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

Autor:	Song, Xingyou, Perel, Sagi, Lee, Chansoo, Kochanski, Greg, Golovin, Daniel
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Distributed Parallel and Cluster Computing Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Published as a conference paper for the systems track at the 1st International Conference on Automated Machine Learning (AutoML-Conf 2022). Code can be found at https://github.com/google/vizier
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.13676 Zobrazit plný text záznamu View this record from Arxiv