ONNX

ONNX (Open Neural Network Exchange) is an open-source format and ecosystem for representing machine learning models, enabling interoperability between different deep learning frameworks. It allows models trained in one framework (e.g., PyTorch, TensorFlow) to be exported and run in another framework or on various hardware accelerators. This facilitates model sharing, deployment, and optimization across diverse environments.

Also known as: Open Neural Network Exchange, ONNX Runtime, onnx, ONNX format, ONNX model

Why learn ONNX?

Developers should learn ONNX when working on cross-framework machine learning projects, as it simplifies model portability and reduces vendor lock-in. It is particularly useful for deploying models to production on edge devices, mobile platforms, or cloud services that support ONNX runtime, enabling efficient inference with optimized performance. Use cases include converting PyTorch models to run on TensorFlow-serving systems or deploying models to ONNX Runtime for hardware acceleration.

Learning Resources

• docs: ONNX Official Documentation
• tutorial: ONNX Tutorials on GitHub
• video: Introduction to ONNX on YouTube
• docs: ONNX Runtime Documentation
• course: Coursera Course: Deploying Machine Learning Models