[Serve] RayServe TF, PyTorch, Sklearn Examples (#8156)

python/ray/serve/examples/doc/tutorial_sklearn.py

@@ -0,0 +1,87 @@
+# yapf: disable
+# __doc_import_begin__
+from ray import serve
+
+import pickle
+import json
+import numpy as np
+import requests
+
+from sklearn.datasets import load_iris
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.metrics import mean_squared_error
+# __doc_import_end__
+# yapf: enable
+
+# __doc_train_model_begin__
+# Load data
+data, target, target_names, description, feature_names, _ = load_iris().values(
+)
+
+# Instantiate model
+model = GradientBoostingClassifier()
+
+# Training and validation split
+np.random.shuffle(data), np.random.shuffle(target)
+train_x, train_y = data[:100], target[:100]
+val_x, val_y = data[100:], target[100:]
+
+# Train and evaluate models
+model.fit(train_x, train_y)
+print("MSE:", mean_squared_error(model.predict(val_x), val_y))
+
+# Save the model and label to file
+with open("/tmp/iris_model_logistic_regression.pkl", "wb") as f:
+    pickle.dump(model, f)
+with open("/tmp/iris_labels.json", "w") as f:
+    json.dump(target_names.tolist(), f)
+# __doc_train_model_end__
+
+
+# __doc_define_servable_begin__
+class BoostingModel:
+    def __init__(self):
+        with open("/tmp/iris_model_logistic_regression.pkl", "rb") as f:
+            self.model = pickle.load(f)
+        with open("/tmp/iris_labels.json") as f:
+            self.label_list = json.load(f)
+
+    def __call__(self, flask_request):
+        payload = flask_request.json
+        print("Worker: received flask 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__
+serve.init()
+serve.create_endpoint("iris_classifier", "/regressor")
+serve.create_backend(BoostingModel, "lr:v1")
+serve.set_traffic("iris_classifier", {"lr:v1": 1})
+# __doc_deploy_end__
+
+# __doc_query_begin__
+sample_request_input = {
+    "sepal length": 1.2,
+    "sepal width": 1.0,
+    "petal length": 1.1,
+    "petal width": 0.9,
+}
+response = requests.get(
+    "http://localhost:8000/regressor", json=sample_request_input)
+print(response.text)
+# Result:
+# {
+#  "result": "versicolor"
+# }
+# __doc_query_end__