chore: import upstream snapshot with attribution

doc/source/serve/doc_code/getting_started/model_deployment.py

@@ -0,0 +1,67 @@
+# flake8: noqa
+
+# __import_start__
+from starlette.requests import Request
+
+import ray
+from ray import serve
+
+# __import_end__
+
+# __model_start__
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+
+@serve.deployment(num_replicas=2, ray_actor_options={"num_cpus": 0.2, "num_gpus": 0})
+class Translator:
+    def __init__(self):
+        # Load model
+        self.tokenizer = AutoTokenizer.from_pretrained("t5-small")
+        self.model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
+
+    def translate(self, text: str) -> str:
+        # Run inference
+        input_ids = self.tokenizer(
+            f"translate English to French: {text}", return_tensors="pt"
+        ).input_ids
+        output_ids = self.model.generate(
+            input_ids, num_beams=4, early_stopping=True, max_length=300
+        )
+
+        # Post-process output to return only the translation text
+        translation = self.tokenizer.decode(
+            output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+        return translation
+
+    async def __call__(self, http_request: Request) -> str:
+        english_text: str = await http_request.json()
+        return self.translate(english_text)
+
+
+# __model_end__
+
+# __model_deploy_start__
+translator_app = Translator.bind()
+# __model_deploy_end__
+
+translator_app = Translator.options(ray_actor_options={}).bind()
+serve.run(translator_app)
+
+# __client_function_start__
+# File name: model_client.py
+import requests
+
+english_text = "Hello world!"
+
+response = requests.post("http://127.0.0.1:8000/", json=english_text)
+french_text = response.text
+
+print(french_text)
+# __client_function_end__
+
+assert french_text == "Bonjour monde!"
+
+serve.shutdown()
+ray.shutdown()