ONNX is an open-source standard for machine learning interoperability that provides a unified format for representing neural network models. By defining a common set of operators and a standardized file structure, it enables models to be shared, exported, and executed consistently across different training frameworks and software ecosystems.
The project functions as an intermediate representation layer that decouples model development from deployment. It utilizes a language-neutral binary serialization format to store model structures and weights, ensuring that computational graphs remain portable. This architecture allows developers to convert models between various frameworks and deploy them onto diverse hardware runtimes, including CPUs, GPUs, and specialized AI accelerators.
Beyond standardization, the framework includes tools for graph-level optimization and visualization, allowing for the inspection of data flow and the application of performance enhancements. It also provides access to a centralized repository of pre-trained models, facilitating the integration of ready-to-use intelligence into production applications while supporting hardware-specific acceleration to improve inference efficiency.
