processing

processing is a package for the Python language which supports the spawning of processes using the API of the standard library's threading module. It runs on both Unix and Windows.

Features:

• Objects can be transferred between processes using pipes or multi-producer/multi-consumer queues.

• Objects can be shared between processes using a server process or (for simple data) shared memory.

• Equivalents of all the synchronization primitives in threading are available.

• A Pool class makes it easy to submit tasks to a pool of worker processes.

Links

• Documentation <http://pyprocessing.berlios.de/doc/index.html>_
• Installation instructions <http://pyprocessing.berlios.de/doc/INSTALL.html>_
• Changelog <http://pyprocessing.berlios.de/doc/CHANGES.html>_
• Acknowledgments <http://pyprocessing.berlios.de/doc/THANKS.html>_
• BSD Licence <http://pyprocessing.berlios.de/doc/COPYING.html>_

The project is hosted at

• http://developer.berlios.de/projects/pyprocessing

The package can be downloaded from

• http://developer.berlios.de/project/filelist.php?group_id=9001 or
• http://pypi.python.org/pypi/processing

Examples

The processing.Process class follows the API of threading.Thread. For example ::

from processing import Process, Queue

def f(q):
    q.put('hello world')

if __name__ == '__main__':
    q = Queue()
    p = Process(target=f, args=[q])
    p.start()
    print q.get()
    p.join()

Synchronization primitives like locks, semaphores and conditions are available, for example ::

>>> from processing import Condition
>>> c = Condition()
>>> print c
<Condition(<RLock(None, 0)>), 0>
>>> c.acquire()
True
>>> print c
<Condition(<RLock(MainProcess, 1)>), 0>

One can also use a manager to create shared objects either in shared memory or in a server process, for example ::

>>> from processing import Manager
>>> manager = Manager()
>>> l = manager.list(range(10))
>>> l.reverse()
>>> print l
[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
>>> print repr(l)
<Proxy[list] object at 0x00E1B3B0>

Tasks can be offloaded to a pool of worker processes in various ways, for example ::

>>> from processing import Pool
>>> def f(x): return x*x
...
>>> p = Pool(4)
>>> result = p.mapAsync(f, range(10))
>>> print result.get(timeout=1)
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]