pebble包有一个很好的利用多处理的抽象。管道，这使得这个非常简单:

from pebble import concurrent

@concurrent.process
def function(arg, kwarg=0):
    return arg + kwarg

future = function(1, kwarg=1)

print(future.result())

示例来自:https://pythonhosted.org/Pebble/#concurrent-decorators

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

您可以使用内置的exit来设置进程的退出代码。它可以从进程的exitcode属性中获得:

import multiprocessing

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

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

    result = []
    for proc in jobs:
        proc.join()
        result.append(proc.exitcode)
    print result

输出:

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

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]

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

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)>