如果你正在使用Python 3，你可以使用concurrent.futures.ProcessPoolExecutor作为一个方便的抽象:

from concurrent.futures import ProcessPoolExecutor

def worker(procnum):
    '''worker function'''
    print(str(procnum) + ' represent!')
    return procnum


if __name__ == '__main__':
    with ProcessPoolExecutor() as executor:
        print(list(executor.map(worker, range(5))))

输出:

0 represent!
1 represent!
2 represent!
3 represent!
4 represent!
[0, 1, 2, 3, 4]

使用共享变量进行通信。比如这样:

import multiprocessing


def worker(procnum, return_dict):
    """worker function"""
    print(str(procnum) + " represent!")
    return_dict[procnum] = procnum


if __name__ == "__main__":
    manager = multiprocessing.Manager()
    return_dict = manager.dict()
    jobs = []
    for i in range(5):
        p = multiprocessing.Process(target=worker, args=(i, return_dict))
        jobs.append(p)
        p.start()

    for proc in jobs:
        proc.join()
    print(return_dict.values())

对于正在寻找如何使用Queue从进程中获取值的任何人:

import multiprocessing

ret = {'foo': False}

def worker(queue):
    ret = queue.get()
    ret['foo'] = True
    queue.put(ret)

if __name__ == '__main__':
    queue = multiprocessing.Queue()
    queue.put(ret)
    p = multiprocessing.Process(target=worker, args=(queue,))
    p.start()
    p.join()
    print(queue.get())  # Prints {"foo": True}

注意，在Windows或Jupyter Notebook中，使用多线程，您必须将其保存为文件并执行该文件。如果你在命令提示符中这样做，你会看到这样的错误:

 AttributeError: Can't get attribute 'worker' on <module '__main__' (built-in)>

我认为@sega_sai建议的方法更好。但它确实需要一个代码示例，所以如下:

import multiprocessing
from os import getpid

def worker(procnum):
    print('I am number %d in process %d' % (procnum, getpid()))
    return getpid()

if __name__ == '__main__':
    pool = multiprocessing.Pool(processes = 3)
    print(pool.map(worker, range(5)))

它将打印返回值:

I am number 0 in process 19139
I am number 1 in process 19138
I am number 2 in process 19140
I am number 3 in process 19139
I am number 4 in process 19140
[19139, 19138, 19140, 19139, 19140]

如果你熟悉map (Python 2内置的)，这应该不是太有挑战性。否则，请查看sega_Sai的链接。

注意，只需要很少的代码。(还要注意如何重用流程)。

似乎应该使用多处理。使用.apply() .apply_async()， map()方法

http://docs.python.org/library/multiprocessing.html?highlight=pool#multiprocessing.pool.AsyncResult

你可以使用ProcessPoolExecutor从函数中获取一个返回值，如下所示:

from concurrent.futures import ProcessPoolExecutor

def test(num1, num2):
    return num1 + num2

with ProcessPoolExecutor() as executor:
    feature = executor.submit(test, 2, 3)
    print(feature.result()) # 5