我能够在没有烧瓶或 django 的情况下像这样工作。只需在 python 中使用默认的 http.server

from http.server import BaseHTTPRequestHandler, HTTPServer
import logging
import sys
import pandas as pd
from keras.models import load_model
from urllib.parse import urlparse
model = load_model('model.h5')

class S(BaseHTTPRequestHandler):
    def _set_response(self):
        self.send_response(200)
        self.send_header('Content-type', 'text/html')
        self.end_headers()

    def do_GET(self):
        query = urlparse(self.path).query
        params = dict(qc.split("=") for qc in query.split("&"))
        X = pd.read_csv(params["input"]).values
        prediction = model.predict(X)
        pd.DataFrame(prediction).to_json(params["output"])
        self._set_response()
        self.wfile.write("Processed".encode('utf-8'))

def run(server_class=HTTPServer, handler_class=S, port=8080):
    logging.basicConfig(level=logging.INFO)
    server_address = ('', port)
    httpd = server_class(server_address, handler_class)
    logging.info('Starting httpd...\n')
    try:
        httpd.serve_forever()
    except KeyboardInterrupt:
        pass
    httpd.server_close()
    logging.info('Stopping httpd...\n')

if __name__ == '__main__':
    from sys import argv

    if len(argv) == 2:
        run(port=int(argv[1]))
    else:
        run()

触发服务器使用

python3.6 predict_server.py 8000

类似 API

http://ip/localhost:8000/?input=predict_scaled.csv&output=prediction.json

我能想到的最简单的方法是创建一个烧瓶应用程序，该应用程序将加载一次模型并具有端点，您可以将数据作为请求发送到已加载的模型。

服务的粗略框架如下所示：

from flask import Flask, request


app = Flask(__name__)


@app.route('/')
def index():
    return ''

@app.route('/predict/', methods=['GET', 'POST'])
def predict():
    X = pd.read_csv(request.get_data()).values
    prediction = model.predict(X)
    return pd.DataFrame(prediction).to_json()


if __name__ == "__main__":
    model = load_model('model.h5')
    app.run()

然后你可以通过另一个脚本向 发出 HTTP 请求localhost:5000/predict，它会返回你的预测，然后你可以保存或做任何你想做的事情。