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distributed and parallel python

Project description

About Ppft

ppft is a fork of Parallel Python, and is developed as part of pathos:

Parallel Python module (PP) provides an easy and efficient way to create parallel-enabled applications for SMP computers and clusters. PP module features cross-platform portability and dynamic load balancing. Thus application written with PP will parallelize efficiently even on heterogeneous and multi-platform clusters (including clusters running other application with variable CPU loads). Visit for further information.

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, with a public ticket list at

NOTE: ppft installs as pp. If pp is installed, it should be uninstalled before ppft is installed – otherwise, “import pp” will likely not find the ppft fork.

Major Changes:

  • pip and setuptools support

  • support for python 3

  • enhanced serialization, using dill.source

Current Release

This version is ppft- (a fork of pp-1.6.4).

The latest released pathos fork of PP is available from:

PP is distributed under a BSD-like license.

Development Version

You can get the latest development version with all the shiny new features at:

If you have a new contribution, please submit a pull request.


Ppft is packaged to install from source, so you must download the tarball, unzip, and run the installer:

$ tar -xvzf ppft-
$ cd ppft-
$ python build
$ python install

You will be warned of any missing dependencies and/or settings after you run the “build” step above.

Alternately, ppft can be installed with pip or easy_install:

$ pip install ppft

NOTE: ppft installs as pp. If pp is installed, it should be uninstalled before ppft is installed – otherwise, “import pp” will likely not find the ppft fork.


Ppft requires:

- python2, version >= 2.5  *or*  python3, version >= 3.1
- six, version >= 1.7.3

Optional requirements:

- setuptools, version >= 0.6
- dill, version >= 0.2.5

More Information

Probably the best way to get started is to look at the examples that are provided within PP. See pp.examples for a set of 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).


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;

Michael McKerns and Michael Aivazis,
"pathos: a framework for heterogeneous computing", 2010- ;

Please see or for further information.

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