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https://github.com/uqfoundation/multiprocess
better multiprocessing and multithreading in Python
https://github.com/uqfoundation/multiprocess
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better multiprocessing and multithreading in Python
- Host: GitHub
- URL: https://github.com/uqfoundation/multiprocess
- Owner: uqfoundation
- License: other
- Created: 2015-06-20T15:45:09.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2024-05-14T20:11:40.000Z (4 months ago)
- Last Synced: 2024-05-15T16:26:55.307Z (4 months ago)
- Language: Python
- Homepage: http://multiprocess.rtfd.io
- Size: 1.73 MB
- Stars: 586
- Watchers: 19
- Forks: 64
- Open Issues: 35
-
Metadata Files:
- Readme: README.md
- License: COPYING
Awesome Lists containing this project
- awesomeLibrary - multiprocess - better multiprocessing and multithreading in Python. (语言资源库 / python)
README
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
[](https://pepy.tech/project/multiprocess)
[](https://anaconda.org/conda-forge/multiprocess)
[](https://stackoverflow.com/questions/tagged/multiprocess)
---------------
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
[](http://www.uqfoundation.org/pages/donate.html)
[](https://multiprocess.readthedocs.io/en/latest/?badge=latest)
[](https://travis-ci.com/github/uqfoundation/multiprocess)
[](https://codecov.io/gh/uqfoundation/multiprocess)
-------------------
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``, **>=0.3.8**
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)
), 0>
>>> c.acquire()
True
>>> print (c)
), 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))
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.