chore: import upstream snapshot with attribution

doc/source/serve/doc_code/object_detection.py

@@ -0,0 +1,69 @@
+# __example_code_start__
+import torch
+from PIL import Image
+import numpy as np
+from io import BytesIO
+from fastapi.responses import Response
+from fastapi import FastAPI
+
+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, object_detection_handle: DeploymentHandle):
+        self.handle = object_detection_handle
+
+    @app.get(
+        "/detect",
+        responses={200: {"content": {"image/jpeg": {}}}},
+        response_class=Response,
+    )
+    async def detect(self, image_url: str):
+        image = await self.handle.detect.remote(image_url)
+        file_stream = BytesIO()
+        image.save(file_stream, "jpeg")
+        return Response(content=file_stream.getvalue(), media_type="image/jpeg")
+
+
+@serve.deployment(
+    ray_actor_options={"num_gpus": 1},
+    autoscaling_config={"min_replicas": 1, "max_replicas": 2},
+)
+class ObjectDetection:
+    def __init__(self):
+        self.model = torch.hub.load("ultralytics/yolov5", "yolov5s")
+        self.model.cuda()
+        self.model.to(torch.device(0))
+
+    def detect(self, image_url: str):
+        result_im = self.model(image_url)
+        return Image.fromarray(result_im.render()[0].astype(np.uint8))
+
+
+entrypoint = APIIngress.bind(ObjectDetection.bind())
+
+
+# __example_code_end__
+
+if __name__ == "__main__":
+    import ray
+    import requests
+    import os
+
+    ray.init(runtime_env={"pip": ["seaborn", "ultralytics"]})
+
+    serve.run(entrypoint)
+    image_url = "https://ultralytics.com/images/zidane.jpg"
+    resp = requests.get(f"http://127.0.0.1:8000/detect?image_url={image_url}")
+
+    with open("output.jpeg", "wb") as f:
+        f.write(resp.content)
+
+    assert os.path.exists("output.jpeg")
+    os.remove("output.jpeg")