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
Pool class makes it easy to submit tasks to a pool of worker
multiprocess is part of
pathos, a python framework for heterogeneous computing.
multiprocess is in active development, so any user feedback, bug reports, comments,
or suggestions are highly appreciated. A list of issues is located at https://github.com/uqfoundation/multiprocess/issues, with a legacy list maintained at https://uqfoundation.github.io/project/pathos/query.
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.
This version a fork of
The latest released version of
multiprocess 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 documentation at
multiprocess.examples for a set of example
scripts. You can also run the test suite with
python -m multiprocess.tests.
Please feel free to submit a ticket on github, or ask a question on
stackoverflow (@Mike McKerns). If you would like to share how you use
multiprocess in your work, please post send an email
(to mmckerns at uqfoundation dot org).
If you use
multiprocess to do research that leads to publication, we ask that you
acknowledge use of
multiprocess 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- ; https://uqfoundation.github.io/project/pathos