fix sanity

Author: Wei Chu

src/sagemaker_pytorch_serving_container/default_pytorch_inference_handler.py

@@ -66,7 +66,7 @@ def default_model_fn(self, model_dir, context=None):
                     "Failed to load {}. Please ensure model is saved using torchscript.".format(model_path)
                 ) from e
         else:
-            if context: 
+            if context:
                 properties = context.system_properties
                 device = torch.device("cuda:" + str(properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
             else:
@@ -100,7 +100,7 @@ def default_input_fn(self, input_data, content_type, context=None):
         Returns: input_data deserialized into torch.FloatTensor or torch.cuda.FloatTensor,
             depending if cuda is available.
         """
-        if context: 
+        if context:
             properties = context.system_properties
             device = torch.device("cuda:" + str(properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
         else:
@@ -130,9 +130,10 @@ def default_predict_fn(self, data, model, context=None):
                 with torch.jit.optimized_execution(True, {"target_device": "eia:0"}):
                     output = model(input_data)
             else:
-                if context: 
+                if context:
                     properties = context.system_properties
-                    device = torch.device("cuda:" + str(properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
+                    device = torch.device("cuda:" + str(properties.get("gpu_id")) 
+                                          if torch.cuda.is_available() else "cpu")
                 else:
                     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
                 model = model.to(device)

src/sagemaker_pytorch_serving_container/handler_service.py

@@ -22,6 +22,7 @@
 PYTHON_PATH_ENV = "PYTHONPATH"
 ENABLE_MULTI_MODEL = os.getenv("SAGEMAKER_MULTI_MODEL", "false") == "true"
 
+
 class HandlerService(DefaultHandlerService):
     """
     Handler service that is executed by the model server.

src/sagemaker_pytorch_serving_container/transformer.py

@@ -13,7 +13,6 @@
 
 from __future__ import absolute_import
 
-import logging
 import traceback
 
 from six.moves import http_client
@@ -88,7 +87,7 @@ def transform(self, data, context):
                     GenericInferenceToolkitError(http_client.INTERNAL_SERVER_ERROR, str(e)),
                     trace,
                 )
-    
+
     def validate_and_initialize(self, model_dir=environment.model_dir, context=None):
         """Validates the user module against the SageMaker inference contract.
         Load the model as defined by the ``model_fn`` to prepare handling predictions.
@@ -126,7 +125,7 @@ def _default_transform_fn(self, model, input_data, content_type, accept):
         result = self._run_handle_function(self._output_fn, *(prediction, accept))
 
         return result
-    
+
     def _run_handle_function(self, func, *argv):
         """Wrapper to call the handle function which covers 2 cases:
         1. context passed to the handle function
@@ -137,5 +136,5 @@ def _run_handle_function(self, func, *argv):
             result = func(*argv_context)
         except TypeError:
             result = func(*argv)
-        
+
         return result

test/utils/file_utils.py

@@ -19,7 +19,7 @@
 def make_tarfile(script, model, output_path, filename="model.tar.gz", script_path=None):
     output_filename = os.path.join(output_path, filename)
     with tarfile.open(output_filename, "w:gz") as tar:
-        if(script_path):
+        if (script_path):
             tar.add(script, arcname=os.path.join(script_path, os.path.basename(script)))
         else:
             tar.add(script, arcname=os.path.basename(script))