-----------------------------------------------------------------
multiprocess: better multiprocessing and multithreading in Python
-----------------------------------------------------------------
About Multiprocess
==================
``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.
Major Features
==============
``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``
Current Release
===============
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``.
Development Version
===================
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.
Installation
============
``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.
Requirements
============
``multiprocess`` requires:
- ``python`` (or ``pypy``), **>=3.8**
- ``setuptools``, **>=42**
- ``dill``, **>=0.3.9**
Basic Usage
===========
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]
More Information
================
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/py3.12/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**).
Citation
========
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.
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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.\n\n\nMajor Features\n==============\n\n``multiprocess`` enables:\n\n - objects to be transferred between processes using pipes or multi-producer/multi-consumer queues\n - objects to be shared between processes using a server process or (for simple data) shared memory\n\n``multiprocess`` provides:\n\n - equivalents of all the synchronization primitives in ``threading``\n - a ``Pool`` class to facilitate submitting tasks to worker processes\n - enhanced serialization, using ``dill``\n\n\nCurrent Release\n===============\n\nThe latest released version of ``multiprocess`` is available from:\n\n https://pypi.org/project/multiprocess\n\n``multiprocess`` is distributed under a 3-clause BSD license, and is a fork of ``multiprocessing``.\n\n\nDevelopment Version\n===================\n\nYou can get the latest development version with all the shiny new features at:\n\n https://github.com/uqfoundation\n\nIf you have a new contribution, please submit a pull request.\n\n\nInstallation\n============\n\n``multiprocess`` can be installed with ``pip``::\n\n $ pip install multiprocess\n\nFor 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.\n\n\nRequirements\n============\n\n``multiprocess`` requires:\n\n - ``python`` (or ``pypy``), **>=3.8**\n - ``setuptools``, **>=42**\n - ``dill``, **>=0.3.9**\n\n\nBasic Usage\n===========\n\nThe ``multiprocess.Process`` class follows the API of ``threading.Thread``.\nFor example ::\n\n from multiprocess import Process, Queue\n\n def f(q):\n q.put('hello world')\n\n if __name__ == '__main__':\n q = Queue()\n p = Process(target=f, args=[q])\n p.start()\n print (q.get())\n p.join()\n\nSynchronization primitives like locks, semaphores and conditions are\navailable, for example ::\n\n >>> from multiprocess import Condition\n >>> c = Condition()\n >>> print (c)\n <Condition(<RLock(None, 0)>), 0>\n >>> c.acquire()\n True\n >>> print (c)\n <Condition(<RLock(MainProcess, 1)>), 0>\n\nOne can also use a manager to create shared objects either in shared\nmemory or in a server process, for example ::\n\n >>> from multiprocess import Manager\n >>> manager = Manager()\n >>> l = manager.list(range(10))\n >>> l.reverse()\n >>> print (l)\n [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]\n >>> print (repr(l))\n <Proxy[list] object at 0x00E1B3B0>\n\nTasks can be offloaded to a pool of worker processes in various ways,\nfor example ::\n\n >>> from multiprocess import Pool\n >>> def f(x): return x*x\n ...\n >>> p = Pool(4)\n >>> result = p.map_async(f, range(10))\n >>> print (result.get(timeout=1))\n [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n\nWhen ``dill`` is installed, serialization is extended to most objects,\nfor example ::\n\n >>> from multiprocess import Pool\n >>> p = Pool(4)\n >>> print (p.map(lambda x: (lambda y:y**2)(x) + x, xrange(10)))\n [0, 2, 6, 12, 20, 30, 42, 56, 72, 90]\n\n\nMore Information\n================\n\nProbably the best way to get started is to look at the documentation at\nhttp://multiprocess.rtfd.io. Also see ``multiprocess.tests`` for scripts that\ndemonstrate how ``multiprocess`` can be used to leverge multiple processes\nto execute Python in parallel. You can run the test suite with\n``python -m multiprocess.tests``. As ``multiprocess`` conforms to the\n``multiprocessing`` interface, the examples and documentation found at\nhttp://docs.python.org/library/multiprocessing.html also apply to\n``multiprocess`` if one will ``import multiprocessing as multiprocess``.\nSee https://github.com/uqfoundation/multiprocess/tree/master/py3.12/examples\nfor a set of examples that demonstrate some basic use cases and benchmarking\nfor running Python code in parallel. Please feel free to submit a ticket on\ngithub, or ask a question on stackoverflow (**@Mike McKerns**). 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