# Installer vowpal-wabbit avec Homebrew, Nix, apt, MacPorts Consultez les chemins d'installation, exécutables, métadonnées et notes de sécurité de vowpal-wabbit pour les workflows d'agents IA. ## installation ```sh sudo av install brew:vowpal-wabbit ``` Commandes d'installation supplémentaires: ### macOS - Homebrew (100%): ```sh brew install vowpal-wabbit ``` Preuve: local Homebrew formula metadata - MacPorts (94%): ```sh sudo port install vowpal_wabbit ``` Preuve: MacPorts ports tree: math/vowpal_wabbit/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1 ### Linux - Nix (92%): ```sh nix profile install nixpkgs#vowpal-wabbit ``` Preuve: nixpkgs package indexes: pkgs/by-name/vo/vowpal-wabbit/package.nix from https://api.github.com/repos/NixOS/nixpkgs/git/trees/master?recursive=1 - Ubuntu apt (92%): ```sh sudo apt install vowpal-wabbit ``` Preuve: Ubuntu 24.04 LTS package indexes: vowpal-wabbit from https://archive.ubuntu.com/ubuntu/dists/noble/universe/binary-amd64/Packages.gz ## Faits du paquet - **Clé du paquet:** brew:vowpal-wabbit - **Gestionnaire de paquets:** Homebrew - **Version:** 9.11.2 - **Résumé source:** Online learning algorithm - **Page d'accueil:** - **Dépôt:** - **Dernière mise à jour:** 2026-06-15T10:21:23-04:00 - **Généré:** 2026-08-03T19:37:03+00:00 ## exécutables - active_interactor (alias) - csv2vw (alias) - csv2vw.orig (alias) - logistic (alias) - spanning_tree (alias) - version_number.py (alias) - vw (alias) - vw-audit-pp (alias) - vw-convergence (alias) - vw-csv2bin (alias) - vw-doc2lda (alias) - vw-experiment (alias) - vw-format.pl (alias) - vw-hyperopt.py (alias) - vw-hypersearch (alias) - vw-lda (alias) - vw-regr (alias) - vw-top-errors (alias) - vw-varinfo (alias) - vw2csv (alias) ## Comportement d'installation - Bouteille: non disponible ## Version et fraîcheur - page générée: 2026-08-03 - version du gestionnaire: 9.11.2 ## Historique du projet et usages Vowpal Wabbit, usually abbreviated VW, is an open-source machine-learning system for fast online, active, and interactive learning. It is especially associated with reductions, feature hashing, out-of-core learning, allreduce-style distributed training, contextual bandits, and reinforcement-learning-adjacent production experimentation. The package occupies a distinctive niche: it is not a general deep-learning framework, but a performance-oriented command-line and library toolkit for learning from streams, very large sparse feature spaces, and partial-feedback decision data. ### Historique du projet The official Vowpal Wabbit research page says the project was created after an internal Yahoo! Research contest in 2007. It performed well in that setting and then became an active open-source project focused on online interactive learning. John Langford led the project from its Yahoo! Research origins into its Microsoft Research era. Langford's VW project page describes it as a project started at Yahoo! Research and continuing at Microsoft Research to design a fast, scalable, useful learning algorithm; Microsoft's own Azure documentation similarly describes VW as a fast parallel learning framework developed at Yahoo! Research and later adapted by Langford at Microsoft Research. Over time, VW grew from a fast linear online learner into a research platform with a reduction stack and interactive-learning features. The upstream README highlights online learning, hashing, allreduce, reductions, learning-to-search, active learning, contextual bandits, and reinforcement learning, with performance treated as a core design constraint rather than an afterthought. ### Historique d'adoption VW's adoption history is closely tied to large-scale machine-learning research and industrial experimentation. Its official pages present Microsoft Research as a major contributor, and the project has long been used as a vehicle for turning research in online and interactive learning into runnable software. Microsoft described contextual-bandit technology based on this research line as deployed on MSN.com in January 2016, reporting a 26 percent increase in clicks for personalized news article selection. The same Microsoft Research post connected the open-source availability of core contextual-bandit algorithms to Vowpal Wabbit and related services. Academic work has repeatedly used or implemented algorithms in VW. For example, the 2021 JMLR Contextual Bandit Bake-off ran contextual-bandit algorithms online using Vowpal Wabbit, which reflects VW's role as a practical experimental substrate for large-scale contextual-bandit evaluation. ### Modes