update samples from Release-72 as a part of SDK release

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/infer.py

@@ -10,6 +10,8 @@ from sklearn.metrics import mean_absolute_error, mean_squared_error
 from azureml.automl.runtime.shared.score import scoring, constants
 from azureml.core import Run
 
+import torch
+
 
 def align_outputs(y_predicted, X_trans, X_test, y_test,
                   predicted_column_name='predicted',
@@ -221,6 +223,10 @@ def MAPE(actual, pred):
     return np.mean(APE(actual_safe, pred_safe))
 
 
+def map_location_cuda(storage, loc):
+    return storage.cuda()
+
+
 parser = argparse.ArgumentParser()
 parser.add_argument(
     '--max_horizon', type=int, dest='max_horizon',
@@ -274,8 +280,13 @@ X_lookback_df = lookback_dataset.drop_columns(columns=[target_column_name])
 y_lookback_df = lookback_dataset.with_timestamp_columns(
     None).keep_columns(columns=[target_column_name])
 
-fitted_model = joblib.load(model_path)
-
+# Load the trained model with torch.
+if torch.cuda.is_available():
+    map_location = map_location_cuda
+else:
+    map_location = 'cpu'
+with open(model_path, 'rb') as fh:
+    fitted_model = torch.load(fh, map_location=map_location)
 
 if hasattr(fitted_model, 'get_lookback'):
     lookback = fitted_model.get_lookback()