Formatting and convention fix

Author: chiui0x18

src/sagemaker/automl/automl.py

@@ -402,9 +402,9 @@ def deploy(
             volume_size (int): The size, in GB, of the ML storage volume attached to individual
                 inference instance associated with the production variant. Currenly only Amazon EBS
                 gp2 storage volumes are supported.
-            model_data_download_timeout (int): The timeout value, in seconds, to download and extract
-                model data from Amazon S3 to the individual inference instance associated with this
-                production variant.
+            model_data_download_timeout (int): The timeout value, in seconds, to download and
+                extract model data from Amazon S3 to the individual inference instance associated
+                with this production variant.
             container_startup_health_check_timeout (int): The timeout value, in seconds, for your
                 inference container to pass health check by SageMaker Hosting. For more information
                 about health check see:
@@ -434,11 +434,11 @@ def deploy(
             deserializer=deserializer,
             endpoint_name=endpoint_name,
             kms_key=model_kms_key,
+            tags=tags,
+            wait=wait,
             volume_size=volume_size,
             model_data_download_timeout=model_data_download_timeout,
             container_startup_health_check_timeout=container_startup_health_check_timeout,
-            tags=tags,
-            wait=wait,
         )
 
     def _check_problem_type_and_job_objective(self, problem_type, job_objective):

src/sagemaker/estimator.py

@@ -1350,9 +1350,9 @@ def deploy(
             volume_size (int): The size, in GB, of the ML storage volume attached to individual
                 inference instance associated with the production variant. Currenly only Amazon EBS
                 gp2 storage volumes are supported.
-            model_data_download_timeout (int): The timeout value, in seconds, to download and extract
-                model data from Amazon S3 to the individual inference instance associated with this
-                production variant.
+            model_data_download_timeout (int): The timeout value, in seconds, to download and
+                extract model data from Amazon S3 to the individual inference instance associated
+                with this production variant.
             container_startup_health_check_timeout (int): The timeout value, in seconds, for your
                 inference container to pass health check by SageMaker Hosting. For more information
                 about health check see:

src/sagemaker/huggingface/model.py

@@ -275,9 +275,9 @@ def deploy(
             volume_size (int): The size, in GB, of the ML storage volume attached to individual
                 inference instance associated with the production variant. Currenly only Amazon EBS
                 gp2 storage volumes are supported.
-            model_data_download_timeout (int): The timeout value, in seconds, to download and extract
-                model data from Amazon S3 to the individual inference instance associated with this
-                production variant.
+            model_data_download_timeout (int): The timeout value, in seconds, to download and
+                extract model data from Amazon S3 to the individual inference instance associated
+                with this production variant.
             container_startup_health_check_timeout (int): The timeout value, in seconds, for your
                 inference container to pass health check by SageMaker Hosting. For more information
                 about health check see:

src/sagemaker/model.py

@@ -1098,9 +1098,9 @@ def deploy(
             volume_size (int): The size, in GB, of the ML storage volume attached to individual
                 inference instance associated with the production variant. Currenly only Amazon EBS
                 gp2 storage volumes are supported.
-            model_data_download_timeout (int): The timeout value, in seconds, to download and extract
-                model data from Amazon S3 to the individual inference instance associated with this
-                production variant.
+            model_data_download_timeout (int): The timeout value, in seconds, to download and
+                extract model data from Amazon S3 to the individual inference instance associated
+                with this production variant.
             container_startup_health_check_timeout (int): The timeout value, in seconds, for your
                 inference container to pass health check by SageMaker Hosting. For more information
                 about health check see:

src/sagemaker/pipeline.py

@@ -176,9 +176,9 @@ def deploy(
             volume_size (int): The size, in GB, of the ML storage volume attached to individual
                 inference instance associated with the production variant. Currenly only Amazon EBS
                 gp2 storage volumes are supported.
-            model_data_download_timeout (int): The timeout value, in seconds, to download and extract
-                model data from Amazon S3 to the individual inference instance associated with this
-                production variant.
+            model_data_download_timeout (int): The timeout value, in seconds, to download and
+                extract model data from Amazon S3 to the individual inference instance associated
+                with this production variant.
             container_startup_health_check_timeout (int): The timeout value, in seconds, for your
                 inference container to pass health check by SageMaker Hosting. For more information
                 about health check see:
@@ -204,7 +204,9 @@ def deploy(
         )
 
