Learn ONNX with Real Code Examples

Graph-based model representation (nodes = operators, edges = tensors)

Supports standard data types and tensor shapes

Includes metadata for inputs, outputs, and training parameters

Extensible operator set for custom computations

ONNX Runtime executes graphs with hardware-specific optimizations

Graph-based representation of operators and tensors

Supports standard and custom operator sets

Serialization in ModelProto format

Executable via ONNX Runtime or compatible engines

Optimizable via quantization and graph transformations

Graph of nodes representing operations

Tensors as data flowing between nodes

Separation of model definition and runtime execution

Operator sets versioned for backward compatibility

Extensible architecture for custom layers

Cross-framework ML model deployment

Mobile and edge AI applications

Cloud inference pipelines

IoT devices with constrained resources

Hybrid models combining multiple frameworks

Framework-agnostic model representation

Interoperable across hardware and software

Optimized for inference performance

Extensible operator set for custom use cases

Simplify cross-platform deployment

Optimize graphs for faster inference

Quantize models for reduced memory footprint

Use batching for high-throughput inference

Deploy across multiple CPUs/GPUs or cloud instances

Leverage ONNX Runtime distributed execution for large workloads

Upgrade ONNX via pip

Ensure runtime compatibility with model opset

Test exported models for inference correctness

Update deployment pipelines for new ONNX version

Verify performance on target hardware