您现在可以使用支持Python和Javascript的RPC库，例如zerorpc

从他们的头版:

node . js的客户

var zerorpc = require("zerorpc");

var client = new zerorpc.Client();
client.connect("tcp://127.0.0.1:4242");

client.invoke("hello", "RPC", function(error, res, more) {
    console.log(res);
});

Python服务器

import zerorpc

class HelloRPC(object):
    def hello(self, name):
        return "Hello, %s" % name

s = zerorpc.Server(HelloRPC())
s.bind("tcp://0.0.0.0:4242")
s.run()

通过extrabacon, Python -shell模块是一种从Node.js运行Python脚本的简单方法，具有基本但有效的进程间通信和更好的错误处理。

安装:

npm: NPM安装python-shell。

或者用纱线: 纱线加蟒壳

运行一个简单的Python脚本:

const PythonShell = require('python-shell').PythonShell;

PythonShell.run('my_script.py', null, function (err) {
  if (err) throw err;
  console.log('finished');
});

运行带有参数和选项的Python脚本:

const PythonShell = require('python-shell').PythonShell;

var options = {
  mode: 'text',
  pythonPath: 'path/to/python',
  pythonOptions: ['-u'],
  scriptPath: 'path/to/my/scripts',
  args: ['value1', 'value2', 'value3']
};

PythonShell.run('my_script.py', options, function (err, results) {
  if (err) 
    throw err;
  // Results is an array consisting of messages collected during execution
  console.log('results: %j', results);
});

要获得完整的文档和源代码，请访问https://github.com/extrabacon/python-shell

您现在可以使用支持Python和Javascript的RPC库，例如zerorpc

从他们的头版:

node . js的客户

var zerorpc = require("zerorpc");

var client = new zerorpc.Client();
client.connect("tcp://127.0.0.1:4242");

client.invoke("hello", "RPC", function(error, res, more) {
    console.log(res);
});

Python服务器

import zerorpc

class HelloRPC(object):
    def hello(self, name):
        return "Hello, %s" % name

s = zerorpc.Server(HelloRPC())
s.bind("tcp://0.0.0.0:4242")
s.run()

Boa很适合您的需求，请参阅扩展Python tensorflow keras的示例。JavaScript中的顺序类。

const fs = require('fs');
const boa = require('@pipcook/boa');
const { tuple, enumerate } = boa.builtins();

const tf = boa.import('tensorflow');
const tfds = boa.import('tensorflow_datasets');

const { keras } = tf;
const { layers } = keras;

const [
  [ train_data, test_data ],
  info
] = tfds.load('imdb_reviews/subwords8k', boa.kwargs({
  split: tuple([ tfds.Split.TRAIN, tfds.Split.TEST ]),
  with_info: true,
  as_supervised: true
}));

const encoder = info.features['text'].encoder;
const padded_shapes = tuple([
  [ null ], tuple([])
]);
const train_batches = train_data.shuffle(1000)
  .padded_batch(10, boa.kwargs({ padded_shapes }));
const test_batches = test_data.shuffle(1000)
  .padded_batch(10, boa.kwargs({ padded_shapes }));

const embedding_dim = 16;
const model = keras.Sequential([
  layers.Embedding(encoder.vocab_size, embedding_dim),
  layers.GlobalAveragePooling1D(),
  layers.Dense(16, boa.kwargs({ activation: 'relu' })),
  layers.Dense(1, boa.kwargs({ activation: 'sigmoid' }))
]);

model.summary();
model.compile(boa.kwargs({
  optimizer: 'adam',
  loss: 'binary_crossentropy',
  metrics: [ 'accuracy' ]
}));

完整的示例在:https://github.com/alibaba/pipcook/blob/master/example/boa/tf2/word-embedding.js

我在另一个项目pipook中使用了Boa，这是为了解决JavaScript开发人员的机器学习问题，我们通过Boa库在Python生态系统(tensorflow,keras,pytorch)上实现了ML/DL模型。

/*eslint-env es6*/
/*global require*/
/*global console*/
var express = require('express'); 
var app = express();

// Creates a server which runs on port 3000 and  
// can be accessed through localhost:3000
app.listen(3000, function() { 
    console.log('server running on port 3000'); 
} ) 

app.get('/name', function(req, res) {

    console.log('Running');

    // Use child_process.spawn method from  
    // child_process module and assign it 
    // to variable spawn 
    var spawn = require("child_process").spawn;   
    // Parameters passed in spawn - 
    // 1. type_of_script 
    // 2. list containing Path of the script 
    //    and arguments for the script  

    // E.g : http://localhost:3000/name?firstname=Levente
    var process = spawn('python',['apiTest.py', 
                        req.query.firstname]);

    // Takes stdout data from script which executed 
    // with arguments and send this data to res object
    var output = '';
    process.stdout.on('data', function(data) {

        console.log("Sending Info")
        res.end(data.toString('utf8'));
    });

    console.log(output);
}); 

这对我很管用。必须将python.exe添加到此代码段的路径变量中。另外，确保你的python脚本在你的项目文件夹中。

有Python背景，想要在Node.js应用程序中集成机器学习模型的人:

它使用了child_process核心模块:

const express = require('express')
const app = express()

app.get('/', (req, res) => {

    const { spawn } = require('child_process');
    const pyProg = spawn('python', ['./../pypy.py']);

    pyProg.stdout.on('data', function(data) {

        console.log(data.toString());
        res.write(data);
        res.end('end');
    });
})

app.listen(4000, () => console.log('Application listening on port 4000!'))

它不需要Python脚本中的sys模块。

下面是使用Promise执行任务的更模块化的方式:

const express = require('express')
const app = express()

let runPy = new Promise(function(success, nosuccess) {

    const { spawn } = require('child_process');
    const pyprog = spawn('python', ['./../pypy.py']);

    pyprog.stdout.on('data', function(data) {

        success(data);
    });

    pyprog.stderr.on('data', (data) => {

        nosuccess(data);
    });
});

app.get('/', (req, res) => {

    res.write('welcome\n');

    runPy.then(function(fromRunpy) {
        console.log(fromRunpy.toString());
        res.end(fromRunpy);
    });
})

app.listen(4000, () => console.log('Application listening on port 4000!'))