multiprocess
====
a friendly fork of the multiprocessing module

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.


Pathos is a python framework for heterogeneous computing.
Pathos is in active development, so any user feedback, bug reports, comments,
or suggestions are highly appreciated.  A list of known issues is maintained
at http://trac.mystic.cacr.caltech.edu/project/pathos/query, with a public
ticket list at https://github.com/uqfoundation/pathos/issues.

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 version is multiprocess-0.70.3 (a fork of multiprocessing-0.70a1).

The latest released pathos fork of multiprocessing is available from::
    https://pypi.python.org/pypi/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.


Examples
--------
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 examples that are
provided within multiprocess.  See the examples directory for a set of
example scripts.  Please feel free to submit a ticket on github, or ask
a question on stackoverflow (@Mike McKerns).

Pathos is an active research tool. There are a growing number of publications
and presentations that discuss real-world examples and new features of pathos
in greater detail than presented in the user's guide.  If you would like to
share how you use pathos in your work, please post a link or send an email
(to mmckerns at caltech dot edu).


Citation
--------
If you use pathos to do research that leads to publication, we ask that you
acknowledge use of pathos 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- ;
    http://trac.mystic.cacr.caltech.edu/project/pathos

Please see http://trac.mystic.cacr.caltech.edu/project/pathos or
http://arxiv.org/pdf/1202.1056 for further information.