d'utilisation At the command line, VW users feed examples in VW's sparse text format to train classifiers, regressors, ranking models, topic models, contextual-bandit policies, and other reduction-based learners. The package is often chosen when data is too large or too streaming-oriented for batch-first tools, or when feature namespaces and interactions need to be explored quickly. In applications, VW is commonly used for online updates, offline policy evaluation, contextual-bandit learning, and experiments where the learner receives logged propensities or partial feedback rather than fully labeled examples. Its Python bindings and tutorials make the same engine usable from notebooks and application code while preserving the CLI-oriented workflow. ### Pourquoi les passionnés de paquets s'y intéressent For package nerds, Vowpal Wabbit is one of the classic examples of a research-grade command-line machine-learning tool that stayed relevant because it solved a specific systems problem: train quickly over enormous sparse feature spaces with little ceremony. It is also historically interesting because it bridges eras of ML tooling. VW predates the deep-learning framework boom, but its focus on streaming data, contextual decisions, and fast experimentation remains unusual and useful in package collections. ### Chronologie - 2007: Created in response to an internal Yahoo! Research contest. - Late 2000s: Opens as a fast online interactive-learning project led by John Langford. - 2010s: Continues under Microsoft Research sponsorship and expands reduction-stack, parallel-learning, and contextual-bandit capabilities. - 2016: Microsoft Research reports MSN.com deployment of contextual-bandit personalization technology connected to VW's algorithmic line. - 2021: Contextual Bandit Bake-off publishes large empirical evaluation using VW for online contextual-bandit algorithms. - 2020s: Upstream remains an active GitHub project with command-line, Python, C#, and Java surfaces documented. ### Related projects - Related ideas include feature hashing, online gradient methods, cost-sensitive classification reductions, contextual-bandit algorithms, and learning-to-search. - Related tools and ecosystems include Microsoft Research's contextual-bandit services and examples, Python notebook tutorials around VW, and data-science workflows that need fast sparse linear models rather than neural-network training stacks. ### 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 - vowpal-wabbit: normalized package name match | nixpkgs package indexes: pkgs/by-name/vo/vowpal-wabbit/package.nix from https://api.github.com/repos/NixOS/nixpkgs/git/trees/master?recursive=1 - Ubuntu apt - libvw-dev - 8.6.1.dfsg1-1build3: normalized package name match | Ubuntu 24.04 LTS package indexes: libvw-dev from https://archive.ubuntu.com/ubuntu/dists/noble/universe/binary-amd64/Packages.gz | fast and scalable online machine learning algorithm - development files | http://hunch.net/~vw/ - Ubuntu apt - libvw0 - 8.6.1.dfsg1-1build3: normalized package name match | Ubuntu 24.04 LTS package indexes: libvw0 from https://archive.ubuntu.com/ubuntu/dists/noble/universe/binary-amd64/Packages.gz | fast and scalable online machine learning algorithm - dynamic library | http://hunch.net/~vw/ - Ubuntu apt - vowpal-wabbit - 8.6.1.dfsg1-1build3: normalized package name match | Ubuntu 24.04 LTS package indexes: vowpal-wabbit from https://archive.ubuntu.com/ubuntu/dists/noble/universe/binary-amd64/Packages.gz | fast and scalable online machine learning algorithm | http://hunch.net/~vw/ - Ubuntu apt - vowpal-wabbit-dbg - 8.6.1.dfsg1-1build3: normalized package name match | Ubuntu 24.04 LTS package indexes: vowpal-wabbit-dbg from https://archive.ubuntu.com/ubuntu/dists/noble/universe/binary-amd64/Packages.gz | fast and scalable online machine learning algorithm - debug files | http://hunch.net/~vw/ - Ubuntu apt - vowpal-wabbit-doc - 8.6.1.dfsg1-1build3: normalized package name match | Ubuntu 24.04 LTS package indexes: vowpal-wabbit-doc from https://archive.ubuntu.com/ubuntu/dists/noble/universe/binary-amd64/Packages.gz | fast and scalable online machine learning algorithm - documentation | http://hunch.net/~vw/ - MacPorts - vowpal_wabbit: normalized package name match | MacPorts ports tree: math/vowpal_wabbit/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1 ## Combined YAML source View the package source record on GitHub. [combined/vowpal-wabbit.yml](https://github.com/mxcl/pkgdb/blob/main/combined/vowpal-wabbit.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