Pricing
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Overview
ONNX, the Open Neural Network Exchange, is an open standard and open-source ecosystem for representing machine-learning models in a portable graph format. It defines common operators and model structures so trained models can move between frameworks, runtimes, optimization tools, and hardware backends without being tied to one training stack.
The repository contains the ONNX specification, operator definitions, model schemas, serialization support, validation tools, and reference components used by the broader ecosystem. Frameworks can export models to ONNX, while inference engines and hardware vendors can implement the standard to run those models efficiently.
ONNX is foundational infrastructure for ML interoperability rather than an end-user modeling framework. It is most useful when teams need a stable exchange format between training and deployment systems, especially in environments where models must target multiple runtimes or specialized accelerators.
README
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://lf-aidata.atlassian.net/wiki/spaces/DL/pages/14091402/LF+AI+Day+-ONNX+Community+Virtual+Meetup+-+Silicon+Valley+-+2020+April+9
- 2020.10.14 https://lf-aidata.atlassian.net/wiki/spaces/DL/pages/14092138/LF+AI+Day+-+ONNX+Community+Workshop+-+2020+October+14
- 2021.03.24 https://lf-aidata.atlassian.net/wiki/spaces/DL/pages/14092424/Instructions+for+Event+Hosts+-+LF+AI+Data+Day+-+ONNX+Virtual+Community+Meetup+-+March+2021
- 2021.10.21 https://lf-aidata.atlassian.net/wiki/spaces/DL/pages/14093194/LF+AI+Data+Day+ONNX+Community+Virtual+Meetup+-+October+2021
- 2022.06.24 https://lf-aidata.atlassian.net/wiki/spaces/DL/pages/14093969/ONNX+Community+Day+-+2022+June+24
- 2023.06.28 https://lf-aidata.atlassian.net/wiki/spaces/DL/pages/14094507/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/X]
Roadmap
A roadmap process takes place every year. More details can be found in ROADMAP.md.
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
Python ABI3 Compatibility
This package provides abi3-compatible wheels, allowing a single binary wheel to work across multiple Python versions (from 3.12 onwards).
Testing
ONNX uses pytest as test driver. In order to run tests, you will first need to install pytest:
pip install pytest
After installing pytest, use the following command to run tests.
pytest
Development
Check out the contributor guide for instructions.
Reproducible Builds (Linux)
This project provides reproducible builds for Linux.
A reproducible build means that the same source code will always produce identical binary outputs, no matter who builds it or where it is built.
To achieve this, we use the SOURCE_DATE_EPOCH standard. This ensures that build timestamps and other time-dependent information are fixed, making the output bit-for-bit identical across different environments.
Why this matters
- Transparency: Anyone can verify that the distributed binaries were created from the published source code.
- Security: Prevents tampering or hidden changes in the build process.
- Trust: Users can be confident that the binaries they download are exactly what the maintainers intended.
If you prefer, you can use the prebuilt reproducible binaries instead of building from source yourself.
License
Trademark
Checkout https://trademarks.justia.com for the trademark.
General rules of the Linux Foundation on Trademark usage
Code of Conduct
Releases
Release information has not been added yet.
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