How do you pass a Queue reference to a function managed by pool.map_async()?

Question

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()

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?

Answer 1

Making q global works...:

import multiprocessing, time

q = multiprocessing.Queue()

def task(count):
    for i in xrange(count):
        q.put("%d mississippi" % i)
    return "Done"

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

if __name__ == "__main__":
    main()

If you need multiple queues, e.g. to avoid mixing up the progress of the various pool processes, a global list of queues should work (of course, each process will then need to know what index in the list to use, but that's OK to pass as an argument;-).

Answer 2

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()

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