Codabench is an open-source web-based platform that enables researchers, developers, and data scientists to collaborate, with the goal of advancing research fields where machine learning and advanced computation is used. Codabench helps to solve many common problems in the arena of data-oriented research through its online community where people can share worksheets and participate in competitions and benchmarks. It can be seen as a version 2 of CodaLab Competitions.
To see Codabench in action, visit codabench.org.
To participate, or even organize your own benchmarks or competitions, you don't need to install anything, you just need to sign in an instance of the platform (e.g. this one). If you wish to configure your own instance of Codabench platform, here are the instructions:
$ cp .env_sample .env
$ docker-compose up -d
$ docker-compose exec django ./manage.py migrate
$ docker-compose exec django ./manage.py generate_data
$ docker-compose exec django ./manage.py collectstatic --noinput
You can now login as username "admin" with password "admin" at http://localhost/
If you ever need to reset the database, use the script ./reset_db.sh
For more information about installation, checkout Codabench Basic Installation Guide and How to Deploy Server.
Copyright (c) 2020-2022, Université Paris-Saclay. This software is released under the Apache License 2.0 (the "License"); you may not use the software except in compliance with the License.
The text of the Apache License 2.0 can be found online at: http://www.opensource.org/licenses/apache2.0.php
@article{codabench,
title = {Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform},
author = {Zhen Xu and Sergio Escalera and Adrien Pavão and Magali Richard and
Wei-Wei Tu and Quanming Yao and Huan Zhao and Isabelle Guyon},
journal = {Patterns},
volume = {3},
number = {7},
pages = {100543},
year = {2022},
issn = {2666-3899},
doi = {https://doi.org/10.1016/j.patter.2022.100543},
url = {https://www.sciencedirect.com/science/article/pii/S2666389922001465}
}