processing Package for using processes which mimics the threading module `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] R Oudkerk b68d788e9c58251e7b069ecf947665cb919bfe94 0.52