chore: import upstream snapshot with attribution

doc/source/serve/doc_code/distilbert.py

@@ -0,0 +1,61 @@
+# __example_code_start__
+from transformers import pipeline
+from fastapi import FastAPI
+import torch
+
+from ray import serve
+from ray.serve.handle import DeploymentHandle
+
+
+app = FastAPI()
+
+
+@serve.deployment(num_replicas=1)
+@serve.ingress(app)
+class APIIngress:
+    def __init__(self, distilbert_model_handle: DeploymentHandle) -> None:
+        self.handle = distilbert_model_handle
+
+    @app.get("/classify")
+    async def classify(self, sentence: str):
+        return await self.handle.classify.remote(sentence)
+
+
+@serve.deployment(
+    ray_actor_options={"num_gpus": 1},
+    autoscaling_config={"min_replicas": 0, "max_replicas": 2},
+)
+class DistilBertModel:
+    def __init__(self):
+        self.classifier = pipeline(
+            "sentiment-analysis",
+            model="distilbert-base-uncased",
+            framework="pt",
+            # Transformers requires you to pass device with index
+            device=torch.device("cuda:0"),
+        )
+
+    def classify(self, sentence: str):
+        return self.classifier(sentence)
+
+
+entrypoint = APIIngress.bind(DistilBertModel.bind())
+
+# __example_code_end__
+
+if __name__ == "__main__":
+    import requests
+    import ray
+
+    ray.init(runtime_env={"pip": ["transformers==4.36.2", "accelerate==0.28.0"]})
+    serve.run(entrypoint)
+
+    prompt = (
+        "This was a masterpiece. Not completely faithful to the books, but "
+        "enthralling  from beginning to end. Might be my favorite of the three."
+    )
+    input = "%20".join(prompt.split(" "))
+    resp = requests.get(f"http://127.0.0.1:8000/classify?sentence={prompt}")
+    print(resp.status_code, resp.json())
+
+    assert resp.status_code == 200