Skip to main content

ppft: distributed and parallel python

About Ppft

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/pathos-issues.html.

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 documentation is for version ppft-1.6.6.3 (a fork of pp-1.6.6).

The latest released version of ppft is available from:

https://pypi.org/project/ppft

pp and ppft are distributed under a BSD-like 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.

Installation

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

[download]
$ tar -xvzf ppft-1.6.6.3.tgz
$ cd ppft-1.6.6.3
$ python setup.py build
$ python setup.py 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.

Requirements

ppft requires:

- ``python``, **version == 2.7** or **version >= 3.5**, or ``pypy``
- ``six``, **version >= 1.7.3**

Optional requirements:

- ``setuptools``, **version >= 0.6**
- ``dill``, **version >= 0.3.3**

More Information

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).

Citation

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/pathos.html

Please see https://uqfoundation.github.io/pathos.html or http://arxiv.org/pdf/1202.1056 for further information.

Metadata

Release files for ppft 1.6.6.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for ppft 1.6.6.3
File Size Uploaded
ppft-1.6.6.3.zip 106.3 kB Details

Built distributions (wheels)

Table of built distributions (wheels) for ppft 1.6.6.3
File Interpreter ABI Platform
ppft-1.6.6.3-py3-none-any.whl Python 3 none any Details
ppft-1.6.6.3-py2-none-any.whl Python 2 none any Details

Total release size: 233.5 kB

Release files / ppft-1.6.6.3.zip

Download URL ppft-1.6.6.3.zip
Size 106.3 kB
Tags Source
SHA-256 checksum
How to use checksums
1beab076fb9452fd729a9f1183e990d016fcd8ee67252f6897f3785d5b807a22
BLAKE2b-256 checksum
How to use checksums
95a9f6fcdbba44a8366f9da431a67af04f92ba150a88d7b7ef62bfd10a77fdac
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/1.12.1 pkginfo/1.5.0.1 requests/2.18.1 setuptools/50.3.0 requests-toolbelt/0.8.0 tqdm/4.14.0 CPython/3.6.12

Release files / ppft-1.6.6.3-py3-none-any.whl

Download URL ppft-1.6.6.3-py3-none-any.whl
Size 65.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
7ee02f3eb05bbb275bf3b2e793a4920c076440f42e420fef78ed07a932a43cf0
BLAKE2b-256 checksum
How to use checksums
517be63dcf1f9b5ecd37691ee8a7029f71ddb7cafab780a60e312d913afc0f29
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/1.12.1 pkginfo/1.5.0.1 requests/2.18.1 setuptools/50.3.0 requests-toolbelt/0.8.0 tqdm/4.14.0 CPython/3.6.12

Release files / ppft-1.6.6.3-py2-none-any.whl

Download URL ppft-1.6.6.3-py2-none-any.whl
Size 62.1 kB
Tags Python 2
SHA-256 checksum
How to use checksums
e00a63a7c349a8586512ee1842b307b7729010a43da93b0d12cfb77edbea3a1c
BLAKE2b-256 checksum
How to use checksums
dba4ee59af5fe3dd734332986bfc98e4466a4937cb376a0cb224fe2559e1e678
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/1.12.1 pkginfo/1.5.0.1 requests/2.18.1 setuptools/50.3.0 requests-toolbelt/0.8.0 tqdm/4.14.0 CPython/3.6.12

Release history Release notifications | RSS feed

1.7.8

2 release files

1.7.7

2 release files

This release

1.6.6.3 This release

3 release files

1.6.6.2

1 release file

1.6.6.1

1 release file

1.6.4.9

1 release file

1.6.4.8

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page