Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

logo

PyGlove: Manipulating Python Programs

PyPI version codecov pytest

Getting started | Installation | Examples | Reference docs

What is PyGlove

PyGlove is a general-purpose library for Python object manipulation. It introduces symbolic object-oriented programming to Python, allowing direct manipulation of objects that makes meta-programs much easier to write. It has been used to handle complex machine learning scenarios, such as AutoML, as well as facilitating daily programming tasks with extra flexibility.

PyGlove is lightweight and has very few dependencies beyond the Python interpreter. It provides:

  • A mutable symbolic object model for Python;
  • A rich set of operations for Python object manipulation;
  • A solution for automatic search of better Python programs, including:
    • An easy-to-use API for dropping search into an arbitrary pre-existing Python program;
    • A set of powerful search primitives for defining the search space;
    • A library of search algorithms ready to use, and a framework for developing new search algorithms;
    • An API to interface with any distributed infrastructure (e.g. Open Source Vizier) for such search.

It's commonly used in:

  • Automated machine learning (AutoML);
  • Evolutionary computing;
  • Machine learning for large teams (evolving and sharing ML code, reusing ML techniques, etc.);
  • Daily programming tasks in Python (advanced binding capabilities, mutability, etc.).

PyGlove has been published at NeurIPS 2020. It is widely used within Alphabet, including Google Research, Google Cloud, Youtube and Waymo.

PyGlove is developed by Daiyi Peng and colleagues at Google Brain.

Hello PyGlove

import pyglove as pg

@pg.symbolize
class Hello:
  def __init__(self, subject):
    self._greeting = f'Hello, {subject}!'

  def greet(self):
    print(self._greeting)


hello = Hello('World')
hello.greet()

Hello, World!

hello.rebind(subject='PyGlove')
hello.greet()

Hello, PyGlove!

hello.rebind(subject=pg.oneof(['World', 'PyGlove']))
for h in pg.iter(hello):
  h.greet()

Hello, World!
Hello, PyGlove!

Install

pip install pyglove

Or install nightly build with:

pip install pyglove --pre

Examples

Citing PyGlove

@inproceedings{peng2020pyglove,
  title={PyGlove: Symbolic programming for automated machine learning},
  author={Peng, Daiyi and Dong, Xuanyi and Real, Esteban and Tan, Mingxing and Lu, Yifeng and Bender, Gabriel and Liu, Hanxiao and Kraft, Adam and Liang, Chen and Le, Quoc},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={33},
  pages={96--108},
  year={2020}
}

Disclaimer: this is not an officially supported Google product.

Download files

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

Source Distribution

pyglove-0.5.0.dev202607180839.tar.gz (565.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pyglove-0.5.0.dev202607180839-py3-none-any.whl (735.9 kB view details)

Uploaded Python 3

File details

Details for the file pyglove-0.5.0.dev202607180839.tar.gz.

File metadata

  • Download URL: pyglove-0.5.0.dev202607180839.tar.gz
  • Upload date:
  • Size: 565.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.6

File hashes

Hashes for pyglove-0.5.0.dev202607180839.tar.gz
Algorithm Hash digest
SHA256 a08403987b8f7efcb27b20364921d72e0a46036ee6f281db74bb74ee96bdd29d
MD5 495842c0df7c5393f3adfeb393060353
BLAKE2b-256 5705322574c117ef58a0539f72849c53310ad0a987937248d35d868d6f7bab55

See more details on using hashes here.

File details

Details for the file pyglove-0.5.0.dev202607180839-py3-none-any.whl.

File metadata

File hashes

Hashes for pyglove-0.5.0.dev202607180839-py3-none-any.whl
Algorithm Hash digest
SHA256 4f3e71751955f4b154b0aeccc903da4c5e502bd16485e29c97d8bb857317fc86
MD5 7a1387dfc6fce35bd56fa16367a2c1d9
BLAKE2b-256 974eb542e0b0a28d436d999748f30a46221514ee175bf180b6f0474b016cbb71

See more details on using hashes here.

Release history Release notifications | RSS feed

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page