Google Vizier: A Service for Black-Box Optimization
Venue
ACM (2017)
Publication Year
2017
Authors
Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Elliot Karro, D. Sculley
BibTeX
Abstract
Any sufficiently complex system acts as a black box when it becomes easier to
experiment with than to understand. Hence, black-box optimization has become
increasingly important as systems have become more complex. In this paper we
describe Google Vizier, a Google-internal service for performing black-box
optimization that has become the de facto parameter tuning engine at Google. Google
Vizier is used to optimize many of our machine learning models and other systems,
and also provides core capabilities to Google's Cloud Machine Learning
HyperTune subsystem. We discuss our requirements, infrastructure design,
underlying algorithms, and advanced features such as transfer learning and
automated early stopping that the service provides.