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**ONNX (Open Neural Network Exchange)** is an open-source format for representing deep learning models. It enables the interchange of models between different frameworks and tools, fostering collaboration and innovation in the field of machine learning. ONNX allows users to seamlessly convert models trained in one framework to another, facilitating interoperability and reducing the need for costly retraining.

The ONNX format is widely adopted by popular deep learning frameworks such as PyTorch, TensorFlow, and MXNet. This broad support enables developers to leverage the strengths of different frameworks while maintaining compatibility with ONNX-compatible tools and services. Additionally, ONNX provides a standardized way to optimize models for deployment on various hardware platforms, including CPUs, GPUs, and mobile devices.

ONNX: ONNX 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. Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Currently we focus on the capabilities needed for inferencing (scoring). ONNX is widely supported and can be found in many frameworks, tools, and hardware. Enabling interoperability between different frameworks and streamlining the path from research to production helps increase the speed of innovation in the AI community.