{"id":197708,"date":"2025-09-23T06:38:00","date_gmt":"2025-09-23T05:38:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=197708"},"modified":"2025-09-17T14:39:06","modified_gmt":"2025-09-17T13:39:06","slug":"all-bout-onnx","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/all-bout-onnx","title":{"rendered":"ONNX: Towards standardized interoperability of artificial intelligence"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"197708\" class=\"elementor elementor-197708\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-714de9c e-flex e-con-boxed e-con\" data-id=\"714de9c\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8a05df9 elementor-widget elementor-widget-text-editor\" data-id=\"8a05df9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><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><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5ffdf3f e-flex e-con-boxed e-con\" data-id=\"5ffdf3f\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-bb9a3d9 elementor-widget elementor-widget-heading\" data-id=\"bb9a3d9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\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><h2 class=\"elementor-heading-title elementor-size-xl\">What is ONNX?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b70c6ca elementor-widget elementor-widget-text-editor\" data-id=\"b70c6ca\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">ONNX is an open format designed to <\/span><b>represent <\/b><a href=\"https:\/\/datascientest.com\/en\/unraveling-machine-learning-vs-deep-learning-key-differences-explained\">machine learning and deep learning<\/a><b> models independently of frameworks<\/b><span style=\"font-weight: 400;\">. It enables developers to <\/span><b>export a trained model<\/b><span style=\"font-weight: 400;\"> from one environment (e.g., <a href=\"https:\/\/datascientest.com\/en\/pytorch-all-about-this-framework\">PyTorch<\/a> or <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-tfx\"><span style=\"font-weight: 400;\">TensorFlow<\/span><\/a><span style=\"font-weight: 400;\">) and <\/span><b>execute it in another, using a compatible inference engine<\/b><span style=\"font-weight: 400;\"> like ONNX Runtime, TensorRT, or OpenVINO.<\/span><\/p><p><span style=\"font-weight: 400;\">Originally developed by <\/span><b>Facebook and Microsoft<\/b><span style=\"font-weight: 400;\"> in 2017, ONNX is now endorsed by a broad industrial (IBM, Intel, AMD, Qualcomm, etc.) and academic community. This open-source standard promotes <\/span><b>model reuse<\/b><span style=\"font-weight: 400;\">, speeds up <\/span><b>deployment<\/b><span style=\"font-weight: 400;\"> in production, and enhances the <\/span><b>portability<\/b><span style=\"font-weight: 400;\"> and <\/span><b>agility<\/b><span style=\"font-weight: 400;\"> of AI systems.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0eae0c0 elementor-widget elementor-widget-image\" data-id=\"0eae0c0\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\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:\/\/datascientest.com\/en\/files\/2025\/07\/output1-3.png\" class=\"attachment-full size-full wp-image-197711\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/07\/output1-3.png 1536w, https:\/\/datascientest.com\/en\/files\/2025\/07\/output1-3-300x200.png 300w, https:\/\/datascientest.com\/en\/files\/2025\/07\/output1-3-1024x683.png 1024w, https:\/\/datascientest.com\/en\/files\/2025\/07\/output1-3-768x512.png 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-843e6a7 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"843e6a7\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Learn all about ONNX<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-8f10b79 e-flex e-con-boxed e-con\" data-id=\"8f10b79\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-204bc0d elementor-widget elementor-widget-heading\" data-id=\"204bc0d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">Architecture and Technical Components<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eb1fcc8 elementor-widget elementor-widget-text-editor\" data-id=\"eb1fcc8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The ONNX standard is structured on three fundamental principles:<\/span><\/p><ol><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Extensible computation graph<\/b><span style=\"font-weight: 400;\"><br \/><\/span><span style=\"font-weight: 400;\">Each model is represented as a <\/span><b>directed acyclic graph (DAG)<\/b><span style=\"font-weight: 400;\">, where nodes correspond to operations and edges to data flows, shaping the mathematical transformations applied to the inputs.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Standard operators<\/b><span style=\"font-weight: 400;\"><br \/><\/span><span style=\"font-weight: 400;\">ONNX defines a set of operators (convolution, normalization, activation, etc.) that are compatible across frameworks. These operators ensure a <\/span><b>predictable behavior of transferred models<\/b><span style=\"font-weight: 400;\"> without the need to retrain them.<\/span><\/li><li><b>Normalized data types<\/b><span style=\"font-weight: 400;\"><br \/><\/span><span style=\"font-weight: 400;\">The format supports standard types (float, int, multi-dimensional tensor, etc.), ensuring fine <\/span><b>compatibility<\/b><span style=\"font-weight: 400;\"> with execution engines.<\/span><\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-30e43bf elementor-widget elementor-widget-text-editor\" data-id=\"30e43bf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Mainly focused on the inference phase (evaluating already trained models), ONNX optimizes performance without enforcing constraints on training.<\/span><\/p><p><span style=\"font-weight: 400;\">This diagram synthesizes the central role of ONNX as a portability intermediary between AI model training and their deployment on various execution environments.