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https://github.com/vale981/ray
synced 2025-03-09 04:46:38 -04:00
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.
43 lines
1 KiB
Python
43 lines
1 KiB
Python
# flake8: noqa
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# fmt: off
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# __serve_example_begin__
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import requests
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from sklearn.datasets import load_iris
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from sklearn.ensemble import GradientBoostingClassifier
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from ray import serve
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serve.start()
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# Train model.
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iris_dataset = load_iris()
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model = GradientBoostingClassifier()
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model.fit(iris_dataset["data"], iris_dataset["target"])
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@serve.deployment(route_prefix="/iris")
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class BoostingModel:
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def __init__(self, model):
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self.model = model
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self.label_list = iris_dataset["target_names"].tolist()
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async def __call__(self, request):
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payload = (await request.json())["vector"]
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print(f"Received http request with data {payload}")
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prediction = self.model.predict([payload])[0]
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human_name = self.label_list[prediction]
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return {"result": human_name}
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# Deploy model.
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BoostingModel.deploy(model)
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# Query it!
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sample_request_input = {"vector": [1.2, 1.0, 1.1, 0.9]}
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response = requests.get(
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"http://localhost:8000/iris", json=sample_request_input)
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print(response.text)
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# __serve_example_end__
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