ONNX

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 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. If you think some operator should be added to ONNX specification, please read this document. Community Meetups are held at least once a year. Content from previous community meetups are at: 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. A roadmap process takes place every year. More details can be found in ROADMAP.md. ONNX weekly packages are published in PyPI to enable experimentation and early testing. This package provides abi3-compatible wheels, allowing a single binary wheel to work across multiple Python versions (from 3.12 onwards). 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. If you prefer, you can use the prebuilt reproducible binaries instead of building from source yourself.