# Install vowpal-wabbit with Homebrew, MacPorts, Nix, apt Online learning algorithm. Version 9.11.2 via Homebrew; verified 2026-06-15. ## Install ```sh sudo av install brew:vowpal-wabbit ``` Additional install commands: ### macOS - Homebrew (100%): ```sh brew install vowpal-wabbit ``` Evidence: local Homebrew formula metadata - MacPorts (94%): ```sh sudo port install vowpal_wabbit ``` Evidence: 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 ``` Evidence: 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 libvw-dev ``` Evidence: Ubuntu 24.04 LTS package indexes: libvw-dev from https://archive.ubuntu.com/ubuntu/dists/noble/universe/binary-amd64/Packages.gz ## Package facts - **Package key:** brew:vowpal-wabbit - **Package manager:** Homebrew - **Package manager page:** - **Version:** 9.11.2 - **Source summary:** Online learning algorithm - **Homepage:** - **Repository:** - **Upstream docs:** - **License:** BSD-3-Clause - **Source archive:** - **Last updated:** 2026-06-15T10:21:23-04:00 - **Generated:** 2026-07-08T07:18:31+00:00 ## Executables - active_interactor (cli) - csv2vw (cli) - csv2vw.orig (cli) - logistic (cli) - spanning_tree (cli) - version_number.py (cli) - vw (cli) - vw-audit-pp (cli) - vw-convergence (cli) - vw-csv2bin (cli) - vw-doc2lda (cli) - vw-experiment (cli) - vw-format.pl (cli) - vw-hyperopt.py (cli) - vw-hypersearch (cli) - vw-lda (cli) - vw-regr (cli) - vw-top-errors (cli) - vw-varinfo (cli) - vw2csv (cli) - 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) ## Dependencies - fmt ## Build dependencies - boost - cmake - eigen - rapidjson - spdlog - sse2neon ## 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: 9.11.2 - Package-manager updated: 2026-06-15 - Local data: ok - Upstream repository: https://github.com/VowpalWabbit/vowpal_wabbit - Upstream latest detected: 9.11.2 (current) ## Project history and usage 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. ### Project history 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. ### Adoption history 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. ### How it is used 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. ### Why package nerds care 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. ### Timeline - 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 - - - - - - - - ## 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:** vowpal-wabbit - **Version Scheme:** 0 - **Revision:** 0 - **Head Version:** HEAD - **Bottle Stable Root URL:** - **Deprecated:** no - **Disabled:** no - **Keg Only:** no - **URL Keys:** head, stable ## Other Package-Manager Records - 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 ## 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. - [lightgbm](https://www.automicvault.com/pkg/brew/lightgbm/) - 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. - [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/vowpal-wabbit.yml](https://github.com/automic-vault/db/blob/main/combined/vowpal-wabbit.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