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-67eb2d3 elementor-widget elementor-widget-image\" data-id=\"67eb2d3\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/07\/output1-1-2.png\" class=\"attachment-full size-full wp-image-197712\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/07\/output1-1-2.png 1536w, https:\/\/datascientest.com\/en\/files\/2025\/07\/output1-1-2-300x200.png 300w, https:\/\/datascientest.com\/en\/files\/2025\/07\/output1-1-2-1024x683.png 1024w, https:\/\/datascientest.com\/en\/files\/2025\/07\/output1-1-2-768x512.png 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b8b2442 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"b8b2442\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Training in AI<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aeb67ee elementor-widget elementor-widget-heading\" data-id=\"aeb67ee\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">1. Left Section \u2013 Training<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c409c1a elementor-widget elementor-widget-text-editor\" data-id=\"c409c1a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">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:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>PyTorch<\/b><span style=\"font-weight: 400;\">: widely utilized in research and academic environments, <\/span><a href=\"https:\/\/datascientest.com\/en\/pytorch-lightning-empowering-scalable-deep-learning-frameworks\"><span style=\"font-weight: 400;\">PyTorch<\/span><\/a><span style=\"font-weight: 400;\"> is favored for its flexibility, dynamic execution (<\/span><b>eager mode<\/b><span style=\"font-weight: 400;\">), and clear API, making it the preferred tool for rapid prototyping and experimentation.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>TensorFlow<\/b><span style=\"font-weight: 400;\">: extensively used in the industry, <\/span><a href=\"https:\/\/datascientest.com\/en\/tensorflow-course-where-to-learn-how-to-use-the-framework\"><span style=\"font-weight: 400;\">TensorFlow<\/span><\/a><span style=\"font-weight: 400;\"> provides robust infrastructure for large-scale deployment, distributed computing, and optimization on various hardware, notably GPUs and TPUs.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>scikit-learn<\/b><span style=\"font-weight: 400;\">: a staple for classical machine learning models (regression, decision trees, SVM&#8230;), <\/span><a href=\"https:\/\/datascientest.com\/en\/scikit-learn-discover-the-python-library-dedicated-to-machine-learning\"><span style=\"font-weight: 400;\">scikit-learn<\/span><\/a><span style=\"font-weight: 400;\"> is frequently used in preprocessing or in pipelines combining statistics and supervised learning.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5c0ef06 elementor-widget elementor-widget-text-editor\" data-id=\"5c0ef06\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">This combination of PyTorch \/ TensorFlow \/ scikit-learn covers <\/span><b>a vast majority of modern AI use cases<\/b><span style=\"font-weight: 400;\">, from exploratory prototyping to industrial-scale production deployment. ONNX serves here as a bridge connecting these ecosystems.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ca5a5ea elementor-widget elementor-widget-heading\" data-id=\"ca5a5ea\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">2. Center \u2013 ONNX Format<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2d97c37 elementor-widget elementor-widget-text-editor\" data-id=\"2d97c37\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The 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 <\/span><b>portability<\/b><span style=\"font-weight: 400;\"> 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 <\/span><b>hardware compatibility<\/b><span style=\"font-weight: 400;\">. As a result, ONNX provides an interoperable and agnostic representation, ready for deployment on a wide range of platforms.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f5dc1c2 elementor-widget elementor-widget-heading\" data-id=\"f5dc1c2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">3. Right Section \u2013 Multi-platform Execution<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-80f6b6b elementor-widget elementor-widget-text-editor\" data-id=\"80f6b6b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Once 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 <\/span><b>ONNX Runtime<\/b><span style=\"font-weight: 400;\">, <\/span><b>TensorRT<\/b><span style=\"font-weight: 400;\">, or <\/span><b>OpenVINO<\/b><span style=\"font-weight: 400;\">, and seamlessly integrates into applications developed in various languages, such as <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-courses-on-python\"><span style=\"font-weight: 400;\">Python<\/span><\/a><span style=\"font-weight: 400;\">, <a href=\"https:\/\/datascientest.com\/en\/c-what-is-this-computer-language-for\">C++<\/a>, <a href=\"https:\/\/datascientest.com\/en\/java-tutorial-a-comprehensive-guide-to-java-training\">Java<\/a>, or <a href=\"https:\/\/datascientest.com\/en\/javascript-unveiled-a-comprehensive-guide-to-the-language-of-the-web\">JavaScript<\/a>. This decoupling between training and execution provides <\/span><b>maximum flexibility<\/b><span style=\"font-weight: 400;\"> while maintaining <\/span><b>high performance<\/b><span style=\"font-weight: 400;\"> thanks to optimizations specific to each backend.