In Depth
ONNX (Open Neural Network Exchange) is an open standard for representing machine learning models. It defines a common set of operators and a file format that allows models trained in one framework to be used in another. A model trained in PyTorch can be exported to ONNX format and then deployed using TensorFlow, TensorRT, or any other ONNX-compatible runtime.
ONNX addresses a practical problem in AI deployment: the training framework (often PyTorch for its flexibility) may not be the best choice for production inference. ONNX Runtime, developed by Microsoft, provides optimized inference across CPUs, GPUs, and specialized hardware. It applies graph optimizations, operator fusion, and hardware-specific acceleration that can significantly improve inference performance.
For businesses deploying AI models, ONNX provides framework independence and future-proofing. Models can be trained with whatever framework researchers prefer and deployed on whatever hardware offers the best performance-cost ratio. ONNX also enables deployment on edge devices, mobile platforms, and web browsers through ONNX.js, broadening where AI models can run.