Model Packaging and ONNX
ONNX is a shared file format for trained models that lets you train in one framework and run inference in another, avoiding a rewrite every time the serving stack changes.
A model trained in one framework — PyTorch, scikit-learn, XGBoost — is normally only easy to run inside that same framework, which becomes a problem when the serving environment wants something faster or written in a different language, say a low-latency C++ execution engine. ONNX (Open Neural Network Exchange) solves this by defining a common file format that represents a model as a graph of standard mathematical operations, independent of the framework that produced it. Most major training frameworks can export to ONNX, and a range of runtimes — notably ONNX Runtime — can load that file and run inference on CPU or GPU without ever needing the original training code installed.
The practical benefit is decoupling: a research team can keep training in whatever framework is most productive, then hand a single portable .onnx file to a production team that serves it with a runtime optimized for latency, without maintaining a live dependency on the training library's version in production. The tradeoff is that not every custom layer or operation converts cleanly — exotic architectures sometimes need custom ONNX operators or don't convert at all — so packaging to ONNX is usually a validated step in the deployment pipeline, with outputs checked against the original model before it's trusted in production.
ONNX packages a trained model as a framework-independent graph of standard operations, so it can be trained in one library and served by a different, often faster runtime — the main risk is that unusual custom layers may not convert cleanly and need validation before deployment.
Further reading
- ONNX Runtime documentation, Open Neural Network Exchange