import numpy as np import os from sklearn.linear_model import LogisticRegression from sklearn.pipeline import Pipeline import joblib from sklearn import datasets
filename_p = 'IrisClassifier.pkl' print('Saving model in %s' % filename_p) joblib.dump(p, filename_p) print('Model saved!')
if __name__ == "__main__": print('Loading iris data set...') iris = datasets.load_iris() X, y = iris.data, iris.target print('Dataset loaded!') main()
第二步:创建FastAPI实例
import uvicorn from fastapi import FastAPI import joblib from os.path import dirname, join, realpath from typing import List
app = FastAPI( title="Iris Prediction Model API", description="A simple API that use LogisticRegression model to predict the Iris species", version="0.1", )
# load model
with open( join(dirname(realpath(__file__)), "models/IrisClassifier.pkl"), "rb" ) as f: model = joblib.load(f)