How to Safely Export and Verify ML Models Using ONNX Runtime

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ONNX Runtime allows a single model artifact to run across CPU, GPU, and various accelerators via one API, eliminating much of the per-platform engineering work. However, the export process can silently introduce errors, including frozen control flow, operator decomposition into approximations, and numeric drift. Developers must carefully configure dynamic axes during export to avoid inference failures on variable-sized inputs, and should validate the exported graph's node count as an early warning of inefficient decomposition. Numerical accuracy should be verified by running both the original and exported models on identical inputs and comparing outputs within an explicit tolerance threshold. Opset versioning and provider-level operator support are additional compatibility concerns that can cause parts of a model to run on unintended hardware without any error being raised.

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