PT Journal
AU Zhen Xu
   Sergio Escalera
   Adrien Pavao
   Magali Richard
   Wei-Wei Tu
   Quanming Yao
   Huan Zhao
   Isabelle Guyon
TI Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform
SO Patterns
JI PATTERNS
PY 2022
BP 100543
VL 3
IS 7
DI 10.1016/j.patter.2022.100543
DE Machine learning; data science; benchmark platform; reproducibility; competitions
AB Obtaining a standardized benchmark of computational methods is a major issue in data-science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here, we introduce Codabench, a meta-benchmark platform that is open sourced and community driven for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench is open to everyone free of charge and allows benchmark organizers to fairly compare submissions under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating easy organization of flexible and reproducible benchmarks, such as the possibility of reusing 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 2,500 submissions. As illustrative use cases, we introduce four diverse benchmarks covering graph machine learning, cancer heterogeneity, clinical diagnosis, and reinforcement learning.
ER