Google Vizier: A Service for Black-Box Optimization
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.
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.
Research Areas
