chore: import upstream snapshot with attribution

doc/source/serve/doc_code/tutorial_sklearn.py

@@ -0,0 +1,81 @@
+# fmt: off
+# __doc_import_begin__
+from ray import serve
+
+import pickle
+import json
+import numpy as np
+import os
+import tempfile
+from starlette.requests import Request
+from typing import Dict
+
+from sklearn.datasets import load_iris
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.metrics import mean_squared_error
+# __doc_import_end__
+# fmt: on
+
+# __doc_instantiate_model_begin__
+model = GradientBoostingClassifier()
+# __doc_instantiate_model_end__
+
+# __doc_data_begin__
+iris_dataset = load_iris()
+data, target, target_names = (
+    iris_dataset["data"],
+    iris_dataset["target"],
+    iris_dataset["target_names"],
+)
+
+np.random.shuffle(data)
+np.random.shuffle(target)
+train_x, train_y = data[:100], target[:100]
+val_x, val_y = data[100:], target[100:]
+# __doc_data_end__
+
+# __doc_train_model_begin__
+model.fit(train_x, train_y)
+print("MSE:", mean_squared_error(model.predict(val_x), val_y))
+
+# Save the model and label to file
+MODEL_PATH = os.path.join(
+    tempfile.gettempdir(), "iris_model_gradient_boosting_classifier.pkl"
+)
+LABEL_PATH = os.path.join(tempfile.gettempdir(), "iris_labels.json")
+
+with open(MODEL_PATH, "wb") as f:
+    pickle.dump(model, f)
+with open(LABEL_PATH, "w") as f:
+    json.dump(target_names.tolist(), f)
+# __doc_train_model_end__
+
+
+# __doc_define_servable_begin__
+@serve.deployment
+class BoostingModel:
+    def __init__(self, model_path: str, label_path: str):
+        with open(model_path, "rb") as f:
+            self.model = pickle.load(f)
+        with open(label_path) as f:
+            self.label_list = json.load(f)
+
+    async def __call__(self, starlette_request: Request) -> Dict:
+        payload = await starlette_request.json()
+        print("Worker: received starlette request with data", payload)
+
+        input_vector = [
+            payload["sepal length"],
+            payload["sepal width"],
+            payload["petal length"],
+            payload["petal width"],
+        ]
+        prediction = self.model.predict([input_vector])[0]
+        human_name = self.label_list[prediction]
+        return {"result": human_name}
+        # __doc_define_servable_end__
+
+
+# __doc_deploy_begin__
+boosting_model = BoostingModel.bind(MODEL_PATH, LABEL_PATH)
+# __doc_deploy_end__