What is ONNX, and why is it used?

ONNX (Open Neural Network Exchange) is an open-source format designed to represent machine learning models in a standardized way. It allows models trained in one framework, like PyTorch or TensorFlow, to be exported and used in other frameworks or environments without significant rework. ONNX solves the problem of interoperability between different tools and platforms by acting as a universal “middle ground.” For example, a model built in PyTorch can be converted to ONNX and then deployed using a runtime optimized for mobile devices, cloud services, or specialized hardware like GPUs or NPUs. This eliminates the need to rebuild the model from scratch for each deployment scenario.

The primary use case for ONNX is simplifying the transition from model development to production. Developers often train models in research-friendly frameworks (e.g., PyTorch for flexibility) but need to deploy them in environments optimized for performance or specific hardware (e.g., ONNX Runtime for low-latency inference). ONNX bridges this gap by providing a consistent way to serialize model architectures, weights, and operations. For instance, a TensorFlow model can be converted to ONNX using tools like tf2onnx, then optimized with ONNX Runtime for faster inference. This is particularly useful in scenarios like edge computing, where models must run efficiently on resource-constrained devices, or when integrating ML into applications built with non-Python stacks (e.g., C++ or JavaScript).

ONNX’s ecosystem includes tools for model conversion, optimization, and execution. Major frameworks like PyTorch and TensorFlow support exporting to ONNX, while libraries like ONNX Runtime provide cross-platform inference with performance optimizations like quantization or operator fusion. The format is extensible, allowing custom operators for niche use cases. For example, a developer might train a vision transformer in PyTorch, convert it to ONNX, then deploy it on an IoT device using ONNX Runtime’s ARM64 build. By reducing dependency on a single framework, ONNX enables teams to choose the best tools for each stage of the ML lifecycle while avoiding vendor lock-in. Its community-driven governance also ensures broad compatibility and ongoing updates to support new model types and hardware.