#3737th of 56 in ML frameworks and libraries
lightgbm
Gradient boosting library for tree-based models on tabular data
- Stars
- 18.8k
- License
- MIT
- Last commit
- Oct 2026
- Last release
- Jul 2026
- Language
- C++
Missed the latest GitHub check; these facts may be older.
Overview
LightGBM is a gradient boosting framework using tree-based learning, written in C++ with Python, R and command-line interfaces, plus a SWIG wrapper. It supports parallel, distributed and GPU training and targets large tabular datasets. The project moved from Microsoft/LightGBM to lightgbm-org in March 2026.
Who it is for: Data scientists and ML engineers training models on tabular data
Strengths
- Parallel, distributed and GPU training are supported, with a guide for each
- Official packages on PyPI, conda-forge, CRAN and NuGet, plus a CLI
- Many listed integrations: Spark, Ray, Kubernetes, ONNX, PMML, AutoML tuners
- MIT license with exhaustive parameter and feature documentation
Weaknesses
- Covers gradient boosted trees only, not neural networks or general tensor work
- Most integrations and extra language bindings are unofficial, not team-maintained
- README gives no RAM or GPU sizing, so hardware needs are unknown
- Org move in March 2026 means older links may point to the old location
What it needs
- GPU optional
- Models: gradient boosting decision trees
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