chore: import upstream snapshot with attribution

doc/source/serve/doc_code/tutorial_tensorflow.py

@@ -0,0 +1,75 @@
+# fmt: off
+# __doc_import_begin__
+from ray import serve
+
+import os
+import tempfile
+import numpy as np
+from starlette.requests import Request
+from typing import Dict
+
+import tensorflow as tf
+# __doc_import_end__
+# fmt: on
+
+# __doc_train_model_begin__
+TRAINED_MODEL_PATH = os.path.join(tempfile.gettempdir(), "mnist_model.h5")
+
+
+def train_and_save_model():
+    # Load mnist dataset
+    mnist = tf.keras.datasets.mnist
+    (x_train, y_train), (x_test, y_test) = mnist.load_data()
+    x_train, x_test = x_train / 255.0, x_test / 255.0
+
+    # Train a simple neural net model
+    model = tf.keras.models.Sequential(
+        [
+            tf.keras.layers.Flatten(input_shape=(28, 28)),
+            tf.keras.layers.Dense(128, activation="relu"),
+            tf.keras.layers.Dropout(0.2),
+            tf.keras.layers.Dense(10),
+        ]
+    )
+    loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+    model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
+    model.fit(x_train, y_train, epochs=1)
+
+    model.evaluate(x_test, y_test, verbose=2)
+    model.summary()
+
+    # Save the model in h5 format in local file system
+    model.save(TRAINED_MODEL_PATH)
+
+
+if not os.path.exists(TRAINED_MODEL_PATH):
+    train_and_save_model()
+# __doc_train_model_end__
+
+
+# __doc_define_servable_begin__
+@serve.deployment
+class TFMnistModel:
+    def __init__(self, model_path: str):
+        import tensorflow as tf
+
+        self.model_path = model_path
+        self.model = tf.keras.models.load_model(model_path)
+
+    async def __call__(self, starlette_request: Request) -> Dict:
+        # Step 1: transform HTTP request -> tensorflow input
+        # Here we define the request schema to be a json array.
+        input_array = np.array((await starlette_request.json())["array"])
+        reshaped_array = input_array.reshape((1, 28, 28))
+
+        # Step 2: tensorflow input -> tensorflow output
+        prediction = self.model(reshaped_array)
+
+        # Step 3: tensorflow output -> web output
+        return {"prediction": prediction.numpy().tolist(), "file": self.model_path}
+        # __doc_define_servable_end__
+
+
+# __doc_deploy_begin__
+mnist_model = TFMnistModel.bind(TRAINED_MODEL_PATH)
+# __doc_deploy_end__