ONNX - Design Principles

ONNX (Open Neural Network Exchange) is a powerful and flexible framework that enables interoperability between various machine learning and deep learning frameworks.

It facilitates the seamless transfer of models across different platforms, ensuring that models trained in one environment can be used for inference in another. In this tutorial, we will learn about the key design principles of ONNX.

Support for Both DL and Traditional ML

ONNX is designed to support deep learning models and traditional machine learning algorithms. Initially, ONNX was focused on deep learning, but as its ecosystem grew with contributions from diverse companies and organizations expanded, ONNX began to include support for traditional machine learning (ML) models as well.

Whether you are working with neural networks in deep learning or traditional machine learning algorithms like decision trees, linear regression, or support vector machines, you can convert these models to ONNX format. This ensures that models from different domains can be interoperability used and deployed across different platforms and environments.

Adaptability to Rapid Technological Advances

The machine learning and deep learning fields are continuously growing, with regular updates to frameworks such as TensorFlow, PyTorch, and Scikit-Learn. ONNX is designed to be flexible, tracking updates and changes in these frameworks and evolving accordingly.

As new features and improvements are introduced in machine learning frameworks, ONNX is also updated to incorporate these advancements. This ensures that ONNX remains relevant and compatible with the latest tools and libraries, allowing users to advantage of cutting-edge technology without being confined to a particular framework.

Compact and Cross-Platform Model

ONNX provides a compact and cross-platform representation for model serialization. This means that ONNX models can be easily saved, transferred, and loaded across different systems and platforms. The compact structure of ONNX files helps in reducing storage requirements and facilitating efficient model sharing.

For example, once you have an ONNX format, then that can be used across different environments, regardless of the underlying hardware or operating system. This cross-platform capability enhances the portability and usability of models, making it easier to deploy and integrate them into diverse applications.

Standardized List of Well-Defined Operators

ONNX uses a standardized list of well-defined operators informed by real-world usage. This means, ONNX defines a comprehensive set of operators that are commonly used in machine learning and deep learning tasks. These operators are carefully standardized and informed by practical use cases to ensure they cover a wide range of operations required for model execution.