{"product_id":"onnx-format-fur-ki-modelle-zur-interoperabilitat-zwischen-frameworks-🛠️","title":"ONNX – Format de modèles d'IA pour l'interopérabilité entre frameworks 🪴🛠️","description":"\u003ch3 data-start=\"197\" data-end=\"217\"\u003e🧠 Qu'est-ce qu'ONNX ?\u003c\/h3\u003e\n\u003cp data-start=\"219\" data-end=\"599\"\u003eONNX (Open Neural Network Exchange) est un format ouvert, développé pour permettre aux équipes de développement d'entraîner des modèles dans un framework et de les déployer dans un autre, sans avoir à les réécrire. L'objectif est de réduire les frictions entre les différents outils d'IA et d'accélérer le passage de la recherche à la production. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003cspan class=\"ms-1 inline-flex max-w-full items-center relative top-[-0.094rem] animate-[show_150ms_ease-in]\" data-testid=\"webpage-citation-pill\"\u003e\u003ca href=\"https:\/\/onnx.ai\/about.html?utm_source=chatgpt.com\" rel=\"noopener\" alt=\"https:\/\/onnx.ai\/about.html?utm_source=chatgpt.com\" class=\"flex h-4.5 overflow-hidden rounded-xl px-2 text-[9px] font-medium transition-colors duration-150 ease-in-out text-token-text-secondary! bg-[#F4F4F4]! dark:bg-[#303030]!\" target=\"_blank\"\u003e\u003cspan class=\"relative start-0 bottom-0 flex h-full w-full items-center\"\u003e\u003cspan class=\"flex h-4 w-full items-center justify-between\"\u003e\u003cspan class=\"max-w-[15ch] grow truncate overflow-hidden text-center\"\u003eonnx.ai\u003c\/span\u003e\u003cspan class=\"-me-1 flex h-full items-center rounded-full px-1 text-[#8F8F8F]\"\u003e+2\u003c\/span\u003e\u003c\/span\u003e\u003cspan class=\"flex h-4 w-full items-center justify-between absolute\"\u003e\u003cspan class=\"max-w-[15ch] grow truncate overflow-hidden text-center\"\u003eonnx.ai\u003c\/span\u003e\u003cspan class=\"-me-1 flex h-full items-center rounded-full px-1 text-[#8F8F8F]\"\u003e+2\u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/a\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003chr data-start=\"601\" data-end=\"604\"\u003e\n\u003ch3 data-start=\"606\" data-end=\"640\"\u003e🌟 Avantages et particularités\u003c\/h3\u003e\n\u003cul data-start=\"642\" data-end=\"1334\"\u003e\n\n\u003cli data-start=\"642\" data-end=\"795\"\u003e\n\n\u003cp data-start=\"644\" data-end=\"795\"\u003e\u003cstrong data-start=\"644\" data-end=\"672\"\u003eIndépendance vis-à-vis des frameworks\u003c\/strong\u003e : Phase d'entraînement possible dans, par exemple, PyTorch, et déploiement dans TensorFlow ou ONNX Runtime. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003cspan class=\"ms-1 inline-flex max-w-full items-center relative top-[-0.094rem] animate-[show_150ms_ease-in]\" data-testid=\"webpage-citation-pill\"\u003e\u003ca href=\"https:\/\/docs.oracle.com\/en\/database\/oracle\/machine-learning\/oml4sql\/23\/dmprg\/onnx.html?utm_source=chatgpt.com\" rel=\"noopener\" alt=\"https:\/\/docs.oracle.com\/en\/database\/oracle\/machine-learning\/oml4sql\/23\/dmprg\/onnx.html?utm_source=chatgpt.com\" class=\"flex h-4.5 overflow-hidden rounded-xl px-2 text-[9px] font-medium transition-colors duration-150 ease-in-out text-token-text-secondary! bg-[#F4F4F4]! dark:bg-[#303030]!\" target=\"_blank\"\u003e\u003cspan class=\"relative start-0 bottom-0 flex h-full w-full items-center\"\u003e\u003cspan class=\"flex h-4 w-full items-center justify-between\"\u003e\u003cspan class=\"max-w-[15ch] grow truncate overflow-hidden text-center\"\u003eOracle Docs\u003c\/span\u003e\u003cspan class=\"-me-1 flex h-full items-center rounded-full px-1 text-[#8F8F8F]\"\u003e+2\u003c\/span\u003e\u003c\/span\u003e\u003cspan class=\"flex h-4 w-full items-center justify-between absolute\"\u003e\u003cspan class=\"max-w-[15ch] grow truncate overflow-hidden text-center\"\u003eonnx.ai\u003c\/span\u003e\u003cspan class=\"-me-1 flex h-full items-center rounded-full px-1 text-[#8F8F8F]\"\u003e+2\u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/a\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli data-start=\"796\" data-end=\"984\"\u003e\n\n\u003cp data-start=\"798\" data-end=\"984\"\u003e\u003cstrong data-start=\"798\" data-end=\"822\"\u003eOptimisation des performances\u003c\/strong\u003e : ONNX Runtime propose des optimisations pour divers environnements matériels tels que les processeurs (CPU), les cartes graphiques (GPU) ou des accélérateurs spécialisés. