format fix

autogluon · Sep 17, 2024 · 7abb0e8 · 7abb0e8
1 parent 698850b
commit 7abb0e8
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Showing 3 changed files with 3 additions and 6 deletions.
diff --git a/src/autogluon/cloud/backend/sagemaker_backend.py b/src/autogluon/cloud/backend/sagemaker_backend.py
@@ -902,7 +902,7 @@ def predict_proba(
             model_kwargs=model_kwargs,
             transformer_kwargs=transformer_kwargs,
             transform_kwargs=transform_kwargs,
-            local_predictor=local_predictor, 
+            local_predictor=local_predictor,
         )
 
         if include_predict:
@@ -1112,7 +1112,6 @@ def _predict_real_time(self, test_data, accept, split_pred_proba=True, inference
             raise e
 
     def _upload_batch_predict_data(self, test_data, bucket, key_prefix):
-
         if isinstance(test_data, pd.DataFrame):
             test_data = self._prepare_data(test_data, "test", output_type="csv")
         logger.log(20, "Uploading data...")

diff --git a/src/autogluon/cloud/predictor/cloud_predictor.py b/src/autogluon/cloud/predictor/cloud_predictor.py
@@ -722,7 +722,7 @@ def predict_proba(
             backend_kwargs = {}
         backend_kwargs = self.backend.parse_backend_predict_kwargs(backend_kwargs)
         local_predictor = None
-        #TODO add support for multimodal and timeseries as this is needed for batch inference see issue #136
+        # TODO add support for multimodal and timeseries as this is needed for batch inference see issue #136
         if self.predictor_type == "tabular":
             local_predictor = self.to_local_predictor()
         return self.backend.predict_proba(

diff --git a/src/autogluon/cloud/scripts/sagemaker_scripts/tabular_serve.py b/src/autogluon/cloud/scripts/sagemaker_scripts/tabular_serve.py
@@ -58,9 +58,7 @@ def transform_fn(model, request_body, input_content_type, output_content_type="a
 
     elif input_content_type == "application/jsonl":
         buf = StringIO(request_body)
-        data = _read_with_fallback(
-            lambda b: pd.read_json(b, orient="records", lines=True), buf, column_names
-        )
+        data = _read_with_fallback(lambda b: pd.read_json(b, orient="records", lines=True), buf, column_names)
 
     elif input_content_type == "application/x-autogluon":
         buf = bytes(request_body)