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()

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()

你可以把你的python编译，然后像调用javascript一样调用它。我已经成功地为screeps做了这件事，甚至让它在浏览器中运行la brython。

我在节点10和子进程1.0.2上。来自python的数据是一个字节数组，必须进行转换。这是另一个用python发出http请求的快速示例。

node

const process = spawn("python", ["services/request.py", "https://www.google.com"])

return new Promise((resolve, reject) =>{
    process.stdout.on("data", data =>{
        resolve(data.toString()); // <------------ by default converts to utf-8
    })
    process.stderr.on("data", reject)
})

request.py

import urllib.request
import sys

def karl_morrison_is_a_pedant():   
    response = urllib.request.urlopen(sys.argv[1])
    html = response.read()
    print(html)
    sys.stdout.flush()

karl_morrison_is_a_pedant()

p.s.不是一个人为的例子，因为节点的http模块不加载我需要做的一些请求

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。