Renku
Renku is an open-source platform that connects the ecosystem of data, code, and compute to empower researchers to build collaborative communities. It is aimed at independent researchers and data scientists as well as labs, collaborations, and courses and workshops.
Once a Renku project is configured, anyone using it can take advantage of convenient access to data and code within pre-configured computational sessions. Projects are assembled by piecing together:
- Data connectors: connect virtually any cloud-based or on-premise
- Source code: link the source code repositories you are used to, like
- Compute environments: use one of our pre-configure environments with
Getting Started
A public instance of RenkuLab is available at https://renkulab.io. To start exploring Renku, feel free to make an account and try it out! You can follow the hands-on tutorial or visit our documentation.
Documentation
- Documentation for users
- Documentation for administrators
- Renku blog: discover what is new in Renku, for example Renku Release June 2026
- Community portal
- \"Legacy\" documentation): the
Contributing
We\'re happy to receive contributions of all kinds, whether it is an idea for a new feature, a bug report or a pull request!
Please review our contributing guidelines before submitting a pull request.
Getting in touch
There are several channels you can use to communicate with us; we monitor all of them, so your messages will always get to us, but communication will be slightly more streamlined if you pick a channel that most suits your purpose and needs.
- discourse: questions concerning
Renku is developed as an open source project by the Swiss Data Science Center in a team split between EPFL and ETHZ.
Project structure
Renku is built from several sub-repositories:
backend services providing the majority of the platform user-facing functionality.- renku-ui: web
- amalthea: k8s
Citing Renku in research papers
If you use the Renku platform for your research, please do cite our paper. Use this BibTex for the citation:
@inproceedings{NEURIPS2023_838694e9,
author = {Ro\\v{s}kar, Rok and Ramakrishnan, Chandrasekhar and Volpi, Michele and Perez-Cruz, Fernando and Gasser, Lilian and Ozdemir, Firat and Paitz, Patrick and Alisafaee, Mohammad and Fischer, Philipp and Grubenmann, Ralf and Harris, Eliza and Olevski, Tasko and Remlinger, Carl and Salamanca, Luis and Capon Garcia, Elisabet and Cavazzi, Lorenzo and Chrobasik, Jakub and Cordoba Osnas, Darlin and Degano, Alessandro and Dupre, Jimena and Johnson, Wesley and Kettner, Eike and Kinkead, Laura and Murphy, Sean D. and Thiebaut, Flora and Verscheure, Olivier},
booktitle = {Advances in Neural Information Processing Systems},
editor = {A. Oh and T. Naumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},
pages = {42161--42173},
publisher = {Curran Associates, Inc.},
title = {Renku: a platform for sustainable data science},
url = {<https://proceedings.neurips.cc/paper_files/paper/2023/file/838694e9ab6b0a193b84daaafcac0eed-Paper-Datasets_and_Benchmarks.pdf>},
volume = {36},
year = {2023}
}