mirror of
https://github.com/vale981/ray
synced 2025-03-07 02:51:39 -05:00
5685d2e0b6
Updates the landing page to match the format and content of Tune's. Added some shorter quickstarts and sharpened up the messaging in our "Why choose Serve?" section, those are the main content changes. I also moved all of the `doc_code` into one directory and added a bazel target that should run all of the examples added there. Split into a separate PR: https://github.com/ray-project/ray/pull/24736.
26 lines
685 B
Python
26 lines
685 B
Python
import requests
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from transformers import pipeline
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from ray import serve
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# 1: Wrap the pretrained sentiment analysis model in a Serve deployment.
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@serve.deployment(route_prefix="/")
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class SentimentAnalysisDeployment:
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def __init__(self):
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self._model = pipeline("sentiment-analysis")
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def __call__(self, request):
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return self._model(request.query_params["text"])[0]
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# 2: Deploy the deployment.
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serve.start()
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SentimentAnalysisDeployment.deploy()
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# 3: Query the deployment and print the result.
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print(
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requests.get(
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"http://localhost:8000/", params={"text": "Ray Serve is great!"}
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).json()
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)
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# {'label': 'POSITIVE', 'score': 0.9998476505279541}
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