update samples from Release-73 as a part of SDK release

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

README.md

@@ -1,7 +1,5 @@
 # Azure Machine Learning service example notebooks
 
-> A community-driven repository of training and scoring examples can be found at https://github.com/Azure/azureml-examples
-
 This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
 
 ![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/media/concept-azure-machine-learning-architecture/workflow.png)

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

@@ -1,4 +1,5 @@
 import argparse
+import os
 
 import numpy as np
 import pandas as pd
@@ -10,6 +11,13 @@ from sklearn.metrics import mean_absolute_error, mean_squared_error
 from azureml.automl.runtime.shared.score import scoring, constants
 from azureml.core import Run
 
+try:
+    import torch
+
+    _torch_present = True
+except ImportError:
+    _torch_present = False
+
 
 def align_outputs(y_predicted, X_trans, X_test, y_test,
                   predicted_column_name='predicted',
@@ -48,7 +56,7 @@ def align_outputs(y_predicted, X_trans, X_test, y_test,
     # or at edges of time due to lags/rolling windows
     clean = together[together[[target_column_name,
                                predicted_column_name]].notnull().all(axis=1)]
-    return(clean)
+    return (clean)
 
 
 def do_rolling_forecast_with_lookback(fitted_model, X_test, y_test,
@@ -83,8 +91,7 @@ def do_rolling_forecast_with_lookback(fitted_model, X_test, y_test,
         if origin_time != X[time_column_name].min():
             # Set the context by including actuals up-to the origin time
             test_context_expand_wind = (X[time_column_name] < origin_time)
-            context_expand_wind = (
-                X_test_expand[time_column_name] < origin_time)
+            context_expand_wind = (X_test_expand[time_column_name] < origin_time)
             y_query_expand[context_expand_wind] = y[test_context_expand_wind]
 
         # Print some debug info
@@ -115,8 +122,7 @@ def do_rolling_forecast_with_lookback(fitted_model, X_test, y_test,
         # Align forecast with test set for dates within
         # the current rolling window
         trans_tindex = X_trans.index.get_level_values(time_column_name)
-        trans_roll_wind = (trans_tindex >= origin_time) & (
-            trans_tindex < horizon_time)
+        trans_roll_wind = (trans_tindex >= origin_time) & (trans_tindex < horizon_time)
         test_roll_wind = expand_wind & (X[time_column_name] >= origin_time)
         df_list.append(align_outputs(
             y_fcst[trans_roll_wind], X_trans[trans_roll_wind],
@@ -155,8 +161,7 @@ def do_rolling_forecast(fitted_model, X_test, y_test, max_horizon, freq='D'):
         if origin_time != X_test[time_column_name].min():
             # Set the context by including actuals up-to the origin time
             test_context_expand_wind = (X_test[time_column_name] < origin_time)
-            context_expand_wind = (
-                X_test_expand[time_column_name] < origin_time)
+            context_expand_wind = (X_test_expand[time_column_name] < origin_time)
             y_query_expand[context_expand_wind] = y_test[
                 test_context_expand_wind]
 
@@ -186,10 +191,8 @@ def do_rolling_forecast(fitted_model, X_test, y_test, max_horizon, freq='D'):
         # Align forecast with test set for dates within the
         # current rolling window
         trans_tindex = X_trans.index.get_level_values(time_column_name)
-        trans_roll_wind = (trans_tindex >= origin_time) & (
-            trans_tindex < horizon_time)
-        test_roll_wind = expand_wind & (
-            X_test[time_column_name] >= origin_time)
+        trans_roll_wind = (trans_tindex >= origin_time) & (trans_tindex < horizon_time)
+        test_roll_wind = expand_wind & (X_test[time_column_name] >= origin_time)
         df_list.append(align_outputs(y_fcst[trans_roll_wind],
                                      X_trans[trans_roll_wind],
                                      X_test[test_roll_wind],
@@ -221,6 +224,10 @@ 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',
@@ -238,7 +245,6 @@ parser.add_argument(
     '--model_path', type=str, dest='model_path',
     default='model.pkl', help='Filename of model to be loaded')
 
-
 args = parser.parse_args()
 max_horizon = args.max_horizon
 target_column_name = args.target_column_name
@@ -246,7 +252,6 @@ time_column_name = args.time_column_name
 freq = args.freq
 model_path = args.model_path
 
-
 print('args passed are: ')
 print(max_horizon)
 print(target_column_name)
@@ -274,8 +279,19 @@ 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)
-
+_, ext = os.path.splitext(model_path)
+if ext == '.pt':
+    # Load the fc-tcn torch model.
+    assert _torch_present
+    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)
+else:
+    # Load the sklearn pipeline.
+    fitted_model = joblib.load(model_path)
 
 if hasattr(fitted_model, 'get_lookback'):
     lookback = fitted_model.get_lookback()