REP is environment for conducting data-driven research in a consistent and reproducible way.

Main REP features include:

• unified classifiers wrapper for variety of implementations
  • TMVA
  • Sklearn
  • XGBoost
  • uBoost
  • Theanets
  • Pybrain
  • Neurolab
• parallel training of classifiers on cluster
• classification/regression reports with plots
• support for interactive plots
• grid-search algorithms with parallelized execution
• versioning of research using git
• pluggable quality metrics for classification
• meta-algorithm design (aka 'rep-lego')

We provide the docker image with REP and all it's dependencies

• install with Docker on Mac
• install with Docker on Linux
• install with docker-compose on Linux

However, if you want to install REP and all of its dependencies on your machine yourself, follow this manual:
https://github.com/yandex/rep/wiki/Installing-manually
and https://github.com/yandex/rep/wiki/Running-manually

To get started with the framework, look at the notebooks in /howto/
Notebooks in repository can be viewed (not executed) online at nbviewer: http://nbviewer.ipython.org/github/yandex/rep/tree/master/howto/
There are basic introductory notebooks (about python, IPython) and more advanced ones (about the REP itself)