Learn ONNX with Real Code Examples

ONNX provides a standard format for models, allowing them to be trained in one framework and deployed in another.

It supports operators for deep learning, classical ML, and other computational graphs.

ONNX enables cross-platform deployment, including edge devices, mobile, and cloud inference environments.

Interoperability between frameworks (PyTorch, TensorFlow, scikit-learn, etc.)

Graph-based computational representation

Model optimization and runtime acceleration

Cross-platform support for cloud, mobile, and edge

Extensible via custom operators for advanced use cases

ModelProto: ONNX serialized model format

Graph: computational graph representing model operations

Node: operator within the graph (e.g., Conv, Add, Relu)

Tensor: multi-dimensional array data flowing between nodes

OperatorSet: collection of supported operators

scripts/ - model training and conversion scripts

models/ - exported ONNX model files

datasets/ - data used for testing inference

notebooks/ - experiments and validation

logs/ - inference performance metrics

Train a model in your preferred framework

Export the trained model to ONNX format

Optional: optimize the model using ONNX Runtime tools

Run inference using ONNX Runtime across supported hardware

Deploy model on cloud, edge, or mobile platforms

Beginner: export simple PyTorch/TensorFlow models

Intermediate: optimize models for runtime performance

Advanced: handle custom operators and conversion issues

Expert: deploy models on heterogeneous edge devices

Enterprise: integrate ONNX into ML production pipelines

ONNX vs PyTorch: PyTorch for training; ONNX for interoperable deployment

ONNX vs TensorFlow SavedModel: ONNX is cross-framework; TF SavedModel is TF-specific

ONNX vs CoreML: CoreML targets Apple devices; ONNX is cross-platform

ONNX vs TensorRT: TensorRT optimizes for NVIDIA hardware; ONNX is model format

ONNX vs TFLite: TFLite is for mobile; ONNX supports broader deployment targets

2017 – Initial release by Microsoft and Facebook

2018 – ONNX Runtime introduced for high-performance inference

2019 – Added support for more operators and frameworks

2020 – Expanded optimization tools and quantization support

2025 – Latest version with broad framework and hardware interoperability

ONNX: Open Neural Network Exchange

Operator: computation unit/node

Graph: connected nodes and tensors

Runtime: execution engine for ONNX models

Quantization: reducing precision for optimization