venv/lib/python3.10/site-packages/multiprocess/__info__.py

#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 2024 The Uncertainty Quantification Foundation.
# License: 3-clause BSD.  The full license text is available at:
#  - https://github.com/uqfoundation/multiprocess/blob/master/LICENSE
'''
-----------------------------------------------------------------
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``, **>=0.3.8**


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)
    <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.

'''

__all__ = []
__version__ = '0.70.16'
__author__ = 'Mike McKerns'

__license__ = '''
Copyright (c) 2008-2016 California Institute of Technology.
Copyright (c) 2016-2024 The Uncertainty Quantification Foundation.
All rights reserved.

This software forks the python package "multiprocessing". Licence and
copyright information for multiprocessing can be found in "COPYING".

This software is available subject to the conditions and terms laid
out below. By downloading and using this software you are agreeing
to the following conditions.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:

    - Redistributions of source code must retain the above copyright
      notice, this list of conditions and the following disclaimer.

    - Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.

    - Neither the names of the copyright holders nor the names of any of
      the contributors may be used to endorse or promote products derived
      from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

'''