ONNX, Open Neural Network Exchange - The open standard for machine learning, is an open format built to represent machine learning models. ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers. The idea is to provide useful tools, independent to science domain of the codes or applications, that could be of interest, not only to SPACE but also to other CoEs.
ONNX is an open-source framework designed to enable interoperability of machine learning models across various AI tools and platforms supported by many key players in the field. It provides a standard format for representing machine learning and deep learning models. This simplifies deployment and enhances reproducibility of experiments. ONNX supports a wide range of operations and architectures, making it versatile for both industry and academia. By standardizing model representation and providing robust optimization and deployment tools, ONNX lowers barriers to advanced AI research and application, making cutting-edge technology more accessible to the scientific community. ONNX is particularly valuable for basic science because it promotes collaborative research by offering a common language for different tools. It accelerates experimentation, enabling rapid testing and validation of models. Compatibility with various hardware accelerators ensures efficient model execution. ONNXRuntime optimizes model performance, crucial for deployment in resource-constrained environments.