Vowpal Wabbit
Vowpal Wabbit logo.svg
Screen-vowpal-wabbit.png
A screenshot of Vowpal Wabbit
Developer(s)Yahoo! Research & later Microsoft Research
Stable release
9.0.0 / January 28, 2022; 6 months ago (2022-01-28)
Repositorygithub.com/VowpalWabbit/vowpal_wabbit
Written inC++
Operating systemLinux, macOS, Microsoft Windows
PlatformCross-platform
TypeMachine learning
LicenseBSD License
Websitevowpalwabbit.org

Vowpal Wabbit (VW) is an open-source fast online interactive machine learning system library and program developed originally at Yahoo! Research, and currently at Microsoft Research. It was started and is led by John Langford. Vowpal Wabbit's interactive learning support is particularly notable including Contextual Bandits, Active Learning, and forms of guided Reinforcement Learning. Vowpal Wabbit provides an efficient scalable out-of-core implementation with support for a number of machine learning reductions, importance weighting, and a selection of different loss functions and optimization algorithms.

Notable features

The VW program supports:

Scalability

Vowpal wabbit has been used to learn a tera-feature (1012) data-set on 1000 nodes in one hour.[1] Its scalability is aided by several factors:

References

  1. ^ Agarwal, Alekh; Chapelle, Olivier; Dudik, Miroslav; Langford, John (2011). "A Reliable Effective Terascale Linear Learning System". arXiv:1110.4198 [cs.LG].