Documentation | Installation | Citing Vizier
OSS Vizier is a Python-based service for blackbox optimization and research, based on Google Vizier, one of the first hyperparameter tuning services designed to work at scale.
OSS Vizier's distributed client-server system. Animation by Tom Small.
OSS Vizier's interface consists of three main APIs:
- User API: Allows a user to setup an OSS Vizier Server, which can host blackbox optimization algorithms to serve multiple clients simultaneously in a fault-tolerant manner to tune their objective functions.
- Developer API: Defines abstractions and utilities for implementing new optimization algorithms for research and to be hosted in the service.
- Benchmarking API: A wide collection of objective functions and methods to benchmark and compare algorithms.
Please see OSS Vizier's ReadTheDocs documentation for detailed information.
To install the core API, the simplest way is to run:
pip install google-vizier
which will install the necessary dependencies from requirements.txt.
For full installation (to support all algorithms and benchmarks), run:
pip install google-vizier[extra]
which will also install the dependencies:
requirements-jax.txt: Jax libraries shared by both algorithms and benchmarks.requirements-tf.txt: Tensorflow libraries shared by both algorithms and benchmarks.requirements-algorithms.txt: Additional repositories (e.g. Emukit) for algorithms.requirements-benchmarks.txt: Additional repositories (e.g. NASBENCH-201) for benchmarks.
For both cases, you must build relevant protobuf libraries by running build_protos.sh.
Check if all unit tests work by running run_tests.sh. OSS Vizier requires Python 3.10+, while client-only packages require Python 3.7+.
If you found this code useful, please consider citing the OSS Vizier paper as well as the Google Vizier paper. Thanks!
@inproceedings{oss_vizier,
author = {Xingyou Song and
Sagi Perel and
Chansoo Lee and
Greg Kochanski and
Daniel Golovin},
title = {Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization},
booktitle = {Automated Machine Learning Conference, Systems Track (AutoML-Conf Systems)},
year = {2022},
}
@inproceedings{google_vizier,
author = {Daniel Golovin and
Benjamin Solnik and
Subhodeep Moitra and
Greg Kochanski and
John Karro and
D. Sculley},
title = {Google Vizier: {A} Service for Black-Box Optimization},
booktitle = {Proceedings of the 23rd {ACM} {SIGKDD} International Conference on
Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13
- 17, 2017},
pages = {1487--1495},
publisher = {{ACM}},
year = {2017},
url = {https://doi.org/10.1145/3097983.3098043},
doi = {10.1145/3097983.3098043},
}