Code Formatting

Author: gwang111

src/sagemaker/inference_recommender/inference_recommender_mixin.py

@@ -34,6 +34,7 @@
 
 RIGHT_SIZE_TAG = "PythonSDK-RightSize"
 
+
 class Phase:
     """Used to store phases of a traffic pattern to perform endpoint load testing.
 
@@ -615,9 +616,6 @@ def _search_recommendation(self, recommendation_list, inference_recommendation_i
         )
 
     def _add_client_type_tag(self, tags, client_type):
-        client_type_tag = {
-            "Key": "ClientType",
-            "Value": client_type
-        }
+        client_type_tag = {"Key": "ClientType", "Value": client_type}
         tags = tags.append(client_type_tag) if tags else [client_type_tag]
         return tags

src/sagemaker/model.py

@@ -1190,6 +1190,8 @@ def deploy(
             inference_recommendation_id (str): The recommendation id which specifies the
                 recommendation you picked from inference recommendation job results and
                 would like to deploy the model and endpoint with recommended parameters.
+                This can also be a recommendation id returned from ``DescribeModel`` contained in
+                a list of ``RealtimeInferenceRecommendations`` within ``DeploymentRecommendation``
             explainer_config (sagemaker.explainer.ExplainerConfig): Specifies online explainability
                 configuration for use with Amazon SageMaker Clarify. Default: None.
         Raises:

tests/integ/test_inference_recommender.py

@@ -301,7 +301,7 @@ def created_base_model(sagemaker_session, cpu_instance_type):
     model.create(instance_type=cpu_instance_type)
 
     return model
-            
+
 
 @pytest.mark.slow_test
 def test_default_right_size_and_deploy_registered_model_sklearn(
@@ -459,17 +459,23 @@ def test_deploy_inference_recommendation_id_with_registered_model_sklearn(
 def test_deploy_deployment_recommendation_id_with_model(created_base_model, sagemaker_session):
     with timeout(minutes=20):
         try:
-            deployment_recommendation = poll_for_deployment_recommendation(created_base_model, sagemaker_session)
+            deployment_recommendation = poll_for_deployment_recommendation(
+                created_base_model, sagemaker_session
+            )
 
             assert deployment_recommendation != None
 
-            real_time_recommendations = deployment_recommendation.get("RealTimeInferenceRecommendations")
-            recommendation_id = real_time_recommendations[0].get('RecommendationId')
-            
+            real_time_recommendations = deployment_recommendation.get(
+                "RealTimeInferenceRecommendations"
+            )
+            recommendation_id = real_time_recommendations[0].get("RecommendationId")
+
             endpoint_name = unique_name_from_base("test-rec-id-deployment-default-sklearn")
             created_base_model.predictor_cls = SKLearnPredictor
             predictor = created_base_model.deploy(
-                inference_recommendation_id=recommendation_id, initial_instance_count=1, endpoint_name=endpoint_name
+                inference_recommendation_id=recommendation_id,
+                initial_instance_count=1,
+                endpoint_name=endpoint_name,
             )
 
             payload = pd.read_csv(IR_SKLEARN_DATA, header=None)
@@ -479,15 +485,17 @@ def test_deploy_deployment_recommendation_id_with_model(created_base_model, sage
             assert 26 == len(inference)
         finally:
             predictor.delete_model()
-            predictor.delete_endpoint()            
+            predictor.delete_endpoint()
 
 
 def poll_for_deployment_recommendation(created_base_model, sagemaker_session):
     with timeout(minutes=1):
         try:
             completed = False
             while not completed:
-                describe_model_response = sagemaker_session.sagemaker_client.describe_model(ModelName=created_base_model.name)
+                describe_model_response = sagemaker_session.sagemaker_client.describe_model(
+                    ModelName=created_base_model.name
+                )
                 deployment_recommendation = describe_model_response.get("DeploymentRecommendation")
 
                 completed = (

tests/unit/sagemaker/model/test_deploy.py

@@ -633,7 +633,9 @@ def test_deploy_with_recommendation_id_with_model_pkg_arn(name_from_base, sagema
     )
     sagemaker_session.sagemaker_client.describe_model.return_value = None
 
-    model_package = ModelPackage(role=ROLE, model_data=IR_MODEL_DATA, model_package_arn=IR_MODEL_PACKAGE_VERSION_ARN)
+    model_package = ModelPackage(
+        role=ROLE, model_data=IR_MODEL_DATA, model_package_arn=IR_MODEL_PACKAGE_VERSION_ARN
+    )
 
     model_package.sagemaker_session = sagemaker_session
 
@@ -661,7 +663,6 @@ def mock_describe_model(ModelName):
         return DESCRIBE_MODEL_RESPONSE
 
 
-
 @patch("sagemaker.utils.name_from_base", return_value=MODEL_IMAGE)
 def test_deploy_with_recommendation_id_with_model_name(name_from_base, sagemaker_session):
     sagemaker_session.sagemaker_client.describe_inference_recommendations_job.return_value = (
@@ -690,7 +691,9 @@ def test_deploy_with_recommendation_id_with_model_name(name_from_base, sagemaker
 
 
 @patch("sagemaker.utils.name_from_base", return_value=IR_COMPILATION_IMAGE)
-def test_deploy_with_recommendation_id_with_model_pkg_arn_and_compilation(name_from_base, sagemaker_session):
+def test_deploy_with_recommendation_id_with_model_pkg_arn_and_compilation(
+    name_from_base, sagemaker_session
+):
     sagemaker_session.sagemaker_client.describe_inference_recommendations_job.return_value = (
         create_inference_recommendations_job_default_with_model_package_arn_and_compilation()
     )
@@ -699,7 +702,9 @@ def test_deploy_with_recommendation_id_with_model_pkg_arn_and_compilation(name_f
     )
     sagemaker_session.sagemaker_client.describe_model.return_value = None
 
-    model_package = ModelPackage(role=ROLE, model_data=MODEL_DATA, model_package_arn=IR_MODEL_PACKAGE_VERSION_ARN)
+    model_package = ModelPackage(
+        role=ROLE, model_data=MODEL_DATA, model_package_arn=IR_MODEL_PACKAGE_VERSION_ARN
+    )
 
     model_package.sagemaker_session = sagemaker_session
 
@@ -721,7 +726,9 @@ def test_deploy_with_recommendation_id_with_model_pkg_arn_and_compilation(name_f
 
 
 @patch("sagemaker.utils.name_from_base", return_value=MODEL_IMAGE)
-def test_deploy_with_recommendation_id_with_model_name_and_compilation(name_from_base, sagemaker_session):
+def test_deploy_with_recommendation_id_with_model_name_and_compilation(
+    name_from_base, sagemaker_session
+):
     def mock_describe_compilation_job(CompilationJobName):
         if CompilationJobName == IR_COMPILATION_JOB_NAME:
             return DESCRIBE_COMPILATION_JOB_RESPONSE