update samples from Release-108 as a part of 1.35.0 SDK stable release

how-to-use-azureml/explain-model/azure-integration/gpu-explanation/gpu_tree_explainer.py

@@ -0,0 +1,44 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+from azureml.core.run import Run
+import joblib
+import os
+import shap
+import xgboost
+
+OUTPUT_DIR = './outputs/'
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+run = Run.get_context()
+
+# get a dataset on income prediction
+X, y = shap.datasets.adult()
+
+# train an XGBoost model (but any other tree model type should work)
+model = xgboost.XGBClassifier()
+model.fit(X, y)
+
+explainer = shap.explainers.GPUTree(model, X)
+X_shap = X[:100]
+shap_values = explainer(X_shap)
+
+print("computed shap values:")
+print(shap_values)
+
+# write X_shap out as a pickle file for later visualization
+x_shap_pkl = 'x_shap.pkl'
+with open(x_shap_pkl, 'wb') as file:
+    joblib.dump(value=X_shap, filename=os.path.join(OUTPUT_DIR, x_shap_pkl))
+run.upload_file('x_shap_adult_census.pkl', os.path.join(OUTPUT_DIR, x_shap_pkl))
+
+model_file_name = 'xgboost_.pkl'
+# save model in the outputs folder so it automatically gets uploaded
+with open(model_file_name, 'wb') as file:
+    joblib.dump(value=model, filename=os.path.join(OUTPUT_DIR,
+                                                   model_file_name))
+
+# register the model
+run.upload_file('xgboost_model.pkl', os.path.join('./outputs/', model_file_name))
+original_model = run.register_model(model_name='xgboost_with_gpu_tree_explainer',
+                                    model_path='xgboost_model.pkl')