ONNX Tutorials is a collection of educational resources for learning to create and use Open Neural Network Exchange models.
The tutorials address the challenge of working with ONNX, an open standard format for representing machine learning models across different frameworks and tools. The approach centers on providing hands-on examples through Jupyter Notebooks that guide users through the process of converting models from various frameworks into ONNX format and deploying them. The tutorials also direct users to the ONNX Model Zoo, where pre-trained models are available for common scenarios, and to cloud-based services like Azure Custom Vision and Azure Machine Learning that can generate customized ONNX models.
Developers should use these tutorials if they need to understand how to work with ONNX models or convert existing models from their current framework into the ONNX format. The resource is most valuable for teams adopting ONNX as an interoperability layer across multiple machine learning frameworks and deployment targets. The tutorials assume familiarity with machine learning concepts but provide step-by-step guidance suitable for practitioners new to the ONNX ecosystem.
The project maintains a curated collection of working examples with active updates to keep pace with ONNX standard evolution and framework changes. The repository accepts community contributions to expand tutorial coverage and improve existing examples. Development activity focuses on ensuring tutorials remain functional and relevant as the broader ONNX ecosystem develops.
