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.dev20240722.tar.gz (443.4 kB view details)

Uploaded Source

Built Distribution

pyglove-0.4.5.dev20240722-py3-none-any.whl (580.8 kB view details)

Uploaded Python 3

File details

Details for the file pyglove-0.4.5.dev20240722.tar.gz.

File metadata

  • Download URL: pyglove-0.4.5.dev20240722.tar.gz
  • Upload date:
  • Size: 443.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.4

File hashes

Hashes for pyglove-0.4.5.dev20240722.tar.gz
Algorithm Hash digest
SHA256 ec27b5231d706c07633564fef143c234d2342fa73503eae428c7144807566a80
MD5 591ef4d95cc2b6abf9d68a02e7245fe0
BLAKE2b-256 66660f9cb91b80c8e6ae938aa94619e98f80deb7c0c8ef9f24f725afbada3a2d

See more details on using hashes here.

File details

Details for the file pyglove-0.4.5.dev20240722-py3-none-any.whl.

File metadata

File hashes

Hashes for pyglove-0.4.5.dev20240722-py3-none-any.whl
Algorithm Hash digest
SHA256 3b6efae15bd256655062b8aec971d204224df732d9227b0885654aa8e859e027
MD5 081c547413709ab2adabfbdaee3d9f71
BLAKE2b-256 76dfe4ede52281de71f5580f08c4591796be1e59586f1cf6985ece1981756934

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