机器学习模型部署远程服务功能

框架  flask

example

1.sklearn 训练模型并保持

import numpy as np

from sklearn.linear_model import LinearRegression

from sklearn.utils import check_random_state

from sklearn.externals import joblib

n = 100

x = np.arange(n)

rs = check_random_state(0)

y = rs.randint(-50, 50, size=(n,)) + 50. * np.log1p(np.arange(n))

 

lr = LinearRegression()

lr.fit(x[:, np.newaxis], y)

joblib.dump(lr,'lr_model.pkl')

 

2.训练好的模型提供远程服务

import numpy as np

from flask import Flask

from flask import request

from flask import jsonify

from sklearn.externals import joblib

#导入模型

model = joblib.load('lr_model.pkl')

app = Flask(__name__)

@app.route('/',methods=['POST','GET'])

def output_data():

    text=request.args.get('inputdata')

    if text:

    &nb