{"id":197708,"date":"2026-01-28T16:17:23","date_gmt":"2026-01-28T15:17:23","guid":{"rendered":"https:\/\/liora.io\/en\/?p=197708"},"modified":"2026-02-06T07:21:35","modified_gmt":"2026-02-06T06:21:35","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":"<b>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.<\/b>\n\n<style><br \/>\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style>\n<h2 class=\"wp-block-heading\" id=\"h-what-is-onnx\">What is ONNX?<\/h2>\nONNX is an open format designed to <b>represent <\/b>\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"https:\/\/liora.io\/en\/unraveling-machine-learning-vs-deep-learning-key-differences-explained\">machine learning and deep learning<\/a><\/div><\/div>\n<b> models independently of frameworks<\/b>. It enables developers to <b>export a trained model<\/b> from one environment (e.g., <a href=\"https:\/\/liora.io\/en\/pytorch-all-about-this-framework\">PyTorch<\/a> or <a href=\"https:\/\/liora.io\/en\/all-about-tfx\">TensorFlow<\/a>) and <b>execute it in another, using a compatible inference engine<\/b> like ONNX Runtime, TensorRT, or OpenVINO.\n\nOriginally developed by <b>Facebook and Microsoft<\/b> in 2017, ONNX is now endorsed by a broad industrial (IBM, Intel, AMD, Qualcomm, etc.) and academic community. This open-source standard promotes <b>model reuse<\/b>, speeds up <b>deployment<\/b> in production, and enhances the <b>portability<\/b> and <b>agility<\/b> of AI systems.\n\n<style><br \/>\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-3.png\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-3.png 1536w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-3-300x200.png 300w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-3-1024x683.png 1024w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-3-768x512.png 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\">\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/\">Learn all about ONNX<\/a><\/div><\/div>\n\n<h2 class=\"wp-block-heading\" id=\"h-architecture-and-technical-components\">Architecture and Technical Components<\/h2>\nThe ONNX standard is structured on three fundamental principles:\n<ol>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Extensible computation graph<\/b>\nEach model is represented as a <b>directed acyclic graph (DAG)<\/b>, where nodes correspond to operations and edges to data flows, shaping the mathematical transformations applied to the inputs.<\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Standard operators<\/b>\nONNX defines a set of operators (convolution, normalization, activation, etc.) that are compatible across frameworks. These operators ensure a <b>predictable behavior of transferred models<\/b> without the need to retrain them.<\/li>\n \t<li><b>Normalized data types<\/b>\nThe format supports standard types (float, int, multi-dimensional tensor, etc.), ensuring fine <b>compatibility<\/b> with execution engines.<\/li>\n<\/ol>\nMainly focused on the inference phase (evaluating already trained models), ONNX optimizes performance without enforcing constraints on training.\n\nThis diagram synthesizes the central role of ONNX as a portability intermediary between AI model training and their deployment on various execution environments.\n\n<img decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-1-2.png\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-1-2.png 1536w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-1-2-300x200.png 300w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-1-2-1024x683.png 1024w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-1-2-768x512.png 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\">\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/\">Training in AI<\/a><\/div><\/div>\n\n<h3 class=\"wp-block-heading\" id=\"h-1-left-section-training\">1. Left Section \u2013 Training<\/h3>\nThe 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:\n<ul>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>PyTorch<\/b>: widely utilized in research and academic environments, <a href=\"https:\/\/liora.io\/en\/pytorch-lightning-empowering-scalable-deep-learning-frameworks\">PyTorch<\/a> is favored for its flexibility, dynamic execution (<b>eager mode<\/b>), and clear API, making it the preferred tool for rapid prototyping and experimentation.<\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>TensorFlow<\/b>: extensively used in the industry, <a href=\"https:\/\/liora.io\/en\/tensorflow-course-where-to-learn-how-to-use-the-framework\">TensorFlow<\/a> provides robust infrastructure for large-scale deployment, distributed computing, and optimization on various hardware, notably GPUs and TPUs.<\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>scikit-learn<\/b>: a staple for classical machine learning models (regression, decision trees, SVM&#8230;), <a href=\"https:\/\/liora.io\/en\/scikit-learn-discover-the-python-library-dedicated-to-machine-learning\">scikit-learn<\/a> is frequently used in preprocessing or in pipelines combining statistics and supervised learning.<\/li>\n<\/ul>\nThis combination of PyTorch \/ TensorFlow \/ scikit-learn covers <b>a vast majority of modern AI use cases<\/b>, from exploratory prototyping to industrial-scale production deployment. ONNX serves here as a bridge connecting these ecosystems.