distributed and parallel python
ppft is a fork of Parallel Python, and is developed as part of pathos: https://github.com/uqfoundation/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 http://www.parallelpython.com for further information.
ppft is part of pathos, a python framework for heterogeneous computing.
ppft 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/ppft/issues, with a legacy list maintained at https://uqfoundation.github.io/project/pathos/query.
NOTE: ppft installs as pp. If pp is installed, it should be uninstalled before ppft is installed -- otherwise, import pp may not find the ppft fork.
pipandsetuptoolssupport- support for python 3
- enhanced serialization, using
dill.source
This version is a fork of pp-1.6.4.
The latest released version of ppft is available from::
https://pypi.org/project/ppft
pp and ppft are distributed under a BSD-like license.
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.
Probably the best way to get started is to look at the set of example scripts
in ppft.examples. You can run the test suite with python -m ppft.tests.
ppft will create and execute jobs on local workers (automatically created
using python -u -m ppft). Additionally, remote servers can be created with
ppserver (or python -m ppft.server), and then jobs can be distributed
to remote workers. See --help for more details on how to configure a server.
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
ppft in your work, please send an email (to mmckerns at uqfoundation dot org).
If you use ppft to do research that leads to publication, we ask that you
acknowledge use of ppft 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.