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Obtaining standardized crowdsourced benchmark of computational methods is amajor issue in data science communities. Dedicated frameworks enabling fairbenchmarking in a unified environment are yet to be developed. Here weintroduce Codabench, an open-source, community-driven platform for benchmarkingalgorithms or software agents versus datasets or tasks. A public instance ofCodabench (https://www.codabench.org/) is open to everyone, free of charge, andallows benchmark organizers to compare fairly submissions, under the samesetting (software, hardware, data, algorithms), with custom protocols and dataformats. Codabench has unique features facilitating the organization ofbenchmarks flexibly, easily and reproducibly, such as the possibility ofre-using templates of benchmarks, and supplying compute resources on-demand.Codabench has been used internally and externally on various applications,receiving more than 130 users and 2500 submissions. As illustrative use cases,we introduce 4 diverse benchmarks covering Graph Machine Learning, CancerHeterogeneity, Clinical Diagnosis and Reinforcement Learning.

Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform