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.dev20240710225609.tar.gz.

File metadata

File hashes

Hashes for google-vizier-dev-0.1.16.dev20240710225609.tar.gz
Algorithm Hash digest
SHA256 cf84efd1bdea0b4aa171757e75072f885c63485b7102b161b437d9b90bd3464b
MD5 22837536213640e1ff1eff8d9f1cf9ac
BLAKE2b-256 cda2549dbee1a71ad2cd40b8b8f02e90629b89cd76772b19ad4c401acd50e9fb

See more details on using hashes here.

File details

Details for the file google_vizier_dev-0.1.16.dev20240710225609-py3-none-any.whl.

File metadata

File hashes

Hashes for google_vizier_dev-0.1.16.dev20240710225609-py3-none-any.whl
Algorithm Hash digest
SHA256 22adbcae649859b113b31fc0b29a2302b19773df52b3eff13a345ef11d527c53
MD5 72a5767e7a2c2a7a0988812ae6cae12b
BLAKE2b-256 11ff734b67d23501839fe3072490609b3765fc360f86d2495bfe3ee9056fe37a

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