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+#
+# 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).
+#
+
+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().get_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()