What problem it solves

Machine learning workflows can involve multiple frameworks, tools, and hardware, creating interoperability challenges between model producers and consumers. A common model format and computation graph specification helps streamline the path from research to production.

Key features

• Extensible Model Format — The format represents deep learning and traditional ML models through an extensible computation graph model, built-in operator definitions, and standard data types.

  Source: README

• Framework Interoperability — Models can move between frameworks, tools, and hardware that support the format.

  Source: README

• Graph Programming Utilities — The project provides utilities for shape and type inference, graph optimization, and opset version conversion.

  Source: README

• Inference-Oriented Specification — The specification currently focuses on the capabilities needed for inference, or scoring.

  Source: README

• Community Governance — Developers can participate through open governance, Special Interest Groups, Working Groups, issues, Slack, and community meetings.

  Source: README

Quick start

Copied verbatim from the project's README.

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

Who it may suit

Inference may suit AI developers who need a shared format for models across frameworks, as well as contributors interested in model specifications, graph utilities, or community governance.

Topics to discuss

• Which inference capabilities and operator definitions should receive priority in the next specification cycle?

• How can compatibility and conversion workflows be improved across supported frameworks and tools?

• What contributions would be most useful to the Special Interest Groups and Working Groups?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/onnx/onnx#readme

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