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better multiprocessing and multithreading in python

Project description

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), ==2.7 or >=3.7

  • setuptools, >=42

  • dill, >=0.3.5.1

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 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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