带有工作进程的python池 [英] python Pool with worker Processes

问题描述

我正在尝试使用Process对象在python中使用工作池.每个工作程序(一个进程)都进行一些初始化(花费很短的时间)，传递一系列的工作(理想情况下使用map())，然后返回一些内容.除此之外，没有必要进行任何沟通.但是，我似乎无法弄清楚如何使用map()使用工人的compute()函数.

I am trying to use a worker Pool in python using Process objects. Each worker (a Process) does some initialization (takes a non-trivial amount of time), gets passed a series of jobs (ideally using map()), and returns something. No communication is necessary beyond that. However, I can't seem to figure out how to use map() to use my worker's compute() function.

from multiprocessing import Pool, Process

class Worker(Process):
    def __init__(self):
        print 'Worker started'
        # do some initialization here
        super(Worker, self).__init__()

    def compute(self, data):
        print 'Computing things!'
        return data * data

if __name__ == '__main__':
    # This works fine
    worker = Worker()
    print worker.compute(3)

    # workers get initialized fine
    pool = Pool(processes = 4,
                initializer = Worker)
    data = range(10)
    # How to use my worker pool?
    result = pool.map(compute, data)

是要代替工作队列吗?还是可以使用map()?

Is a job queue the way to go instead, or can I use map()?

推荐答案

我建议您为此使用一个队列.

I would suggest that you use a Queue for this.

class Worker(Process):
    def __init__(self, queue):
        super(Worker, self).__init__()
        self.queue = queue

    def run(self):
        print('Worker started')
        # do some initialization here

        print('Computing things!')
        for data in iter(self.queue.get, None):
            # Use data

现在您可以开始一堆了，所有这些工作都从一个队列中完成

Now you can start a pile of these, all getting work from a single queue

request_queue = Queue()
for i in range(4):
    Worker(request_queue).start()
for data in the_real_source:
    request_queue.put(data)
# Sentinel objects to allow clean shutdown: 1 per worker.
for i in range(4):
    request_queue.put(None) 

这种事情应该可以让您分摊多名员工的昂贵启动成本.

That kind of thing should allow you to amortize the expensive startup cost across multiple workers.

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