Project details for peewit

Screenshot peewit 0.7

by lorenz - November 4, 2011, 19:54:09 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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peewit intends to facilitate programming, running and result examination of machine learning experiments. It does not provide any sort of ML data types, engine, or interfaces, but it give some assistance in avoiding a mess. The user is to decompose the experimetnal code into named nodes that correpsond to aspects or dimension of an experiment. Since this breakdown is readable for machines too, simple services can be precoded that help to keep track of the experimental dimensions. Further, it puts some regime on output files, helps to recover former versions, and handles simple parallelization.

Be aware that the implementation still has deficiencies: it is weakly tested, many doc-string are missing, erroneous calls are not always caught by helpful error messages and there is much left for smaller and greater amendments. At least, the examples are decorated with many explicatory comments. We do not recommend peewit at the current state unless you are curious about it. Also, after release of version 0.7 we overworked the experiment-model and then rewrote the core modules.

The core modules depend on numpy only but for full func­tion­al­i­ty you fur­ther have to pro­vide uni­son, ssh with agent, git, and matplotlib, as well as lib­svm for the ex­am­ple project. Feel free to contact us for questions.​

Changes to previous version:

semi-uniform v-cubes

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Posix
Data Formats: Agnostic
Tags: Workflow
Archive: download here

Other available revisons

Version Changelog Date

v-cube with side-cubes

May 7, 2014, 16:04:18

switched to python-3

February 11, 2013, 21:21:05

core rewrite

July 9, 2012, 23:32:20

semi-uniform v-cubes

November 4, 2011, 19:54:09

archiving services

September 18, 2010, 16:22:01

parallelization services

August 18, 2010, 14:00:21

platform-independent path handling

July 21, 2010, 15:05:31

incremental e-tree definitions

May 12, 2010, 21:44:21

self-referential descent inputs

May 4, 2010, 13:30:36

services for arg-aggregations

April 26, 2010, 11:17:50

Initial Announcement on

April 23, 2010, 23:14:51


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