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 version: 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.

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: If you found this code useful, please consider citing the OSS Vizier paper as well as the Google Vizier paper.

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

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 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-0.1.16.dev20240520173809.tar.gz.

File metadata

File hashes

Hashes for google-vizier-dev-0.1.16.dev20240520173809.tar.gz
Algorithm Hash digest
SHA256 6f465bae7dcc01447310991b3c28774b93cce2edf3042841545a566623c331f3
MD5 dd430a31cc024861349014d8c7d080cf
BLAKE2b-256 7d823a53c18ce66b3a3993e6875fb0f35bbd5aee7c810ff2f8eaf768dd244cc0

See more details on using hashes here.

File details

Details for the file google_vizier_dev-0.1.16.dev20240520173809-py3-none-any.whl.

File metadata

File hashes

Hashes for google_vizier_dev-0.1.16.dev20240520173809-py3-none-any.whl
Algorithm Hash digest
SHA256 48108dd089f7dc9ea50fcb0022f86efbbd7b67339e645cc1670568c0f1cf946f
MD5 822aec3be71d8ead41a968362ee2650a
BLAKE2b-256 a2c8a6e0a62bc42133653d217a618d1c7998ccccdc792e4d3cc55d684d17961a

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