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-0.1.17.dev20240806145621.tar.gz.

File metadata

File hashes

Hashes for google-vizier-dev-0.1.17.dev20240806145621.tar.gz
Algorithm Hash digest
SHA256 35b4a638b43603ed9394d6bc25ed9a7d2aa2af59482f70231a178ea103d055bb
MD5 2d68f01d59eb794413ee1aed4407ec43
BLAKE2b-256 8f88207687574af30d02414e3c5c9ed2df1aa5e2b52b04871b4c1fd2e29688c0

See more details on using hashes here.

File details

Details for the file google_vizier_dev-0.1.17.dev20240806145621-py3-none-any.whl.

File metadata

File hashes

Hashes for google_vizier_dev-0.1.17.dev20240806145621-py3-none-any.whl
Algorithm Hash digest
SHA256 76230ed8a60c128b6a3bb91c486b010c7379c1089789e243bc8710754e4575c6
MD5 5c63fb01220be2777b8d3c1239bde7a0
BLAKE2b-256 0bb6579c609d5d66e39a0d1966f7938fc254779101c8268f34474ede33e15e3b

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