Skip to main content

Open Source Vizier: Distributed service framework for blackbox optimization and research.

Project description

Open Source Vizier: Reliable and Flexible Black-Box Optimization.

PyPI version Continuous Integration Docs

Google AI Blog | Getting Started | Documentation | Installation | Citing and Highlights

What is Open Source (OSS) Vizier?

OSS Vizier is a Python-based service for black-box 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.

Getting Started

As a basic example for users, below shows how to tune a simple objective using all flat search space types:

from vizier.service import clients
from vizier.service import pyvizier as vz

# Objective function to maximize.
def evaluate(w: float, x: int, y: float, z: str) -> float:
  return w**2 - y**2 + x * ord(z)

# Algorithm, search space, and metrics.
study_config = vz.StudyConfig(algorithm='DEFAULT')
study_config.search_space.root.add_float_param('w', 0.0, 5.0)
study_config.search_space.root.add_int_param('x', -2, 2)
study_config.search_space.root.add_discrete_param('y', [0.3, 7.2])
study_config.search_space.root.add_categorical_param('z', ['a', 'g', 'k'])
study_config.metric_information.append(vz.MetricInformation('metric_name', goal=vz.ObjectiveMetricGoal.MAXIMIZE))

# Setup client and begin optimization. Vizier Service will be implicitly created.
study = clients.Study.from_study_config(study_config, owner='my_name', study_id='example')
for i in range(10):
  suggestions = study.suggest(count=2)
  for suggestion in suggestions:
    params = suggestion.parameters
    objective = evaluate(params['w'], params['x'], params['y'], params['z'])
    suggestion.complete(vz.Measurement({'metric_name': objective}))

Documentation

OSS Vizier's interface consists of three main APIs:

  • User API: Allows a user to optimize their blackbox objective and optionally setup a server for distributed multi-client settings.
  • 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.

Additionally, it contains advanced API for:

  • Tensorflow Probability: For writing Bayesian Optimization algorithms using Tensorflow Probability and Flax.
  • PyGlove: For large-scale evolutionary experimentation and program search using OSS Vizier as a distributed backend.

Please see OSS Vizier's ReadTheDocs documentation for detailed information.

Installation

Quick start: For tuning objectives using our state-of-the-art JAX-based Bayesian Optimizer, run:

pip install google-vizier[jax]

Advanced Installation

Minimal installation: To install only the core service and client APIs from requirements.txt, run:

pip install google-vizier

Full installation: To support all algorithms and benchmarks, run:

pip install google-vizier[all]

Specific installation: If you only need a specific part "X" of OSS Vizier, run:

pip install google-vizier[X]

which installs add-ons from requirements-X.txt. Possible options:

  • requirements-jax.txt: Jax libraries shared by both algorithms and benchmarks.
  • requirements-tf.txt: Tensorflow libraries used by benchmarks.
  • requirements-algorithms.txt: Additional repositories (e.g. EvoJAX) for algorithms.
  • requirements-benchmarks.txt: Additional repositories (e.g. NASBENCH-201) for benchmarks.
  • requirements-test.txt: Libraries needed for testing code.

Developer installation: To install up to the latest commit, run:

pip install google-vizier-dev[X]

Check if all unit tests work by running run_tests.sh after a full installation. OSS Vizier requires Python 3.10+, while client-only packages require Python 3.8+.

Citing and Highlights

Citing Vizier: Please consider citing the appropriate paper(s): Algorithm, OSS Package, and Google System if you found any of them useful.

Highlights: We track notable users and media attention - let us know if OSS Vizier was helpful for your work.

Thanks!

@article{gaussian_process_bandit,
  author       = {Xingyou Song and
                  Qiuyi Zhang and
                  Chansoo Lee and
                  Emily Fertig and
                  Tzu-Kuo Huang and
                  Lior Belenki and
                  Greg Kochanski and
                  Setareh Ariafar and
                  Srinivas Vasudevan and
                  Sagi Perel and
                  Daniel Golovin},
  title        = {The Vizier Gaussian Process Bandit Algorithm},
  journal      = {Google DeepMind Technical Report},
  year         = {2024},
  eprinttype    = {arXiv},
  eprint       = {2408.11527},
}

@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 Black-box 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},
}

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

Built Distribution

File details

Details for the file google-vizier-dev-0.1.20.dev20241111192350.tar.gz.

File metadata

File hashes

Hashes for google-vizier-dev-0.1.20.dev20241111192350.tar.gz
Algorithm Hash digest
SHA256 ebe5d38c395cc643c3d57579a4717bdeb65b7fe61ce2cf48ea7cf6f155d3839e
MD5 fb3ff366d31b83fe5d4b7ea8b6e7da39
BLAKE2b-256 071f024a6a216ed1fe5d9aacc6c2aa45f5c5b2147368cce3b01a6dddaa76fe07

See more details on using hashes here.

File details

Details for the file google_vizier_dev-0.1.20.dev20241111192350-py3-none-any.whl.

File metadata

File hashes

Hashes for google_vizier_dev-0.1.20.dev20241111192350-py3-none-any.whl
Algorithm Hash digest
SHA256 29f1a3315c2d03151695976f77495b287e620ef9197d64ced93d29e12fbf757e
MD5 6b7c46c3fc9fcb9642cc6ed1ab27e4e2
BLAKE2b-256 772a54d8d3592ced239ffda6bf6b63e38ce7d92e18038c814e4b454a0c21dad9

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page