Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization

Xingyou Song, Sagi Perel, Chansoo Lee, Greg Kochanski, Daniel Golovin
7/27/2022
cs.LGcs.DCstat.ML

Official code: google/vizier (1,680 stars, Apache-2.0, last commit 2026-02-17).

Abstract

Vizier is the de-facto blackbox and hyperparameter optimization service across Google, having optimized some of Google's largest products and research efforts. To operate at the scale of tuning thousands of users' critical systems, Google Vizier solved key design challenges in providing multiple different features, while remaining fully fault-tolerant. In this paper, we introduce Open Source (OSS) Vizier, a standalone Python-based interface for blackbox optimization and research, based on the Google-internal Vizier infrastructure and framework. OSS Vizier provides an API capable of defining and solving a wide variety of optimization problems, including multi-metric, early stopping, transfer learning, and conditional search. Furthermore, it is designed to be a distributed system that assures reliability, and allows multiple parallel evaluations of the user's objective function. The flexible RPC-based infrastructure allows users to access OSS Vizier from binaries written in any language. OSS Vizier also provides a back-end ("Pythia") API that gives algorithm authors a way to interface new algorithms with the core OSS Vizier system. OSS Vizier is available at https://github.com/google/vizier.

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Code Implementations(1)

google/vizierOfficial100%
Apache-2.0

Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.

1,680112Shell, PythonFeb 16, 20228 months agoApache-2.0
algorithmbayesian-optimizationblackbox-optimizationdeep-learningdistributed-computing+12 more

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Cite this paper

@article{song2022open,
  title  = {Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization},
  author = {Xingyou Song and Sagi Perel and Chansoo Lee and Greg Kochanski and Daniel Golovin},
  year   = {2022},
  eprint = {2207.13676},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.13676v2}
}

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