wehub-resource-sync
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
# flake8: noqa
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# fmt: off
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# __example_deployment_start__
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import time
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from ray import serve
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from starlette.requests import Request
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@serve.deployment(
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# Each replica will be sent 2 requests at a time.
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max_ongoing_requests=2,
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# Each caller queues up to 2 requests at a time.
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# (beyond those that are sent to replicas).
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max_queued_requests=2,
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)
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class SlowDeployment:
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def __call__(self, request: Request) -> str:
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# Emulate a long-running request, such as ML inference.
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time.sleep(2)
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return "Hello!"
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# __example_deployment_end__
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# __client_test_start__
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import ray
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import aiohttp
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@ray.remote
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class Requester:
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async def do_request(self) -> int:
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async with aiohttp.ClientSession("http://localhost:8000/") as session:
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return (await session.get("/")).status
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r = Requester.remote()
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serve.run(SlowDeployment.bind())
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# Send 4 requests first.
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# 2 of these will be sent to the replica. These requests take a few seconds to execute.
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first_refs = [r.do_request.remote() for _ in range(2)]
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_, pending = ray.wait(first_refs, timeout=1)
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assert len(pending) == 2
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# 2 will be queued in the proxy.
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queued_refs = [r.do_request.remote() for _ in range(2)]
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_, pending = ray.wait(queued_refs, timeout=0.1)
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assert len(pending) == 2
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# Send an additional 5 requests. These will be rejected immediately because
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# the replica and the proxy queue are already full.
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for status_code in ray.get([r.do_request.remote() for _ in range(5)]):
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assert status_code == 503
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# The initial requests will finish successfully.
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for ref in first_refs:
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print(f"Request finished with status code {ray.get(ref)}.")
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# __client_test_end__
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