The LightGBM benchmark aims at providing tools and automation to compare implementations of lightgbm and other boosting-tree-based algorithms for both training and inferencing. The focus is on production use cases, and the evaluation on both model quality (validation metrics) and computing performance (training speed, compute hours, inferencing latency, etc).
The goal is to support the community of developers of LightGBM by providing tools and a methodology for evaluating new releases of LightGBM on a standard and reproducible benchmark.
Please find the full documentation of this project at microsoft.github.io/lightgbm-benchmark
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.