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b7ae8bf elementor-widget elementor-widget-image\" data-id=\"b7ae8bf\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/07\/output1-4.png\" class=\"attachment-full size-full wp-image-197713\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/07\/output1-4.png 1536w, https:\/\/datascientest.com\/en\/files\/2025\/07\/output1-4-300x200.png 300w, https:\/\/datascientest.com\/en\/files\/2025\/07\/output1-4-1024x683.png 1024w, https:\/\/datascientest.com\/en\/files\/2025\/07\/output1-4-768x512.png 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f5fadcc elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"f5fadcc\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Learn how to use ONNX<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-9dc9387 e-flex e-con-boxed e-con\" data-id=\"9dc9387\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7bc08f6 elementor-widget elementor-widget-heading\" data-id=\"7bc08f6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">ONNX Runtime: Optimized Execution Engine<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1fe573f elementor-widget elementor-widget-text-editor\" data-id=\"1fe573f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">ONNX Runtime is the official execution engine for ONNX models, designed for performance with <\/span><b>optimizations tailored to hardware architectures<\/b><span style=\"font-weight: 400;\"> (<strong>CPU<\/strong>, <strong>GPU<\/strong>, <\/span><b>NPU<\/b><span style=\"font-weight: 400;\">), versatile with <\/span><b>multi-platform compatibility<\/b><span style=\"font-weight: 400;\"> (Windows, Linux, macOS, Android, iOS, web), and <\/span><b>multilingual<\/b><span style=\"font-weight: 400;\">; accessible via Python, C++, C#, Java, among others. It enables quick inferences with a <\/span><b>low memory footprint<\/b><span style=\"font-weight: 400;\">, making it particularly well-suited for production environments and embedded devices.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2a1bbae e-flex e-con-boxed e-con\" data-id=\"2a1bbae\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-cffab74 elementor-widget elementor-widget-heading\" data-id=\"cffab74\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">Industrial Use Cases<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-627bef0 elementor-widget elementor-widget-text-editor\" data-id=\"627bef0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">ONNX offers several practical advantages, including <\/span><b>interoperability between data science and engineering teams<\/b><span style=\"font-weight: 400;\">. It permits researchers to develop models in PyTorch, while the product team can easily integrate them into optimized backends.<\/span><\/p><p><span style=\"font-weight: 400;\">It also facilitates cross-platform deployment, allowing the same model to <\/span><b>run on diverse platforms<\/b><span style=\"font-weight: 400;\"> like <\/span><a href=\"https:\/\/datascientest.com\/en\/microsoft-azure-empower-yourself-with-knowledge\"><span style=\"font-weight: 400;\">Azure<\/span><\/a><span style=\"font-weight: 400;\">, <\/span><a href=\"https:\/\/datascientest.com\/en\/amazon-web-services-aws-unveiling-the-power-of-the-amazon-cloud\"><span style=\"font-weight: 400;\">AWS<\/span><\/a><span style=\"font-weight: 400;\">, Android, or even in connected vehicles.<\/span><\/p><p><span style=\"font-weight: 400;\">Finally, ONNX enables <\/span><b>independent assessment of AI models<\/b><span style=\"font-weight: 400;\"> on different inference engines, ensuring their robustness, stability, and accuracy.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-b4386a3 e-flex e-con-boxed e-con\" data-id=\"b4386a3\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5314de5 elementor-widget elementor-widget-heading\" data-id=\"5314de5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">Conclusion<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cd573e5 elementor-widget elementor-widget-text-editor\" data-id=\"cd573e5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">ONNX has become a <\/span><b>technical cornerstone of AI interoperability<\/b><span style=\"font-weight: 400;\">. Thanks to its standardized format, it simplifies the transition from research to production, reduces dependence on proprietary tools, and encourages <\/span><b>large-scale model reuse<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">In a context where AI architectures are swiftly evolving, ONNX signifies a <\/span><b>strategic technological investment for any organization<\/b><span style=\"font-weight: 400;\"> aiming to industrialize its AI solutions efficiently.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-64b579f elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"64b579f\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover our AI training courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c787f0e e-flex e-con-boxed e-con\" data-id=\"c787f0e\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-93dc137 elementor-widget elementor-widget-html\" data-id=\"93dc137\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"What is ONNX?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"ONNX is an open format designed to represent machine learning and deep learning models independently of frameworks, enabling model export and execution across different environments.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Architecture and Technical Components\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"ONNX is based on extensible computation graphs, standard operators, and normalized data types, ensuring compatibility and portability across frameworks and platforms.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"ONNX Runtime: Optimized Execution Engine\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"ONNX Runtime is the official engine for executing ONNX models with high performance, multi-platform support, and compatibility with several programming languages.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Industrial Use Cases\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"ONNX facilitates model transfer between teams, supports cross-platform deployment, and allows independent model evaluation on different inference engines.\"\r\n      }\r\n    }\r\n  ]\r\n}\r\n<\/script>\r\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","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 [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":197709,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2427],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>ONNX: Towards standardized interoperability of artificial intelligence<\/title>\n<meta name=\"description\" content=\"Discover ONNX, the open-source standard revolutionizing interoperability in artificial intelligence. 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