onnx 1.21.0rc4

Open Neural Network Exchange

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

• Documentation of ONNX Python Package
• Tutorials for creating ONNX models
• Pre-trained ONNX models

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

• Shape and Type Inference
• Graph Optimization
• Opset Version Conversion

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 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

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

Apache License v2.0

Trademark

Checkout https://trademarks.justia.com for the trademark.

General rules of the Linux Foundation on Trademark usage

Code of Conduct

ONNX Open Source Code of Conduct