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003cspan class=\"ms-1 inline-flex max-w-full items-center relative top-[-0.094rem] animate-[show_150ms_ease-in]\" data-testid=\"webpage-citation-pill\"\u003e\u003ca href=\"https:\/\/vraspar.github.io\/onnxruntime\/about.html?utm_source=chatgpt.com\" rel=\"noopener\" alt=\"https:\/\/vraspar.github.io\/onnxruntime\/about.html?utm_source=chatgpt.com\" class=\"flex h-4.5 overflow-hidden rounded-xl px-2 text-[9px] font-medium transition-colors duration-150 ease-in-out text-token-text-secondary! bg-[#F4F4F4]! dark:bg-[#303030]!\" target=\"_blank\"\u003e\u003cspan class=\"relative start-0 bottom-0 flex h-full w-full items-center\"\u003e\u003cspan class=\"flex h-4 w-full items-center justify-between\"\u003e\u003cspan class=\"max-w-[15ch] grow truncate overflow-hidden text-center\"\u003evraspar.github.io\u003c\/span\u003e\u003cspan class=\"-me-1 flex h-full items-center rounded-full px-1 text-[#8F8F8F]\"\u003e+2\u003c\/span\u003e\u003c\/span\u003e\u003cspan class=\"flex h-4 w-full items-center justify-between absolute\"\u003e\u003cspan class=\"max-w-[15ch] grow truncate overflow-hidden text-center\"\u003eonnx.ai\u003c\/span\u003e\u003cspan class=\"-me-1 flex h-full items-center rounded-full px-1 text-[#8F8F8F]\"\u003e+2\u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/a\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli data-start=\"985\" data-end=\"1164\"\u003e\n\n\u003cp data-start=\"987\" data-end=\"1164\"\u003e\u003cstrong data-start=\"987\" data-end=\"1020\"\u003eStructure de graphe standardisée\u003c\/strong\u003e : Les modèles sont enregistrés sous forme de graphe de calcul acyclique avec des opérateurs et des types de données définis. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003cspan class=\"ms-1 inline-flex max-w-full items-center relative top-[-0.094rem] animate-[show_150ms_ease-in]\" data-testid=\"webpage-citation-pill\"\u003e\u003ca href=\"https:\/\/onnx.ai\/about.html?utm_source=chatgpt.com\" rel=\"noopener\" alt=\"https:\/\/onnx.ai\/about.html?utm_source=chatgpt.com\" class=\"flex h-4.5 overflow-hidden rounded-xl px-2 text-[9px] font-medium transition-colors duration-150 ease-in-out text-token-text-secondary! bg-[#F4F4F4]! dark:bg-[#303030]!\" target=\"_blank\"\u003e\u003cspan class=\"relative start-0 bottom-0 flex h-full w-full items-center\"\u003e\u003cspan class=\"flex h-4 w-full items-center justify-between\"\u003e\u003cspan class=\"max-w-[15ch] grow truncate overflow-hidden text-center\"\u003eonnx.ai\u003c\/span\u003e\u003cspan class=\"-me-1 flex h-full items-center rounded-full px-1 text-[#8F8F8F]\"\u003e+2\u003c\/span\u003e\u003c\/span\u003e\u003cspan class=\"flex h-4 w-full items-center justify-between absolute\"\u003e\u003cspan class=\"max-w-[15ch] grow truncate overflow-hidden text-center\"\u003eonnx.ai\u003c\/span\u003e\u003cspan class=\"-me-1 flex h-full items-center rounded-full px-1 text-[#8F8F8F]\"\u003e+2\u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/a\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli data-start=\"1165\" data-end=\"1334\"\u003e\n\n\u003cp data-start=\"1167\" data-end=\"1334\"\u003e\u003cstrong data-start=\"1167\" data-end=\"1187\"\u003eÉcosystème étendu\u003c\/strong\u003e : De nombreux frameworks prennent en charge l'exportation vers ONNX ; des outils de visualisation et d'optimisation sont disponibles. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003cspan class=\"ms-1 inline-flex max-w-full items-center relative top-[-0.094rem] animate-[show_150ms_ease-in]\" data-testid=\"webpage-citation-pill\"\u003e\u003ca href=\"https:\/\/github.com\/onnx\/onnx?utm_source=chatgpt.com\" rel=\"noopener\" alt=\"https:\/\/github.com\/onnx\/onnx?utm_source=chatgpt.com\" class=\"flex h-4.5 overflow-hidden rounded-xl px-2 text-[9px] font-medium transition-colors duration-150 ease-in-out text-token-text-secondary! bg-[#F4F4F4]! dark:bg-[#303030]!