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