DMLC/XGBoost is one of the most popular machine learning library for gradient boosting. It has grown from a research project incubated in academia to the most widely used gradient boosting framework in production environment. On one side, with the growth of volume and variety of data in the production environment, users are putting accordingly growing expectation to XGBoost in terms of more functions, scalability and robustness. On the other side, as an open source project which develops in a fast pace, XGBoost has been receiving contributions from many individuals and organizations around the world. Given the high expectation from the users and the increasing channels of contribution to the project, delivering the high quality software presents a challenge to the project maintainers.
A robust and efficient continuous integration (CI) infrastructure is one of the most critical solutions to address the above challenge. A CI service will monitor a open-source repository and run a suite of integration tests for every incoming contribution. This way, the CI ensures that every proposed change in the codebase is compatible with existing functionalities. Furthermore, XGBoost can enable more thorough tests with a powerful CI infrastructure to cover cases which are closer to the production environment.
Help us fund the CI infrastructure! See the state of the CI system at https://xgboost-ci.net