[docs] Ray Serve Documentation Overhaul (#8524)

python/ray/serve/BUILD

@@ -88,3 +88,12 @@ py_test(
     tags = ["exclusive"],
     deps = [":serve_lib"]
 )
+
+py_test(
+    name = "tutorial_deploy",
+    size = "small",
+    srcs = glob(["examples/doc/*.py"]),
+    tags = ["exclusive"],
+    deps = [":serve_lib"]
+)
+

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

@@ -0,0 +1,169 @@
+# yapf: disable
+# __doc_import_train_begin__
+import pickle
+import json
+import numpy as np
+
+from sklearn.datasets import load_iris
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.metrics import mean_squared_error
+
+# Load data
+iris_dataset = load_iris()
+data, target, target_names = iris_dataset["data"], iris_dataset[
+    "target"], iris_dataset["target_names"]
+
+# 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_import_train_end__
+
+
+# __doc_create_deploy_begin__
+import pickle  # noqa: E402
+import json  # noqa: E402
+
+from ray import serve  # noqa: E402
+import ray  # noqa: E402
+
+
+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}
+
+
+# connect to our existing Ray cluster
+# note that the password will be different for your redis instance!
+ray.init(address="auto")
+# now we initialize /connect to the Ray service
+
+serve.init()
+serve.create_endpoint("iris_classifier", "/regressor")
+serve.create_backend("lr:v1", BoostingModel)
+serve.set_traffic("iris_classifier", {"lr:v1": 1, "version": "v1"})
+# __doc_create_deploy_end__
+
+# __doc_query_begin__
+import requests  # noqa: E402
+
+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": "setosa",
+#  "version": "v1"
+# }
+# this result may vary, since the training parameters may change.
+# as we update this model, this result will also change over time.
+# __doc_query_end__
+
+
+# __doc_create_deploy_2_begin__
+import pickle  # noqa: E402
+import json  # noqa: E402
+import numpy as np  # noqa: E402
+
+from sklearn.datasets import load_iris  # noqa: E402
+from sklearn.ensemble import GradientBoostingClassifier  # noqa: E402
+from sklearn.metrics import mean_squared_error  # noqa: E402
+
+# Load data
+iris_dataset = load_iris()
+data, target, target_names = iris_dataset["data"], iris_dataset[
+    "target"], iris_dataset["target_names"]
+
+# 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_2.pkl", "wb") as f:
+    pickle.dump(model, f)
+with open("/tmp/iris_labels_2.json", "w") as f:
+    json.dump(target_names.tolist(), f)
+
+
+import pickle  # noqa: E402
+import json  # noqa: E402
+
+from ray import serve  # noqa: E402
+import ray  # noqa: E402
+
+
+class BoostingModelv2:
+    def __init__(self):
+        with open("/tmp/iris_model_logistic_regression_2.pkl", "rb") as f:
+            self.model = pickle.load(f)
+        with open("/tmp/iris_labels_2.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, "version": "v2"}
+
+# connect to our existing Ray cluster
+# note that the password will be different for your redis instance!
+# ray.init(address='auto', redis_password='5241590000000000')
+# now we initialize /connect to the Ray service
+
+
+serve.init()
+serve.create_backend("lr:v2", BoostingModelv2)
+serve.set_traffic("iris_classifier", {"lr:v2": 0.25, "lr:v1": 0.75})
+# __doc_create_deploy_2_end__