better multiprocessing and multithreading in python
multiprocess is a fork of
multiprocessing, and is developed as part of
multiprocessing is a package for the Python language which supports the
spawning of processes using the API of the standard library's
multiprocessing has been distributed in the standard
library since python 2.6.
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
Poolclass makes it easy to submit tasks to a pool of worker processes.
pathos is a python framework for heterogeneous computing.
pathos is in active development, so any user feedback, bug reports, comments,
or suggestions are highly appreciated. A list of known issues is maintained
at http://trac.mystic.cacr.caltech.edu/project/pathos/query, with a public
ticket list at https://github.com/uqfoundation/pathos/issues.
NOTE: A C compiler is required to build the included extension module. For python 3.3 and above, a C compiler is suggested, but not required.
- enhanced serialization, using
This version a fork of
The latest released
pathos fork of
multiprocessing is available from::
multiprocessing is distributed under a BSD license.
You can get the latest development version with all the shiny new features at:: https://github.com/uqfoundation
If you have a new contribution, please submit a pull request.
multiprocess.Process class follows the API of
For example ::
from multiprocess 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 multiprocess 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 multiprocess 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 multiprocess import Pool >>> def f(x): return x*x ... >>> p = Pool(4) >>> result = p.map_async(f, range(10)) >>> print (result.get(timeout=1)) [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
dill is installed, serialization is extended to most objects,
for example ::
>>> from multiprocess import Pool >>> p = Pool(4) >>> print (p.map(lambda x: (lambda y:y**2)(x) + x, xrange(10))) [0, 2, 6, 12, 20, 30, 42, 56, 72, 90]
Probably the best way to get started is to look at the examples that are
multiprocess. See the examples directory for a set of
example scripts. Please feel free to submit a ticket on github, or ask
a question on stackoverflow (@Mike McKerns).
pathos is an active research tool. There are a growing number of publications
and presentations that discuss real-world examples and new features of
in greater detail than presented in the user's guide. If you would like to
share how you use
pathos in your work, please post a link or send an email
(to mmckerns at uqfoundation dot org).
If you use
pathos to do research that leads to publication, we ask that you
acknowledge use of
pathos by citing the following in your publication::
M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis, "Building a framework for predictive science", Proceedings of the 10th Python in Science Conference, 2011; http://arxiv.org/pdf/1202.1056 Michael McKerns and Michael Aivazis, "pathos: a framework for heterogeneous computing", 2010- ; http://trac.mystic.cacr.caltech.edu/project/pathos
Please see http://trac.mystic.cacr.caltech.edu/project/pathos or http://arxiv.org/pdf/1202.1056 for further information.