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architkulkarni
96 lines
2.5 KiB
Python
96 lines
2.5 KiB
Python
# yapf: disable
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import ray
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# __doc_import_begin__
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from ray import serve
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import pickle
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import json
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import numpy as np
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import requests
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import os
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import tempfile
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from sklearn.datasets import load_iris
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.metrics import mean_squared_error
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# __doc_import_end__
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# yapf: enable
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# __doc_train_model_begin__
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# Load data
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iris_dataset = load_iris()
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data, target, target_names = iris_dataset["data"], iris_dataset[
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"target"], iris_dataset["target_names"]
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# Instantiate model
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model = GradientBoostingClassifier()
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# Training and validation split
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np.random.shuffle(data), np.random.shuffle(target)
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train_x, train_y = data[:100], target[:100]
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val_x, val_y = data[100:], target[100:]
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# Train and evaluate models
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model.fit(train_x, train_y)
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print("MSE:", mean_squared_error(model.predict(val_x), val_y))
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# Save the model and label to file
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MODEL_PATH = os.path.join(tempfile.gettempdir(),
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"iris_model_logistic_regression.pkl")
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LABEL_PATH = os.path.join(tempfile.gettempdir(), "iris_labels.json")
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with open(MODEL_PATH, "wb") as f:
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pickle.dump(model, f)
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with open(LABEL_PATH, "w") as f:
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json.dump(target_names.tolist(), f)
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# __doc_train_model_end__
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# __doc_define_servable_begin__
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class BoostingModel:
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def __init__(self):
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with open(MODEL_PATH, "rb") as f:
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self.model = pickle.load(f)
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with open(LABEL_PATH) as f:
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self.label_list = json.load(f)
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async def __call__(self, starlette_request):
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payload = await starlette_request.json()
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print("Worker: received starlette request with data", payload)
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input_vector = [
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payload["sepal length"],
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payload["sepal width"],
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payload["petal length"],
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payload["petal width"],
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]
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prediction = self.model.predict([input_vector])[0]
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human_name = self.label_list[prediction]
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return {"result": human_name}
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# __doc_define_servable_end__
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ray.init(num_cpus=8)
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# __doc_deploy_begin__
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client = serve.start()
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client.create_backend("lr:v1", BoostingModel)
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client.create_endpoint("iris_classifier", backend="lr:v1", route="/regressor")
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# __doc_deploy_end__
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# __doc_query_begin__
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sample_request_input = {
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"sepal length": 1.2,
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"sepal width": 1.0,
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"petal length": 1.1,
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"petal width": 0.9,
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}
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response = requests.get(
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"http://localhost:8000/regressor", json=sample_request_input)
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print(response.text)
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# Result:
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# {
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# "result": "versicolor"
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# }
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# __doc_query_end__
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