如何将队列引用传递给 pool.map_async() 管理的函数?

How do you pass a Queue reference to a function managed by pool.map_async()?(如何将队列引用传递给 pool.map_async() 管理的函数?)

问题描述

我想要一个长时间运行的进程通过队列(或类似的东西)返回它的进度，我将把它提供给进度条对话框.当过程完成时，我还需要结果.此处的测试示例失败并出现 RuntimeError: Queue objects should only be shared between processes through inheritance.

I want a long-running process to return its progress over a Queue (or something similar) which I will feed to a progress bar dialog. I also need the result when the process is completed. A test example here fails with a RuntimeError: Queue objects should only be shared between processes through inheritance.

import multiprocessing, time

def task(args):
    count = args[0]
    queue = args[1]
    for i in xrange(count):
        queue.put("%d mississippi" % i)
    return "Done"

def main():
    q = multiprocessing.Queue()
    pool = multiprocessing.Pool()
    result = pool.map_async(task, [(x, q) for x in range(10)])
    time.sleep(1)
    while not q.empty():
        print q.get()
    print result.get()

if __name__ == "__main__":
    main()

我已经能够使用单独的 Process 对象(我 am 允许传递 Queue 引用)使其工作，但是我没有一个池来管理我的许多进程想启动.有什么更好的模式建议吗?

I've been able to get this to work using individual Process objects (where I am alowed to pass a Queue reference) but then I don't have a pool to manage the many processes I want to launch. Any advise on a better pattern for this?

推荐答案

以下代码似乎可以工作:

The following code seems to work:

import multiprocessing, time

def task(args):
    count = args[0]
    queue = args[1]
    for i in xrange(count):
        queue.put("%d mississippi" % i)
    return "Done"


def main():
    manager = multiprocessing.Manager()
    q = manager.Queue()
    pool = multiprocessing.Pool()
    result = pool.map_async(task, [(x, q) for x in range(10)])
    time.sleep(1)
    while not q.empty():
        print q.get()
    print result.get()

if __name__ == "__main__":
    main()

请注意，队列来自 manager.Queue() 而不是 multiprocessing.Queue().感谢 Alex 为我指明了这个方向.

Note that the Queue is got from a manager.Queue() rather than multiprocessing.Queue(). Thanks Alex for pointing me in this direction.

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