multiprocess 0.70.12

better multiprocessing and multithreading in python

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 (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.tgz
$ cd multiprocess-0.70.12
$ 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.