lint

src/autogluon/cloud/predictor/timeseries_cloud_predictor.py

@@ -181,15 +181,15 @@ def predict_real_time(
         self.id_column = id_column or self.id_column
         self.timestamp_column = timestamp_column or self.timestamp_column
         self.target_column = target or self.target_column
-        
+
         return self.backend.predict_real_time(
             test_data=test_data,
             id_column=self.id_column,
             timestamp_column=self.timestamp_column,
             target=self.target_column,
             static_features=static_features,
             accept=accept,
-            inference_kwargs=kwargs
+            inference_kwargs=kwargs,
         )
 
     def predict_proba_real_time(self, **kwargs) -> pd.DataFrame:

src/autogluon/cloud/scripts/sagemaker_scripts/tabular_serve.py

@@ -33,6 +33,7 @@ def _save_image_and_update_dataframe_column(bytes):
 
     return im_path
 
+
 def _custom_json_deserializer(serialized_str):
     """
     Deserialize the JSON string that may include representations of complex data types like DataFrames
@@ -55,6 +56,7 @@ def _custom_json_deserializer(serialized_str):
 
     return deserialized_kwargs
 
+
 def model_fn(model_dir):
     """loads model from previously saved artifact"""
     logger.info("Loading the model")

src/autogluon/cloud/scripts/sagemaker_scripts/timeseries_serve.py

@@ -1,17 +1,19 @@
 # flake8: noqa
+import logging
 import os
 import pickle
 import shutil
+import sys
 from io import BytesIO, StringIO
 
 import pandas as pd
-import logging
-import sys
 
 from autogluon.timeseries import TimeSeriesDataFrame, TimeSeriesPredictor
+
 logging.basicConfig(stream=sys.stdout, level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+
 def model_fn(model_dir):
     """loads model from previously saved artifact"""
     # TSPredictor will write to the model file during inference while the default model_dir is read only