Principle:Onnx Onnx Model Building
| Knowledge Sources | |
|---|---|
| Domains | Model_Construction, Intermediate_Representation |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
A packaging mechanism that wraps a computation graph with model-level metadata including IR version, operator set declarations, and producer information to create a complete, self-describing model.
Description
Model building is the final assembly step that converts a computation graph into a fully self-contained ONNX model (ModelProto). While the graph defines the computation itself, the model adds critical metadata: the IR version (defining which features of the ONNX specification are used), operator set imports (declaring which versions of operator definitions the model relies on), and optional producer metadata. This metadata enables runtimes to determine compatibility and select the correct operator implementations.
The operator set import mechanism is central to ONNX versioning: each model declares which opset version it targets (e.g., opset 21 for ONNX 1.16), and runtimes use this declaration to dispatch to the correct operator semantics. Without explicit opset imports, the model defaults to the latest opset version available in the ONNX library.
Usage
Use this principle as the final step in programmatic model construction, after the computation graph has been assembled. Model building is required before validation or serialization, since checker.check_model and save_model both operate on ModelProto objects.
Theoretical Basis
An ONNX model is defined as:
Failed to parse (syntax error): {\displaystyle \text{Model} = (\text{graph}, \text{ir\_version}, \text{opset\_imports}, \text{metadata}) }
Where:
- graph is the computation GraphProto
- ir_version is an integer specifying the ONNX IR version (determines available features)
- opset_imports is a set of (domain, version) pairs declaring operator set dependencies
- metadata includes producer_name, model_version, doc_string, etc.
Pseudo-code:
# Abstract model construction
model = ModelProto()
model.ir_version = current_ir_version
model.graph = assembled_graph
model.opset_imports = [(domain, version), ...]
# If no opset specified, defaults to latest ai.onnx opset