feature: add wait argument to estimator deploy

src/sagemaker/estimator.py

@@ -331,7 +331,7 @@ def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='m
         return estimator
 
     def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
-               use_compiled_model=False, update_endpoint=False, **kwargs):
+               use_compiled_model=False, update_endpoint=False, wait=True, **kwargs):
         """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
 
         More information:
@@ -355,6 +355,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
                     >>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
                     For more information about tags, see https://boto3.amazonaws.com/v1/documentation\
                     /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
+            wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 
             **kwargs: Passed to invocation of ``create_model()``. Implementations may customize
                 ``create_model()`` to accept ``**kwargs`` to customize model creation during deploy.
@@ -381,7 +382,8 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
             accelerator_type=accelerator_type,
             endpoint_name=endpoint_name,
             update_endpoint=update_endpoint,
-            tags=self.tags)
+            tags=self.tags,
+            wait=wait)
 
     @property
     def model_data(self):

src/sagemaker/model.py

@@ -228,7 +228,7 @@ def compile(self, target_instance_family, input_shape, output_path, role,
         return self
 
     def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
-               update_endpoint=False, tags=None, kms_key=None):
+               update_endpoint=False, tags=None, kms_key=None, wait=True):
         """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.
 
         Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
@@ -256,6 +256,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
             tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
             kms_key (str): The ARN of the KMS key that is used to encrypt the data on the
                 storage volume attached to the instance hosting the endpoint.
+            wait (bool): Whether the call should wait until the deployment of this model completes (default: True).
 
         Returns:
             callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
@@ -296,7 +297,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
             self.sagemaker_session.update_endpoint(self.endpoint_name, endpoint_config_name)
         else:
             self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant],
-                                                                     tags, kms_key)
+                                                                     tags, kms_key, wait)
 
         if self.predictor_cls:
             return self.predictor_cls(self.endpoint_name, self.sagemaker_session)

src/sagemaker/pipeline.py

@@ -67,7 +67,7 @@ def pipeline_container_def(self, instance_type):
 
         return sagemaker.pipeline_container_def(self.models, instance_type)
 
-    def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None):
+    def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None, wait=True):
         """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.
 
         Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
@@ -86,6 +86,7 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags
             endpoint_name (str): The name of the endpoint to create (default: None).
                 If not specified, a unique endpoint name will be created.
             tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
+            wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 
         Returns:
             callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
@@ -101,7 +102,8 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags
 
         production_variant = sagemaker.production_variant(self.name, instance_type, initial_instance_count)
         self.endpoint_name = endpoint_name or self.name
-        self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags)
+        self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags,
+                                                                 wait=wait)
         if self.predictor_cls:
             return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
 

src/sagemaker/tuner.py

@@ -326,7 +326,8 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim
 
         return tuner
 
-    def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, **kwargs):
+    def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, wait=True,
+               **kwargs):
         """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
         ``sagemaker.RealTimePredictor`` object.
 
@@ -342,6 +343,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
                 For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
             endpoint_name (str): Name to use for creating an Amazon SageMaker endpoint. If not specified,
                 the name of the training job is used.
+            wait (bool): Whether the call should wait until the deployment of model completes (default: True).
             **kwargs: Other arguments needed for deployment. Please refer to the ``create_model()`` method of
                 the associated estimator to see what other arguments are needed.
 
@@ -354,7 +356,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
                                                sagemaker_session=self.estimator.sagemaker_session)
         return best_estimator.deploy(initial_instance_count, instance_type,
                                      accelerator_type=accelerator_type,
-                                     endpoint_name=endpoint_name, **kwargs)
+                                     endpoint_name=endpoint_name, wait=wait, **kwargs)
 
     def stop_tuning_job(self):
         """Stop latest running hyperparameter tuning job.

tests/unit/test_estimator.py

@@ -922,7 +922,8 @@ def test_fit_deploy_keep_tags(sagemaker_session):
     sagemaker_session.endpoint_from_production_variants.assert_called_with(job_name,
                                                                            variant,
                                                                            tags,
-                                                                           None)
+                                                                           None,
+                                                                           True)
 
     sagemaker_session.create_model.assert_called_with(
         ANY,

tests/unit/test_model.py

@@ -166,7 +166,8 @@ def test_deploy(sagemaker_session, tmpdir):
           'InitialInstanceCount': 1,
           'VariantName': 'AllTraffic'}],
         None,
-        None)
+        None,
+        True)
 
 
 @patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
@@ -182,7 +183,8 @@ def test_deploy_endpoint_name(sagemaker_session, tmpdir):
           'InitialInstanceCount': 55,
           'VariantName': 'AllTraffic'}],
         None,
-        None)
+        None,
+        True)
 
 
 @patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
@@ -199,7 +201,8 @@ def test_deploy_tags(sagemaker_session, tmpdir):
           'InitialInstanceCount': 1,
           'VariantName': 'AllTraffic'}],
         tags,
-        None)
+        None,
+        True)
 
 
 @patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
@@ -217,7 +220,8 @@ def test_deploy_accelerator_type(tfo, time, sagemaker_session):
           'VariantName': 'AllTraffic',
           'AcceleratorType': ACCELERATOR_TYPE}],
         None,
-        None)
+        None,
+        True)
 
 
 @patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
@@ -235,7 +239,8 @@ def test_deploy_kms_key(tfo, time, sagemaker_session):
           'InitialInstanceCount': 1,
           'VariantName': 'AllTraffic'}],
         None,
-        key)
+        key,
+        True)
 
 
 @patch('sagemaker.session.Session')

tests/unit/test_pipeline_model.py

@@ -102,7 +102,8 @@ def test_deploy(tfo, time, sagemaker_session):
           'InstanceType': INSTANCE_TYPE,
           'InitialInstanceCount': 1,
           'VariantName': 'AllTraffic'}],
-        None)
+        None,
+        wait=True)
 
 
 @patch('tarfile.open')
@@ -119,7 +120,8 @@ def test_deploy_endpoint_name(tfo, time, sagemaker_session):
           'InstanceType': INSTANCE_TYPE,
           'InitialInstanceCount': 1,
           'VariantName': 'AllTraffic'}],
-        None)
+        None,
+        wait=True)
 
 
 @patch('tarfile.open')
@@ -178,7 +180,8 @@ def test_deploy_tags(tfo, time, sagemaker_session):
           'InstanceType': INSTANCE_TYPE,
           'InitialInstanceCount': 1,
           'VariantName': 'AllTraffic'}],
-        tags)
+        tags,
+        wait=True)
 
 
 def test_delete_model_without_deploy(sagemaker_session):