Skip to main content

PyGlove: A library for manipulating Python objects.

Project description

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 in Google Brain Team.

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.

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

pyglove-0.4.5.dev20240313.tar.gz (441.5 kB view details)

Uploaded Source

Built Distribution

pyglove-0.4.5.dev20240313-py3-none-any.whl (578.8 kB view details)

Uploaded Python 3

File details

Details for the file pyglove-0.4.5.dev20240313.tar.gz.

File metadata

  • Download URL: pyglove-0.4.5.dev20240313.tar.gz
  • Upload date:
  • Size: 441.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.2

File hashes

Hashes for pyglove-0.4.5.dev20240313.tar.gz
Algorithm Hash digest
SHA256 4d5d8422eaae375cc4305c35f510927ef174702c250a97e75a9fa95a47fbc6bb
MD5 51d51ef107d4f23d77f0b827b7b3477e
BLAKE2b-256 7803f6bccb7a99da00f1b446c64e7815687423118dee4e921185623bbd0bc336

See more details on using hashes here.

File details

Details for the file pyglove-0.4.5.dev20240313-py3-none-any.whl.

File metadata

File hashes

Hashes for pyglove-0.4.5.dev20240313-py3-none-any.whl
Algorithm Hash digest
SHA256 d747895bfcadbb4fce79a5c99854a968ecd295c053305f42a74c7377a746b8d1
MD5 d12960abb7947ccbe85d75b6e5dcf60d
BLAKE2b-256 3cc626a88be349caad2dabd16d6b6a4f600f2efd80ae237f016f2f8568087b6e

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