\n<h3 class=\"wp-block-heading\" id=\"h-2-center-onnx-format\">2. Center \u2013 ONNX Format<\/h3>\nThe central ONNX block in the diagram embodies a critical technological convergence point. It functions as a universal abstraction layer, encapsulating the model in a format that is independent of any specific framework. This <b>portability<\/b> hinges on three key elements: a DAG-structured computation graph for optimized execution, a set of standardized operators ensuring coherent semantics, and formalized data types guaranteeing <b>hardware compatibility<\/b>. As a result, ONNX provides an interoperable and agnostic representation, ready for deployment on a wide range of platforms.\n<h3 class=\"wp-block-heading\" id=\"h-3-right-section-multi-platform-execution\">3. Right Section \u2013 Multi-platform Execution<\/h3>\nOnce exported, the ONNX model can be deployed in the cloud, locally, at the edge, or on mobile devices. It operates with optimized inference engines like <b>ONNX Runtime<\/b>, <b>TensorRT<\/b>, or <b>OpenVINO<\/b>, and seamlessly integrates into applications developed in various languages, such as <a href=\"https:\/\/liora.io\/en\/all-about-courses-on-python\">Python<\/a>, <a href=\"https:\/\/liora.io\/en\/c-what-is-this-computer-language-for\">C++<\/a>, <a href=\"https:\/\/liora.io\/en\/java-tutorial-a-comprehensive-guide-to-java-training\">Java<\/a>, or <a href=\"https:\/\/liora.io\/en\/javascript-unveiled-a-comprehensive-guide-to-the-language-of-the-web\">JavaScript<\/a>. This decoupling between training and execution provides <b>maximum flexibility<\/b> while maintaining <b>high performance<\/b> thanks to optimizations specific to each backend.\n\n<img decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-4.png\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-4.png 1536w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-4-300x200.png 300w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-4-1024x683.png 1024w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/output1-4-768x512.png 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\">\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/\">Learn how to use ONNX<\/a><\/div><\/div>\n\n<h2 class=\"wp-block-heading\" id=\"h-onnx-runtime-optimized-execution-engine\">ONNX Runtime: Optimized Execution Engine<\/h2>\nONNX Runtime is the official execution engine for ONNX models, designed for performance with <b>optimizations tailored to hardware architectures<\/b> (<strong>CPU<\/strong>, <strong>GPU<\/strong>, <b>NPU<\/b>), versatile with <b>multi-platform compatibility<\/b> (Windows, Linux, macOS, Android, iOS, web), and <b>multilingual<\/b>; accessible via Python, C++, C#, Java, among others. It enables quick inferences with a <b>low memory footprint<\/b>, making it particularly well-suited for production environments and embedded devices.\n<h2 class=\"wp-block-heading\" id=\"h-industrial-use-cases\">Industrial Use Cases<\/h2>\nONNX offers several practical advantages, including <b>interoperability between data science and engineering teams<\/b>. It permits researchers to develop models in PyTorch, while the product team can easily integrate them into optimized backends.\n\nIt also facilitates cross-platform deployment, allowing the same model to <b>run on diverse platforms<\/b> like <a href=\"https:\/\/liora.io\/en\/microsoft-azure-empower-yourself-with-knowledge\">Azure<\/a>, <a href=\"https:\/\/liora.io\/en\/amazon-web-services-aws-unveiling-the-power-of-the-amazon-cloud\">AWS<\/a>, Android, or even in connected vehicles.\n\nFinally, ONNX enables <b>independent assessment of AI models<\/b> on different inference engines, ensuring their robustness, stability, and accuracy.\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Conclusion<\/h2>\nONNX has become a <b>technical cornerstone of AI interoperability<\/b>. Thanks to its standardized format, it simplifies the transition from research to production, reduces dependence on proprietary tools, and encourages <b>large-scale model reuse<\/b>.\n\nIn a context where AI architectures are swiftly evolving, ONNX signifies a <b>strategic technological investment for any organization<\/b> aiming to industrialize its AI solutions efficiently.\n\n<a href=\"\/en\/courses\/data-ai\/\">\nDiscover our AI training courses\n<\/a>","protected":false},"excerpt":{"rendered":"<p>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.<\/p>\n","protected":false},"author":85,"featured_media":197709,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"_acf_changed":false,"editor_notices":[],"footnotes":""},"categories":[2433],"class_list":["post-197708","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/197708","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/users\/85"}],"replies":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/comments?post=197708"}],"version-history":[{"count":5,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/197708\/revisions"}],"predecessor-version":[{"id":205299,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/197708\/revisions\/205299"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media\/197709"}],"wp:attachment":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media?parent=197708"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/categories?post=197708"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}