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Metadata-Version: 2.1
Name: multiprocess
Version: 0.70.12.2
Summary: better multiprocessing and multithreading in python
Home-page: https://github.com/uqfoundation/multiprocess
Author: Mike McKerns
Maintainer: Mike McKerns
License: BSD
Download-URL: https://github.com/uqfoundation/multiprocess/releases/download/multiprocess-0.70.12.2/multiprocess-0.70.12.2.tar.gz
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Requires-Dist: dill (>=0.3.4)
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multiprocess: better multiprocessing and multithreading in python
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About Multiprocess
====================
``multiprocess`` is a fork of ``multiprocessing``, and is developed as part of ``pathos``:
https://github.com/uqfoundation/pathos
``multiprocessing`` is a package for the Python language which supports the
spawning of processes using the API of the standard library's
``threading`` module. ``multiprocessing`` has been distributed in the standard
library since python 2.6.
Features:
- 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 ``threading`` are available.
- A ``Pool`` class makes it easy to submit tasks to a pool of worker processes.
``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.
Major Changes
==============
- enhanced serialization, using ``dill``
Current Release
===============
This documentation is for version ``multiprocess-0.70.12.2`` (a fork of ``multiprocessing-0.70a1``).
The latest released version of ``multiprocess`` is available from::
https://pypi.org/project/multiprocess
``Multiprocessing`` is distributed under a BSD license.
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`` is packaged to install from source, so you must
download the tarball, unzip, and run the installer::
[download]
$ tar -xvzf multiprocess-0.70.12.2.tgz
$ cd multiprocess-0.70.12.2
$ python setup.py build
$ python setup.py install
You will be warned of any missing dependencies and/or settings
after you run the "build" step above.
Alternately, ``multiprocess`` can be installed with ``pip`` or ``easy_install``::
$ pip install multiprocess
NOTE: A C compiler is required to build the included extension module from source. For python 3.3 and above, a C compiler is suggested, but not required. Binary installs do not require a C compiler.
Requirements
============
``multiprocess`` requires::
- ``python``, **version == 2.7** or **version >= 3.6**, or ``pypy``
- ``dill``, **version >= 0.3.4**
Optional requirements::
- ``setuptools``, **version >= 0.6**
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. See ``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**).
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.