LightGBM and GBM-perf
One tool is a fast, distributed gradient boosting framework, while the other is a project for evaluating the performance of various open-source gradient boosting implementations; therefore, they are complements, as the latter can be used to benchmark and understand the former.
About LightGBM
lightgbm-org/LightGBM
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
LightGBM is a powerful tool for anyone building predictive models. It takes your raw data, learns patterns from it, and generates highly accurate predictions for tasks like ranking items, classifying customers, or forecasting trends. Data scientists and machine learning engineers use LightGBM to quickly develop high-performing models, even with very large datasets.
About GBM-perf
szilard/GBM-perf
Performance of various open source GBM implementations
This project helps data scientists and machine learning engineers understand the real-world performance of popular gradient boosting machine (GBM) implementations. It compares training times and accuracy (AUC) of tools like H2O, XGBoost, LightGBM, and CatBoost on large datasets, using both CPU and GPU hardware configurations. You can use this to make informed decisions about which GBM library will perform best for your specific predictive modeling tasks.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work