ONNX (Open Neural Network Exchange)

ONNX (Open Neural Network Exchange) is an open standard for representing machine learning models in a framework-agnostic format. It defines a common way to describe a model’s computation graph, operators, and parameters so that models can move reliably between tools and runtimes.

In practice, ONNX is a portability and interoperability layer for ML.

Why It Exists

The ML ecosystem is fragmented: models are trained in one framework, optimised in another, and deployed in yet another environment. ONNX addresses three recurring problems:

• Framework lock-in — models tied to a single training library
• Deployment friction — re-implementing or rewriting models for production
• Performance gaps — difficulty exploiting specialised runtimes or hardware

ONNX decouples training, optimisation, and inference.

How It Works (Conceptually)

1. A model is trained in a framework such as PyTorch, TensorFlow, or JAX
2. The model is exported to ONNX format
3. The ONNX model is run by an ONNX-compatible runtime or compiler

The ONNX file contains: - A computation graph - Standardised operators (the ONNX “opset”) - Model weights and metadata

What ONNX Is Not

• It is not a training framework
• It is not a runtime by itself
• It is not a guarantee of identical performance everywhere

Think of it as a contract, not an execution engine.

Why Organisations Use ONNX

1. Deployment Flexibility

Run the same model across: - Cloud services - Edge devices - Mobile and embedded systems

without rewriting model logic.

2. Performance Optimisation

ONNX models can be executed by high-performance runtimes such as: - ONNX Runtime - TensorRT - OpenVINO

These can deliver significant inference speedups over general-purpose frameworks.

3. Vendor and Framework Independence

ONNX reduces strategic risk by: - Avoiding dependence on a single ML framework - Allowing teams to switch tooling without retraining models

4. Cleaner Engineering Boundaries

ML research and production engineering can evolve independently: - Research teams choose the best training tools - Platform teams choose the best deployment stack

Typical Use Cases

• Exporting research models into production systems
• Standardising model deployment across multiple products
• Running inference efficiently on CPUs, GPUs, or accelerators
• Supporting long-lived models while frameworks evolve

Trade-offs and Limitations

• Operator coverage: New or experimental layers may not export cleanly
• Debuggability: ONNX graphs are harder to inspect than native code
• Feature lag: Cutting-edge framework features may arrive later in ONNX
• Training not supported: ONNX is inference-first by design

These are usually acceptable costs when stability and portability matter more than rapid experimentation.

Strategic Takeaway

ONNX is best viewed as infrastructure glue for machine learning:

• It lowers operational risk
• Improves deployment consistency
• Extends model lifespan beyond any single framework

If ML models are becoming core production assets rather than research artefacts, ONNX is often a pragmatic foundation.

References

• Wikipedia
• Github