Pre-release
This release is a pre-release and may not be stable for production use.
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:
- 2020.04.09 https://wiki.lfaidata.foundation/display/DL/LF+AI+Day+-ONNX+Community+Virtual+Meetup+-+Silicon+Valley+-+April+9
- 2020.10.14 https://wiki.lfaidata.foundation/display/DL/LF+AI+Day+-+ONNX+Community+Workshop+-+October+14
- 2021.03.24 https://wiki.lfaidata.foundation/pages/viewpage.action?pageId=35160391
- 2021.10.21 https://wiki.lfaidata.foundation/pages/viewpage.action?pageId=46989689
- 2022.06.24 https://wiki.lfaidata.foundation/display/DL/ONNX+Community+Day+-+June+24
- 2023.06.28 https://wiki.lfaidata.foundation/display/DL/ONNX+Community+Day+2023+-+June+28
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
AMD's 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
Code of Conduct
1 maintainer
Metadata
1 maintainer
Release files for amd-onnx-weekly 1.18.0.dev20250127
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 225.0 MB
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