Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers
to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard
data types. Currently we focus on the capabilities needed for inferencing (scoring).

ONNX is widely supported and can be found in many frameworks, tools, and hardware. Enabling interoperability between different frameworks and streamlining the path from research to production helps increase the speed of innovation in the AI community. We invite the community to join us and further evolve ONNX.

Use ONNX

Learn about the ONNX spec

Overview
ONNX intermediate representation spec
Versioning principles of the spec
Operators documentation
Operators documentation (latest release)
Python API Overview

Programming utilities for working with ONNX Graphs

Contribute

ONNX is a community project and the open governance model is described here. We encourage you to join the effort and contribute feedback, ideas, and code. You can participate in the Special Interest Groups and Working Groups to shape the future of ONNX.

Check out our contribution guide to get started.

If you think some operator should be added to ONNX specification, please read
this document.

Community meetings

The schedules of the regular meetings of the Steering Committee, the working groups and the SIGs can be found here

Community Meetups are held at least once a year. Content from previous community meetups are at:

Discuss

We encourage you to open Issues, or use Slack (If you have not joined yet, please use this link to join the group) for more real-time discussion.

Follow Us

Stay up to date with the latest ONNX news. [Facebook] [Twitter]

Roadmap

A roadmap process takes place every year. More details can be found here

Installation

ONNX released packages are published in PyPi.

pip install onnx # or pip install onnx[reference] for optional reference implementation dependencies

ONNX weekly packages are published in PyPI to enable experimentation and early testing.

Detailed install instructions, including Common Build Options and Common Errors can be found here

Testing

ONNX uses pytest as test driver. In order to run tests, you will first need to install pytest:

pip install pytest nbval

After installing pytest, use the following command to run tests.

pytest

Development

Check out the contributor guide for instructions.

License

Apache License v2.0

Code of Conduct

ONNX Open Source Code of Conduct

Dependency	Optional
numpy	false
protobuf	false
typing_extensions	false
google-re2	true
Pillow	true

Package profile

onnx

Releases

Release table

PyPI Downloads

Dependencies

Details

Project URLs

Dependencies

Package description