onnx

ONNX is an open format built to represent machine learning models in a way that can be moved between different frameworks, tools, runtimes, and compilers. The project defines a common set of operators, the basic computational blocks used in both machine‑learning and deep‑learning models, and stores them in a single file format. You can begin by visiting the home page, where links to “Get Started,” documentation, and community resources are provided.

In practice, you train a model in your preferred framework, such as PyTorch, TensorFlow, or another supported environment, and then export it to the ONNX file format. The exported file contains the model's graph, weights, and the standardized operators, which any ONNX‑compatible runtime can read. This means you can keep your training pipeline unchanged while selecting an inference engine that matches the hardware you intend to use. Typical inputs to an ONNX model are the same tensors you would feed to the original framework model; the output is a set of prediction tensors produced by the runtime, without requiring framework‑specific code at inference time.

The format is aimed at developers who need flexibility in deployment. If you work on research prototypes in one framework but must run the model on edge devices, servers, or specialized AI accelerators, ONNX gives you a path to do so without rewriting the model. Conversely, users who rely on a single framework for both training and inference may not need the extra conversion step, and the listing does not specify any built‑in tools for model training itself.

Hardware access is highlighted as a benefit: ONNX‑compatible runtimes and libraries are designed to exploit optimizations on CPUs, GPUs, and other accelerators. By selecting a runtime that targets a specific accelerator, you can achieve better performance while keeping the model file unchanged. The site mentions “supported accelerators” but does not enumerate them, so you will need to check individual runtimes for exact compatibility.

Community involvement is another practical aspect. ONNX operates under an open governance structure, offering transparency and inclusion. You can join the Slack channel, participate in special interest groups (SIGs), or become part of a working group. The site also advertises events such as the First ONNX Meetup in Europe, and it provides a contribution guide and a travel fund to support community participation.