ONNX (Open Neural Network Exchange) is an open standard for representing machine learning models that enables seamless interoperability across frameworks, tools, and hardware platforms. It defines a common intermediate representation and operator set that allows models trained in one framework to run efficiently on any compatible runtime.
Unified Model Representation
- Common intermediate representation and operator specifications eliminate vendor lock-in and reduce framework conversion costs
- Defines a graph-based intermediate representation with strongly typed computation nodes and standardized data types
- Models are serialized using Protocol Buffers for efficient cross-language parsing and transport
Broad Runtime Ecosystem
- Multiple inference engines and hardware accelerators enable optimized deployments across diverse targets
- Migrating research prototypes to production environments while leveraging specialized hardware accelerators for improved inference throughput
- Cross-framework model validation and benchmarking to ensure consistent behavior across different execution environments
Spec Governance and Versioning
- Opset versioning and spec governance manage backward compatibility while allowing the operator set to grow with community contributions
- Operator specifications provide detailed semantics with community-driven extension mechanisms
- Model interchange between training frameworks and production inference engines to simplify deployment pipelines