# Install lightgbm with Homebrew, MacPorts, Nix, winget Fast, distributed, high performance gradient boosting framework. Version 4.6.0 via Homebrew; verified 2026-06-15. ## Install ```sh sudo av install brew:lightgbm ``` Additional install commands: ### macOS - Homebrew (100%): ```sh brew install lightgbm ``` Evidence: local Homebrew formula metadata - MacPorts (94%): ```sh sudo port install LightGBM ``` Evidence: MacPorts ports tree: math/LightGBM/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1 ### Linux - Nix (92%): ```sh nix profile install nixpkgs#lightgbm ``` Evidence: nixpkgs package indexes: pkgs/by-name/li/lightgbm/package.nix from https://api.github.com/repos/NixOS/nixpkgs/git/trees/master?recursive=1 ### Windows - winget (92%): ```sh winget install --id Microsoft.LightGBM -e ``` Evidence: Windows Package Manager source index: Microsoft.LightGBM from https://cdn.winget.microsoft.com/cache/source.msix ## Package facts - **Package key:** brew:lightgbm - **Package manager:** Homebrew - **Package manager page:** - **Version:** 4.6.0 - **Source summary:** Fast, distributed, high performance gradient boosting framework - **Homepage:** - **Repository:** - **Upstream docs:** - **License:** MIT - **Source archive:** - **Last updated:** 2026-06-15T10:20:20-04:00 - **Generated:** 2026-07-08T07:18:31+00:00 ## Executables - lightgbm (cli) - lightgbm (alias) ## Dependencies - libomp ## Build dependencies - cmake ## Install behavior - Post-install hook: not defined - Bottle: available on arm64_linux, arm64_sequoia, arm64_sonoma, arm64_tahoe, sonoma, x86_64_linux ## Freshness - Page generated: 2026-07-08 - Package-manager version: 4.6.0 - Package-manager updated: 2026-06-15 - Local data: ok - Upstream repository: https://github.com/lightgbm-org/LightGBM - info: No cached GitHub release or tag data was available. ## Project history and usage LightGBM, short for Light Gradient Boosting Machine, is a high-performance gradient boosting framework for tree-based learning. It became one of the standard packages for tabular machine learning because it combines fast histogram-based training, low memory use, categorical-feature handling, and parallel or distributed execution. ### Project history The GitHub repository was created on 2016-08-05. The README describes LightGBM as a gradient boosting framework designed for faster training, lower memory usage, better accuracy, parallel and distributed learning, GPU learning, and large-scale data. The official papers connect the implementation to Microsoft Research work on communication-efficient parallel decision trees in 2016 and the 2017 NIPS paper "LightGBM: A Highly Efficient Gradient Boosting Decision Tree." The paper introduced Gradient-based One-Side Sampling and Exclusive Feature Bundling as key techniques for speeding up GBDT training. The repository moved from Microsoft/LightGBM to lightgbm-org/LightGBM in March 2026. The maintainers documented the move in issue 7187 and stated that the same maintainers, including the creator of LightGBM, continued managing the official source repository. ### Adoption history The README says LightGBM has been widely used in winning machine-learning competition solutions. Its package footprint spans command-line binaries, Python, R, conda, CRAN, NuGet, Winget, Homebrew, and downstream integrations such as Spark-oriented wrappers and inference converters. LightGBM's adoption followed a practical need in tabular-data workflows: teams wanted XGBoost-class accuracy with faster training iterations and better memory behavior on large datasets. ### How it is used Users train models from the CLI or language bindings for regression, classification, ranking, and large-scale distributed tasks. Common package-manager use cases include installing the CLI for experiments, installing Python or R bindings for notebooks and pipelines, and installing the library as a dependency of higher-level ML systems. ### Why package nerds care LightGBM is package-nerd significant because it is a research system that became packaging infrastructure: native C++, Python wheels, R packages, GPU builds, distributed modes, and many downstream wrappers all have to agree on the same fast tree learner. It is also a canonical example of ML