mirror of
https://github.com/vale981/ray
synced 2025-03-06 18:41:40 -05:00
a12c04a2fe
Removes deprecated APIs: - serve.start() - get_handle() Rewrites the ServeHandle doc snippet to use the recommended workflow for ServeHandles (only access them from other deployments, pass Deployments in as input args to `.bind()`, which get resolved to ServeHandles at runtime) Co-authored-by: shrekris-anyscale <92341594+shrekris-anyscale@users.noreply.github.com>
98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
# flake8: noqa
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# fmt: off
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#
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# __serve_example_begin__
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#
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# This brief example shows how to create, deploy, and expose access to
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# deployment models, using the simple Ray Serve deployment APIs.
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# Once deployed, you can access deployment via two methods:
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# ServerHandle API and HTTP
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#
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import os
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from random import random
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import requests
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import starlette.requests
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from ray import serve
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#
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# A simple example model stored in a pickled format at an accessible path
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# that can be reloaded and deserialized into a model instance. Once deployed
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# in Ray Serve, we can use it for prediction. The prediction is a fake condition,
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# based on threshold of weight greater than 0.5.
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#
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class Model:
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def __init__(self, path):
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self.path = path
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def predict(self, data: float) -> float:
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return random() + data if data > 0.5 else data
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@serve.deployment
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class Predictor:
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# Take in a path to load your desired model
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def __init__(self, path: str) -> None:
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self.path = path
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self.model = Model(path)
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# Get the pid on which this deployment is running on
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self.pid = os.getpid()
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# Deployments are callable. Here we simply return a prediction from
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# our request.
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async def predict(self, data: float) -> str:
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pred = self.model.predict(data)
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return (f"(pid: {self.pid}); path: {self.path}; "
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f"data: {float(data):.3f}; prediction: {pred:.3f}")
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async def __call__(self, http_request: starlette.requests.Request) -> str:
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data = float(await http_request.query_params['data'])
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return await self.predict(data)
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@serve.deployment
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class ServeHandleDemo:
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def __init__(self, predictor_1: Predictor, predictor_2: Predictor):
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self.predictor_1 = predictor_1
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self.predictor_2 = predictor_2
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async def run(self):
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# Query each deployment twice to demonstrate that the requests
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# get forwarded to different replicas (below, we will set
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# num_replicas to 2 for each deployment).
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for _ in range(2):
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for predictor in [self.predictor_1, self.predictor_2]:
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# Call our deployments from Python using the ServeHandle API.
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random_prediction = await predictor.predict.remote(random())
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print(f"prediction: {random_prediction}")
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async def __call__(self, http_request: starlette.requests.Request) -> str:
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return await self.run()
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predictor_1 = Predictor.options(num_replicas=2).bind("/model/model-1.pkl")
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predictor_2 = Predictor.options(num_replicas=2).bind("/model/model-2.pkl")
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# Pass in our deployments as arguments. At runtime, these are resolved to ServeHandles.
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serve_handle_demo = ServeHandleDemo.bind(predictor_1, predictor_2)
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# Start a local single-node Ray cluster and start Ray Serve. These will shut down upon
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# exiting this script.
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serve.run(serve_handle_demo)
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print("ServeHandle API responses: " + "--" * 5)
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url = "http://127.0.0.1:8000/"
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response = requests.get(url)
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prediction = response.text
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print(f"prediction : {prediction}")
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# Output ("INFO" logs omitted for brevity):
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# (ServeReplica:ServeHandleDemo pid=16062) prediction: (pid: 16059); path: /model/model-1.pkl; data: 0.166; prediction: 0.166
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# (ServeReplica:ServeHandleDemo pid=16062) prediction: (pid: 16061); path: /model/model-2.pkl; data: 0.820; prediction: 0.986
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# (ServeReplica:ServeHandleDemo pid=16062) prediction: (pid: 16058); path: /model/model-1.pkl; data: 0.691; prediction: 0.857
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# (ServeReplica:ServeHandleDemo pid=16062) prediction: (pid: 16060); path: /model/model-2.pkl; data: 0.948; prediction: 1.113
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# __serve_example_end__
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