lightgbm
LightGBM Python-package
Decision gist · record as of 2026-08-14
Yes. LightGBM is a mature, actively maintained gradient boosting framework in the top 1000 PyPI packages with no known vulnerabilities. Install friction is moderate but manageable with clear platform-specific setup documented. The only caveat is verifying the license terms before use in restricted contexts, as the license treatment is currently unclear in the metadata.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires 64-bit Python (32-bit not supported).
- macOS users must install OpenMP via brew install libomp.
- Windows users need VC runtime if Visual Studio is not installed.
License · maintenance · safety
(unclear) — License treatment is unclear—no SPDX identifier or raw license text provided in metadata. Verify the actual license terms in the repository before use in proprietary or restricted contexts.
last release 2026-07-18 (27 days) · last repo commit 2026-08-11 · 18,683 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 29,396,241 downloads/mo, #815 on PyPI
Alternatives
Verify before relying
pip install lightgbm
import lightgbm as lgb
model = lgb.LGBMClassifier()
model.fit(X_train, y_train)- Exact license identifier and terms (license_treatment is 'unclear')
- Whether narwhals, numpy, scipy versions have known compatibility constraints
- Performance characteristics and typical use-case scale limits
What it is and what it does
LightGBM is a production-grade gradient boosting library that trains tree-based models for classification, regression, and ranking. It depends on narwhals, numpy, and scipy and is designed to be fast and memory-efficient compared to traditional boosting approaches. The package ships with pre-compiled wheels supporting CPU and GPU acceleration on Windows and Linux, and CPU-only on macOS.
The library is actively maintained (latest release 2026-07-18) and supports modern Python versions from 3.10 through 3.14. Installation requires platform-specific setup: macOS needs OpenMP, Windows needs VC runtime, and all platforms need 64-bit Python. Optional extras like [arrow], [polars], [dask], [pandas], [plotting], and [scikit-learn] provide integration with popular data and ML ecosystems.
Use it for
- Train fast gradient boosting models for tabular classification or regression tasks in production pipelines
- Build ranking models for information retrieval or recommendation systems with GPU acceleration
- Integrate with pandas, polars, or PyArrow dataframes for data preprocessing and feature engineering workflows
- Distribute training across multiple machines using the Dask integration for large-scale datasets
- Create interpretable tree-based models with built-in feature importance and plotting capabilities
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
LightGBM is a mature, actively maintained gradient boosting framework in the top 1000 PyPI packages with no known vulnerabilities. Install friction is moderate but manageable with clear platform-specific setup documented. The only caveat is verifying the license terms before use in restricted contexts, as the license treatment is currently unclear in the metadata.
Install
lightgbm on PyPI
Before you install
Medium install friction due to compiled wheels for multiple platforms (macOS x86_64/arm64, Linux aarch64/x86_64, Windows). Active maintenance with release 27 days old and last commit 2026-08-11. Requires 64-bit Python and platform-specific runtime libraries (OpenMP on macOS, VC runtime on Windows).
Requires 64-bit Python (32-bit not supported). macOS users must install OpenMP via brew install libomp. Windows users need VC runtime if Visual Studio is not installed.
License in practice
License treatment is unclear—no SPDX identifier or raw license text provided in metadata. Verify the actual license terms in the repository before use in proprietary or restricted contexts.
Quickstart
pip install lightgbm
import lightgbm as lgb
model = lgb.LGBMClassifier()
model.fit(X_train, y_train)Verify before relying
- Exact license identifier and terms (license_treatment is 'unclear')
- Whether narwhals, numpy, scipy versions have known compatibility constraints
- Performance characteristics and typical use-case scale limits
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagesnarwhalsnumpyscipy |
| Maintenance | Actively maintained 27 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 29,396,241 / month, #815 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Science/ResearchNatural Language :: EnglishOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: lightgbm-4.7.0-py3-none-macosx_10_15_x86_64.whl; lightgbm-4.7.0-py3-none-macosx_12_0_arm64.whl; lightgbm-4.7.0-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; lightgbm-4.7.0-py3-none-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; lightgbm-4.7.0-py3-none-win_amd64.whl
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