packaging complexity, where one upstream project must serve CLI users, language-binding users, GPU users, and distro maintainers without losing performance-sensitive native code paths. ### Timeline - 2016-08-05: GitHub repository created. - 2016: NIPS paper on communication-efficient parallel decision trees published by LightGBM authors. - 2017-02-27: Official experiment documentation records the first version of comparison and parallel experiments. - 2017: NIPS paper "LightGBM: A Highly Efficient Gradient Boosting Decision Tree" published. - 2018: GPU acceleration paper cited by the README. - 2020-03-08: Official experiment documentation updated against a then-new master branch. - 2022: Quantized training paper cited by the README. - 2026-03: Repository moved from Microsoft/LightGBM to lightgbm-org/LightGBM. ### Related projects - Related projects include XGBoost, scikit-learn integrations, SynapseML, FLAML, Optuna, Treelite, SHAP, ML.NET, ONNX conversion tools, and many language bindings listed by the LightGBM README. ### Sources - - - - - - - - ## Security Notes narrow executable package without higher-risk signals. - **Geiger risk:** green / low - narrow executable package without higher-risk signals ## Source Database Details - **Source Database:** Homebrew formula API - **Tap:** homebrew/core - **Full Name:** lightgbm - **Version Scheme:** 0 - **Revision:** 0 - **Bottle Stable Root URL:** - **Deprecated:** no - **Disabled:** no - **Keg Only:** no - **URL Keys:** stable ## Other Package-Manager Records - Nix - lightgbm: normalized package name match | nixpkgs package indexes: pkgs/by-name/li/lightgbm/package.nix from https://api.github.com/repos/NixOS/nixpkgs/git/trees/master?recursive=1 - MacPorts - LightGBM: normalized package name match | MacPorts ports tree: math/LightGBM/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1 - winget - Microsoft.LightGBM: normalized package name match | Windows Package Manager source index: Microsoft.LightGBM from https://cdn.winget.microsoft.com/cache/source.msix ## Related links - [Terminal utility packages](https://www.automicvault.com/pkg/terminal-utilities/) - Matched terminal and command-line workflow metadata. - [Text processing packages](https://www.automicvault.com/pkg/text-processing-tools/) - Matched text, document, or structured-data processing metadata. - [Networking and protocol packages](https://www.automicvault.com/pkg/networking-protocol-tools/) - Matched network, protocol, or remote-service metadata. - [Database and data packages](https://www.automicvault.com/pkg/database-data-tools/) - Matched database, SQL, migration, or data-store metadata. - [cmake](https://www.automicvault.com/pkg/brew/cmake/) - Build dependency declared by Homebrew. - [classifier](https://www.automicvault.com/pkg/brew/classifier/) - Shares av.db curated category or tags: cli, data, machine-learning. - [djl-serving](https://www.automicvault.com/pkg/brew/djl-serving/) - Shares av.db curated category or tags: cli, data, machine-learning. - [mallet](https://www.automicvault.com/pkg/brew/mallet/) - Shares av.db curated category or tags: cli, data, machine-learning. - [ncnn](https://www.automicvault.com/pkg/brew/ncnn/) - Shares av.db curated category or tags: cli, data, machine-learning. - [rgf](https://www.automicvault.com/pkg/brew/rgf/) - Shares av.db curated category or tags: cli, data, machine-learning. - [vowpal-wabbit](https://www.automicvault.com/pkg/brew/vowpal-wabbit/) - Shares av.db curated category or tags: cli, data, machine-learning. - [mitie](https://www.automicvault.com/pkg/brew/mitie/) - Shares av.db curated category or tags: cli, data, machine-learning. - [sentencepiece](https://www.automicvault.com/pkg/brew/sentencepiece/) - Shares av.db curated category or tags: cli, data, machine-learning. ## Combined YAML source View the package source record on GitHub. [combined/lightgbm.yml](https://github.com/automic-vault/db/blob/main/combined/lightgbm.yml) ## Sources - Nucleus package database - Geiger risk classifier - package-page enrichment - curated package history - package version freshness - av.db category and tag curation - package relationship graph - external package-manager database matches - cross-ecosystem install command graph