\" target=\"_blank\"\u003e\u003cspan class=\"relative start-0 bottom-0 flex h-full w-full items-center\"\u003e\u003cspan class=\"flex h-4 w-full items-center justify-between\"\u003e\u003cspan class=\"max-w-[15ch] grow truncate overflow-hidden text-center\"\u003eGitHub\u003c\/span\u003e\u003cspan class=\"-me-1 flex h-full items-center rounded-full px-1 text-[#8F8F8F]\"\u003e+2\u003c\/span\u003e\u003c\/span\u003e\u003cspan class=\"flex h-4 w-full items-center justify-between absolute\"\u003e\u003cspan class=\"max-w-[15ch] grow truncate overflow-hidden text-center\"\u003eonnx.ai\u003c\/span\u003e\u003cspan class=\"-me-1 flex h-full items-center rounded-full px-1 text-[#8F8F8F]\"\u003e+2\u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/a\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e\n\u003chr data-start=\"1336\" data-end=\"1339\"\u003e\n\u003ch3 data-start=\"1341\" data-end=\"1364\"\u003e🧭 Vision et valeurs\u003c\/h3\u003e\n\u003cp data-start=\"1366\" data-end=\"1633\"\u003eONNX aspire à offrir une liberté aux développeurs, à encourager l'innovation et à éviter que les équipes ne soient limitées par une dépendance à un framework spécifique. L'objectif est un monde de l'IA où les modèles et les outils peuvent collaborer sans barrières artificielles. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003cspan class=\"ms-1 inline-flex max-w-full items-center relative top-[-0.094rem] animate-[show_150ms_ease-in]\" data-testid=\"webpage-citation-pill\"\u003e\u003ca href=\"https:\/\/onnx.ai\/about.html?utm_source=chatgpt.com\" rel=\"noopener\" alt=\"https:\/\/onnx.ai\/about.html?utm_source=chatgpt.com\" class=\"flex h-4.5 overflow-hidden rounded-xl px-2 text-[9px] font-medium transition-colors duration-150 ease-in-out text-token-text-secondary! bg-[#F4F4F4]! dark:bg-[#303030]!\" target=\"_blank\"\u003e\u003cspan class=\"relative start-0 bottom-0 flex h-full w-full items-center\"\u003e\u003cspan class=\"flex h-4 w-full items-center justify-between overflow-hidden\"\u003e\u003cspan class=\"max-w-[15ch] grow truncate overflow-hidden text-center\"\u003eonnx.ai\u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/a\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003chr data-start=\"1635\" data-end=\"1638\"\u003e\n\u003ch3 data-start=\"1640\" data-end=\"1668\"\u003e🧰 Possibilités d'utilisation\u003c\/h3\u003e\n\u003cul data-start=\"1670\" data-end=\"2107\"\u003e\n\n\u003cli data-start=\"1670\" data-end=\"1806\"\u003e\n\n\u003cp data-start=\"1672\" data-end=\"1806\"\u003eÉchange de modèles entre différents frameworks d'IA, par exemple, phase d'entraînement dans un outil et déploiement dans un autre\u003c\/p\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli data-start=\"1807\" data-end=\"1882\"\u003e\n\n\u003cp data-start=\"1809\" data-end=\"1882\"\u003eUtilisation dans des environnements de production à faible latence grâce à ONNX Runtime\u003c\/p\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli data-start=\"1883\" data-end=\"1959\"\u003e\n\n\u003cp data-start=\"1885\" data-end=\"1959\"\u003eUtilisation dans des environnements accélérés par le matériel (par ex. GPU, appareils Edge)\u003c\/p\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli data-start=\"1960\" data-end=\"2018\"\u003e\n\n\u003cp data-start=\"1962\" data-end=\"2018\"\u003eVisualisation, débogage et optimisation de modèles\u003c\/p\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli data-start=\"2019\" data-end=\"2107\"\u003e\n\n\u003cp data-start=\"2021\" data-end=\"2107\"\u003eRecherche et prototypage, par exemple pour le développement de nouvelles architectures ou une itération rapide\u003c\/p\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e","brand":"Antus","offers":[{"title":"Default Title","offer_id":53599620825420,"sku":null,"price":1.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0933\/9011\/1052\/files\/Screenshot2025-10-14122015.png?v=1760437243","url":"https:\/\/antus.de\/fr\/products\/onnx-format-fur-ki-modelle-zur-interoperabilitat-zwischen-frameworks-%f0%9f%9b%a0%ef%b8%8f","provider":"Antus","version":"1.0","type":"link"}