Skip to content

ClusterJob: An automated system for painless and reproducible massive computational experiments

License

Notifications You must be signed in to change notification settings

monajemi/clusterjob

Repository files navigation

ClusterJob

Clusterjob, hereafter CJ, is an experiment management system (EMS) for data science. CJ is written mainly in perl and allows submiting computational jobs to clusters in a hassle-free and reproducible manner. CJ produces 'reporoducible' computational packages for academic publications at no-cost. CJ project started in 2013 at Stanford University by Hatef Monajemi and his PhD advisor David L. Donoho with the goal of encouraging more efficient and reproducible research paradigm. CJ is currently under active development. Current implementation allows submission of MATLAB,Python and R jobs. The code for R works partially for serial jobs only. In the future versions, we hope to include other data science programming languages such as Julia.

You can read more about CJ on http://clusterjob.org

You can find CJ book project at https://github.com/monajemi/CJ-book

key contributors:

  1. Hatef Monajemi
  2. Bekk Blando
  3. David Donoho
  4. Vardan Papyan

How to cite ClusterJob

@article{clusterjob,
Author = {H.~Monajemi and D.~L.~Donoho},
Month = March,
Url= {https://github.com/monajemi/clusterjob},
Title = {ClusterJob: An automated system for painless and reproducible massive computational experiments},
Year = 2015}


@article{MMCEP17,
title = {Making massive computational experiments painless},
author = {H.~Monajemi  and D.~L.~Donoho and V.~Stodden},
journal={Big Data (Big Data), 2016 IEEE International Conference on},
year={2017},
month={February},
}



@article{Monajemi19,
title = {Ambitious data science can be painless},
author = {H.~Monajemi and R.~Murri and E.~Yonas and P.~Liang and V.~Stodden and D.L.~Donoho},
note={arXiv:1901.08705},
year={2019},
}

Copyright 2015 Hatef Monajemi ([email protected])

About

ClusterJob: An automated system for painless and reproducible massive computational experiments

Resources

License

Stars

Watchers

Forks

Packages

No packages published