{"id":197708,"date":"2026-01-28T16:17:23","date_gmt":"2026-01-28T15:17:23","guid":{"rendered":"https:\/\/liora.io\/en\/?p=197708"},"modified":"2026-08-09T19:55:13","modified_gmt":"2026-08-09T18:55:13","slug":"all-bout-onnx","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/all-bout-onnx","title":{"rendered":"ONNX: Towards standardized interoperability of artificial intelligence"},"content":{"rendered":"\n

As AI projects grow increasingly intricate, the necessity to transfer and execute models across varied environments has become crucial. The current landscape is filled with a multitude of development frameworks, each with its own characteristics and proprietary formats. Within this setting, ONNX (Open Neural Network Exchange) emerges as a standardized and open-source solution to ensure interoperability between these different environments.<\/strong><\/p>\n\n\n

What is ONNX?<\/h2>\n\n\n

ONNX is an open format designed to represent <\/strong><\/p>\n\n\n\n

models independently of frameworks<\/strong>. It enables developers to export a trained model<\/strong> from one environment (e.g., PyTorch<\/a> or TensorFlow<\/a>) and execute it in another, using a compatible inference engine<\/strong> like ONNX Runtime, TensorRT, or OpenVINO.<\/p>\n\n\n

Originally developed by Facebook and Microsoft<\/strong> in 2017, ONNX is now endorsed by a broad industrial (IBM, Intel, AMD, Qualcomm, etc.) and academic community. This open-source standard promotes model reuse<\/strong>, speeds up deployment<\/strong> in production, and enhances the portability<\/strong> and agility<\/strong> of AI systems.<\/p>\n\n\n

\"Illustration<\/figure>\n\n\n\n

Architecture and Technical Components<\/h2>\n\n\n

The ONNX standard is structured on three fundamental principles:<\/p>\n\n\n

    \n
  1. Extensible computation graph<\/strong>\nEach model is represented as a directed acyclic graph (DAG)<\/strong>, where nodes correspond to operations and edges to data flows, shaping the mathematical transformations applied to the inputs.<\/li>\n
  2. Standard operators<\/strong>\nONNX defines a set of operators (convolution, normalization, activation, etc.) that are compatible across frameworks. These operators ensure a predictable behavior of transferred models<\/strong> without the need to retrain them.<\/li>\n
  3. Normalized data types<\/strong>\nThe format supports standard types (float, int, multi-dimensional tensor, etc.), ensuring fine compatibility<\/strong> with execution engines.<\/li>\n<\/ol>\n\n\n

    Mainly focused on the inference phase (evaluating already trained models), ONNX optimizes performance without enforcing constraints on training.<\/p>\n\n\n

    This diagram synthesizes the central role of ONNX as a portability intermediary between AI model training and their deployment on various execution environments.<\/p>\n\n\n

    \"Illustration<\/figure>\n\n\n
    Training in AI<\/a><\/div><\/div>\n\n\n

    1. Left Section : Training<\/h3>\n\n\n

    The model is initially designed and trained using one of the primary machine learning or deep learning frameworks. ONNX allows these models to be exported in a unified format, thereby facilitating their reuse and deployment across other platforms:<\/p>\n\n\n