better multiprocessing and multithreading in Python
multiprocess
is a fork of multiprocessing
. multiprocess
extends multiprocessing
to provide enhanced serialization, using dill
. multiprocess
leverages multiprocessing
to support the spawning of processes using the API of the Python standard library's threading
module. multiprocessing
has been distributed as part of the standard library since Python 2.6.
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.
multiprocess
enables:
- objects to be transferred between processes using pipes or multi-producer/multi-consumer queues
- objects to be shared between processes using a server process or (for simple data) shared memory
multiprocess
provides:
- equivalents of all the synchronization primitives in ``threading``
- a ``Pool`` class to facilitate submitting tasks to worker processes
- enhanced serialization, using ``dill``
The latest released version of multiprocess
is available from:
https://pypi.org/project/multiprocess
multiprocess
is distributed under a 3-clause BSD license, and is a fork of multiprocessing
.
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
can be installed with pip
::
$ pip install multiprocess
For Python 2, a C compiler is required to build the included extension module from source. Python 3 and binary installs do not require a C compiler.
multiprocess
requires:
- ``python`` (or ``pypy``), **>=3.7**
- ``setuptools``, **>=42**
- ``dill``, **>=0.3.7**
The multiprocess.Process
class follows the API of threading.Thread
.
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]
When 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
http://multiprocess.rtfd.io. Also see multiprocess.tests
for scripts that
demonstrate how multiprocess
can be used to leverge multiple processes
to execute Python in parallel. You can run the test suite with
python -m multiprocess.tests
. As multiprocess
conforms to the
multiprocessing
interface, the examples and documentation found at
http://docs.python.org/library/multiprocessing.html also apply to
multiprocess
if one will import multiprocessing as multiprocess
.
See https://github.com/uqfoundation/multiprocess/tree/master/py3.11/examples
for a set of examples that demonstrate some basic use cases and benchmarking
for running Python code in parallel. 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 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
Please see https://uqfoundation.github.io/project/pathos or http://arxiv.org/pdf/1202.1056 for further information.