# Installer lightgbm avec Homebrew, Nix, MacPorts, winget Consultez les chemins d'installation, exécutables, métadonnées et notes de sécurité de lightgbm pour les workflows d'agents IA. ## installation ```sh sudo av install brew:lightgbm ``` Commandes d'installation supplémentaires: ### macOS - Homebrew (100%): ```sh brew install lightgbm ``` Preuve: local Homebrew formula metadata - MacPorts (94%): ```sh sudo port install LightGBM ``` Preuve: 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 ``` Preuve: 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 ``` Preuve: Windows Package Manager source index: Microsoft.LightGBM from https://cdn.winget.microsoft.com/cache/source.msix ## Faits du paquet - **Clé du paquet:** brew:lightgbm - **Gestionnaire de paquets:** Homebrew - **Version:** 4.7.0 - **Résumé source:** Fast, distributed, high performance gradient boosting framework - **Page d'accueil:** - **Dépôt:** - **Dernière mise à jour:** 2026-07-18T21:02:43Z - **Généré:** 2026-08-03T19:37:03+00:00 ## exécutables - lightgbm (alias) ## Comportement d'installation - Bouteille: non disponible ## Version et fraîcheur - page générée: 2026-08-03 - version du gestionnaire: 4.7.0 ## Historique du projet et usages 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. ### Historique du projet 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. ### Historique d'adoption 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. ### Modes d'utilisation 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. ### Pourquoi les passionnés de paquets s'y intéressent 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. ### Chronologie - 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 - - - - - - - - ## Notes de sécurité narrow executable package without higher-risk signals. - **Risque Geiger:** vert / faible - narrow executable package without higher-risk signals ## Autres enregistrements de gestionnaires de paquets - 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 ## Combined YAML source View the package source record on GitHub. [combined/lightgbm.yml](https://github.com/mxcl/pkgdb/blob/main/combined/lightgbm.yml) ## Sources - pkg.so package database - Geiger risk classifier - curated package history - pkgdb category and tag curation - external package-manager database matches - cross-ecosystem install command graph