问题描述
我正在尝试使用进程对象在 python 中使用工作池.每个工人(一个进程)进行一些初始化(花费大量时间),传递一系列作业(理想情况下使用 map()),并返回一些东西.除此之外,不需要任何沟通.但是,我似乎无法弄清楚如何使用 map() 来使用我的工人的 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)
是作业队列代替,还是我可以使用 map()?
Is a job queue the way to go instead, or can I use map()?
推荐答案
我建议你为此使用队列.
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这些,所有这些都从一个队列中获取工作
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)
这样的事情应该可以让您将昂贵的启动成本分摊给多个工人.
That kind of thing should allow you to amortize the expensive startup cost across multiple workers.