CODEWITHSUNDEEP
pythonmultiprocessing
Felix
Felix

Reputation: 2154

python Pool with worker Processes

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)

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

Upvotes: 36

Views: 45546

Answers (3)

Charly Empereur-mot
Charly Empereur-mot

Reputation: 661

Since python 3.3 you can use starmap, also for using multiple arguments AND getting back the results in a very simplistic syntax:

import multiprocessing

nb_cores = multiprocessing.cpu_count()

def caps(nb, letter):
    print('Exec nb:', nb)
    return letter.upper()

if __name__ == '__main__':

    multiprocessing.freeze_support() # for Windows, also requires to be in the statement: if __name__ == '__main__'

    input_data = ['a','b','c','d','e','f','g','h']
    input_order = [1,2,3,4,5,6,7,8,9]

    with multiprocessing.Pool(processes=nb_cores) as pool: # auto closing workers
        results = pool.starmap(caps, zip(input_order, input_data))

    print(results)

Upvotes: 2

S.Lott
S.Lott

Reputation: 391854

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

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)

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

Upvotes: 68

jfs
jfs

Reputation: 414205

initializer expects an arbitrary callable that does initilization e.g., it can set some globals, not a Process subclass; map accepts an arbitrary iterable:

#!/usr/bin/env python
import multiprocessing as mp

def init(val):
    print('do some initialization here')

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

def produce_data():
    yield -100
    for i in range(10):
        yield i
    yield 100

if __name__=="__main__":
  p = mp.Pool(initializer=init, initargs=('arg',))
  print(p.map(compute, produce_data()))

Upvotes: 7

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