Learn ONNX with Real Code Examples

Install Python 3.7+

Install ONNX library: `pip install onnx`

Install ONNX Runtime for inference: `pip install onnxruntime`

Optional: Install framework converters like `onnx-tf` or `torch.onnx`

Verify installation by exporting a sample model and running inference

Install Python 3.7+

Install ONNX and ONNX Runtime

Verify framework version compatibility

Test sample model export and inference

Install optional optimization and conversion tools

models/ - ONNX model files

scripts/ - export and inference scripts

datasets/ - input data for testing

logs/ - performance and debug logs

optimizations/ - optional optimization configurations

pip install onnx onnxruntime

python export_model.py

onnxruntime_test.py to run inference

Use environment variables to select GPU/CPU device

Monitor logs and performance metrics

Unicode dataset support

Global adoption for cross-platform deployment

Documentation in English, community translations available

Compatible with international ML standards

Supported by multiple cloud and edge providers

Cross-platform Python support

ONNX Runtime supports multiple hardware backends

Open-source and free

Framework-agnostic for diverse teams

Accessible for enterprise and research

Jupyter/Colab for experimentation

Visualize model graphs using Netron

Monitor inference latency/throughput

Export plots for reporting

Integrate dashboards for real-time metrics

Version ONNX models for reproducibility

Track runtime performance metrics

Backup exported models

Log inference results

Ensure reproducibility across deployments

Preprocess input datasets compatible with exported model

Maintain consistent data types and shapes

Use batching for efficient inference

Cache intermediate results if needed

Track feature transformations for reproducibility