Model Interoperability Format

ONNX (Open Neural Network Exchange) is an open-source format for representing machine learning models, enabling interoperability between different frameworks and deployment platforms without vendor lock-in. Purpose: train model in one framework (PyTorch, TensorFlow), export to ONNX, deploy anywhere (ONNX Runtime, TensorRT, CoreML, mobile). Format: protocol buffer-based representation of computation graph—nodes (operators like Conv, MatMul, ReLU), edges (tensors), and metadata (shapes, types). Supported operators: 150+ standard operators covering deep learning primitives, with extensibility for custom ops. Export workflow: (1) train model in native framework, (2) export to ONNX (torch.onnx.export, tf2onnx), (3) validate (ONNX checker), (4) optimize (ONNX optimizer, ONNX Runtime), (5) deploy. Advantages: (1) framework independence (switch frameworks without retraining), (2) deployment flexibility (optimize for target hardware), (3) ecosystem (many tools support ONNX), (4) production stability (decouples training and inference stacks). ONNX Runtime: high-performance inference engine supporting CPU, GPU, NPU—optimizations include graph fusion, quantization, and hardware-specific kernels. Limitations: (1) not all framework features supported (dynamic control flow, custom ops may require workarounds), (2) version compatibility (operator set versions), (3) debugging harder than native framework. Use cases: (1) production deployment (train in PyTorch, deploy with ONNX Runtime), (2) edge deployment (export to mobile formats via ONNX), (3) model sharing (distribute models in framework-agnostic format). ONNX has become the de facto standard for ML model interchange, widely adopted in industry for production deployments.