LightGBM and lleaves
One tool is a compiler that speeds up the prediction phase of the other, a gradient boosting framework, making them complements that are used together.
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 lleaves
siboehm/lleaves
Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.
This project helps data scientists and machine learning engineers significantly speed up predictions from their LightGBM models. You provide a trained LightGBM model, and it outputs an optimized version that makes predictions much faster, whether for individual requests or large batches of data. It's designed for those who need low-latency or high-throughput model inference.
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