuner.delete_endpoint()
+    my_predictor.delete_endpoint()
 
 This example shows a hyperparameter tuning job that creates up to 100 training jobs, running up to 10 training jobs at a time.
 Each training job's learning rate is a value between 0.05 and 0.06, but this value will differ between training jobs.

src/sagemaker/estimator.py

@@ -820,15 +820,6 @@ class constructor
 
         return init_params
 
-    def delete_endpoint(self):
-        """Delete an Amazon SageMaker ``Endpoint``.
-
-        Raises:
-            botocore.exceptions.ClientError: If the endpoint does not exist.
-        """
-        self._ensure_latest_training_job(error_message="Endpoint was not created yet")
-        self.sagemaker_session.delete_endpoint(self.latest_training_job.name)
-
     def transformer(
         self,
         instance_count,

src/sagemaker/tuner.py

@@ -820,18 +820,6 @@ def _get_best_training_job(self):
                 )
             )
 
-    def delete_endpoint(self, endpoint_name=None):
-        """Delete an Amazon SageMaker endpoint.
-
-        If an endpoint name is not specified, this defaults to looking for an
-        endpoint that shares a name with the best training job for deletion.
-
-        Args:
-            endpoint_name (str): Name of the endpoint to delete
-        """
-        endpoint_name = endpoint_name or self.best_training_job()
-        self.sagemaker_session.delete_endpoint(endpoint_name)
-
     def _ensure_last_tuning_job(self):
         """Placeholder docstring"""
         if self.latest_tuning_job is None:

tests/integ/test_local_mode.py

@@ -151,7 +151,7 @@ def test_mxnet_local_mode(sagemaker_local_session, mxnet_full_version, mxnet_ful
             data = numpy.zeros(shape=(1, 1, 28, 28))
             predictor.predict(data)
         finally:
-            mx.delete_endpoint()
+            predictor.delete_endpoint()
 
 
 @pytest.mark.local_mode
@@ -209,7 +209,7 @@ def test_mxnet_local_data_local_script(mxnet_full_version, mxnet_full_py_version
             data = numpy.zeros(shape=(1, 1, 28, 28))
             predictor.predict(data)
         finally:
-            mx.delete_endpoint()
+            predictor.delete_endpoint()
 
 
 @pytest.mark.local_mode

tests/integ/test_source_dirs.py

@@ -51,4 +51,4 @@ def test_source_dirs(tmpdir, sagemaker_local_session):
             predict_response = predictor.predict([7])
             assert predict_response == [49]
         finally:
-            estimator.delete_endpoint()
+            predictor.delete_endpoint()

tests/unit/test_estimator.py

@@ -636,42 +636,6 @@ def test_start_new_wait_called(strftime, sagemaker_session):
     assert sagemaker_session.wait_for_job.assert_called_once
 
 
-def test_delete_endpoint(sagemaker_session):
-    t = DummyFramework(
-        entry_point=SCRIPT_PATH,
-        role="DummyRole",
-        sagemaker_session=sagemaker_session,
-        train_instance_count=INSTANCE_COUNT,
-        train_instance_type=INSTANCE_TYPE,
-        container_log_level=logging.INFO,
-    )
-
-    class tj(object):
-        @property
-        def name(self):
-            return "myjob"
-
-    t.latest_training_job = tj()
-
-    t.delete_endpoint()
-
-    sagemaker_session.delete_endpoint.assert_called_with("myjob")
-
-
-def test_delete_endpoint_without_endpoint(sagemaker_session):
-    t = DummyFramework(
-        entry_point=SCRIPT_PATH,
-        role="DummyRole",
-        sagemaker_session=sagemaker_session,
-        train_instance_count=INSTANCE_COUNT,
-        train_instance_type=INSTANCE_TYPE,
-    )
-
-    with pytest.raises(ValueError) as error:
-        t.delete_endpoint()
-    assert "Endpoint was not created yet" in str(error)
-
-
 def test_enable_cloudwatch_metrics(sagemaker_session):
     fw = DummyFramework(
         entry_point=SCRIPT_PATH,

tests/unit/test_tuner.py

@@ -917,18 +917,6 @@ def test_wait(tuner):
     tuner.estimator.sagemaker_session.wait_for_tuning_job.assert_called_once_with(JOB_NAME)
 
 
-def test_delete_endpoint(tuner):
-    tuner.latest_tuning_job = _TuningJob(tuner.estimator.sagemaker_session, JOB_NAME)
-
-    tuning_job_description = {"BestTrainingJob": {"TrainingJobName": JOB_NAME}}
-    tuner.estimator.sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job = Mock(
-        name="describe_hyper_parameter_tuning_job", return_value=tuning_job_description
-    )
-
-    tuner.delete_endpoint()
-    tuner.sagemaker_session.delete_endpoint.assert_called_with(JOB_NAME)
-
-
 def test_fit_no_inputs(tuner, sagemaker_session):
     script_path = os.path.join(DATA_DIR, "mxnet_mnist", "failure_script.py")
     tuner.estimator = MXNet(