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.dev202608100838.tar.gz (565.9 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.dev202608100838-py3-none-any.whl (736.0 kB view details)

Uploaded Python 3

File details

Details for the file pyglove-0.5.0.dev202608100838.tar.gz.

File metadata

  • Download URL: pyglove-0.5.0.dev202608100838.tar.gz
  • Upload date:
  • Size: 565.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/7.0.0 CPython/3.14.6

File hashes

Hashes for pyglove-0.5.0.dev202608100838.tar.gz
Algorithm Hash digest
SHA256 201be0620d2dffebcb33faadbba5799e1bde29209feb9f24dba4daa2b62cd3db
MD5 3889c1271e5a8f296254c6996646c7ab
BLAKE2b-256 600f7cc7dda8875bc74f5de8e5a3c7ce8253302b7fc37edd4527ef35bcedeb00

See more details on using hashes here.

File details

Details for the file pyglove-0.5.0.dev202608100838-py3-none-any.whl.

File metadata

File hashes

Hashes for pyglove-0.5.0.dev202608100838-py3-none-any.whl
Algorithm Hash digest
SHA256 2211c53b41af3339ce3c2dc9eaa2951a3346dd9d1d2000bce556026f1eee80bd
MD5 e070c722f75413fb379b2c202b2437dc
BLAKE2b-256 54378a09a4d21fbf29d0e47342345701e066a98bb46e6fe2988b7706ba808545

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