         production_variant = sagemaker.production_variant(
-            self.name, instance_type, initial_instance_count,
+            self.name,
+            instance_type,
+            initial_instance_count,
             volume_size=volume_size,
             model_data_download_timeout=model_data_download_timeout,
             container_startup_health_check_timeout=container_startup_health_check_timeout,

src/sagemaker/session.py

@@ -3010,9 +3010,9 @@ def create_endpoint_config(
             volume_size (int): The size, in GB, of the ML storage volume attached to individual
                 inference instance associated with the production variant. Currenly only Amazon EBS
                 gp2 storage volumes are supported.
-            model_data_download_timeout (int): The timeout value, in seconds, to download and extract
-                model data from Amazon S3 to the individual inference instance associated with this
-                production variant.
+            model_data_download_timeout (int): The timeout value, in seconds, to download and
+                extract model data from Amazon S3 to the individual inference instance associated
+                with this production variant.
             container_startup_health_check_timeout (int): The timeout value, in seconds, for your
                 inference container to pass health check by SageMaker Hosting. For more information
                 about health check see:

tests/unit/sagemaker/model/test_deploy.py

@@ -484,30 +484,38 @@ def test_deploy_predictor_cls(production_variant, sagemaker_session):
     assert predictor_async.endpoint_name == endpoint_name_async
     assert predictor_async.sagemaker_session == sagemaker_session
 
+
 @patch("sagemaker.production_variant")
 @patch("sagemaker.model.Model.prepare_container_def")
 @patch("sagemaker.utils.name_from_base", return_value=MODEL_NAME)
-def test_deploy_customized_volume_size_and_timeout(name_from_base, prepare_container_def, production_variant, sagemaker_session):
+def test_deploy_customized_volume_size_and_timeout(
+    name_from_base, prepare_container_def, production_variant, sagemaker_session
+):
     volume_size_gb = 256
     model_data_download_timeout_sec = 1800
     startup_health_check_timeout_sec = 1800
 
     production_variant_result = copy.deepcopy(BASE_PRODUCTION_VARIANT)
-    production_variant_result.update({
-        'VolumeSizeInGB': volume_size_gb,
-        'ModelDataDownloadTimeoutInSeconds': model_data_download_timeout_sec,
-        'ContainerStartupHealthCheckTimeoutInSeconds': startup_health_check_timeout_sec,
-    })
+    production_variant_result.update(
+        {
+            "VolumeSizeInGB": volume_size_gb,
+            "ModelDataDownloadTimeoutInSeconds": model_data_download_timeout_sec,
+            "ContainerStartupHealthCheckTimeoutInSeconds": startup_health_check_timeout_sec,
+        }
+    )
     production_variant.return_value = production_variant_result
 
     container_def = {"Image": MODEL_IMAGE, "Environment": {}, "ModelDataUrl": MODEL_DATA}
     prepare_container_def.return_value = container_def
 
     model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
-    model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT,
-                 volume_size=volume_size_gb,
-                 model_data_download_timeout=model_data_download_timeout_sec,
-                 container_startup_health_check_timeout=startup_health_check_timeout_sec)
+    model.deploy(
+        instance_type=INSTANCE_TYPE,
+        initial_instance_count=INSTANCE_COUNT,
+        volume_size=volume_size_gb,
+        model_data_download_timeout=model_data_download_timeout_sec,
+        container_startup_health_check_timeout=startup_health_check_timeout_sec,
+    )
 
     name_from_base.assert_called_with(MODEL_IMAGE)
     assert 2 == name_from_base.call_count

tests/unit/test_estimator.py

@@ -3332,10 +3332,14 @@ def test_deploy_with_customized_volume_size_timeout(create_model, sagemaker_sess
     model = MagicMock()
     create_model.return_value = model
 
-    estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE, endpoint_name=endpoint_name,
-                     volume_size=volume_size_gb,
-                     model_data_download_timeout=model_data_download_timeout_sec,
-                     container_startup_health_check_timeout=startup_health_check_timeout_sec)
+    estimator.deploy(
+        INSTANCE_COUNT,
+        INSTANCE_TYPE,
+        endpoint_name=endpoint_name,
+        volume_size=volume_size_gb,
+        model_data_download_timeout=model_data_download_timeout_sec,
+        container_startup_health_check_timeout=startup_health_check_timeout_sec,
+    )
 
     model.deploy.assert_called_with(
         instance_type=INSTANCE_TYPE,