#
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# Simple example which uses a pool of workers to carry out some tasks.
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#
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# Notice that the results will probably not come out of the output
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# queue in the same in the same order as the corresponding tasks were
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# put on the input queue. If it is important to get the results back
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# in the original order then consider using `Pool.map()` or
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# `Pool.imap()` (which will save on the amount of code needed anyway).
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#
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# Copyright (c) 2006-2008, R Oudkerk
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# All rights reserved.
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#
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import time
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import random
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from multiprocessing import Process, Queue, current_process, freeze_support
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#
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# Function run by worker processes
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#
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def worker(input, output):
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for func, args in iter(input.get, 'STOP'):
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result = calculate(func, args)
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output.put(result)
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#
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# Function used to calculate result
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#
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def calculate(func, args):
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result = func(*args)
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return '%s says that %s%s = %s' % \
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(current_process().name, func.__name__, args, result)
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#
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# Functions referenced by tasks
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#
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def mul(a, b):
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time.sleep(0.5*random.random())
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return a * b
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def plus(a, b):
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time.sleep(0.5*random.random())
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return a + b
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#
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#
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#
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def test():
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NUMBER_OF_PROCESSES = 4
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TASKS1 = [(mul, (i, 7)) for i in range(20)]
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TASKS2 = [(plus, (i, 8)) for i in range(10)]
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# Create queues
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task_queue = Queue()
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done_queue = Queue()
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# Submit tasks
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for task in TASKS1:
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task_queue.put(task)
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# Start worker processes
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for i in range(NUMBER_OF_PROCESSES):
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Process(target=worker, args=(task_queue, done_queue)).start()
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# Get and print results
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print 'Unordered results:'
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for i in range(len(TASKS1)):
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print '\t', done_queue.get()
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# Add more tasks using `put()`
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for task in TASKS2:
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task_queue.put(task)
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# Get and print some more results
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for i in range(len(TASKS2)):
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print '\t', done_queue.get()
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# Tell child processes to stop
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for i in range(NUMBER_OF_PROCESSES):
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task_queue.put('STOP')
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if __name__ == '__main__':
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freeze_support()
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test()
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