mirror of
https://github.com/saymrwulf/onnxruntime.git
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764 lines
48 KiB
HTML
764 lines
48 KiB
HTML
<!DOCTYPE html>
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<html lang="en">
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<head>
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<script>
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window.dataLayer = window.dataLayer || [];
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function gtag(){dataLayer.push(arguments);}
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gtag('js', new Date());
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gtag('config', 'UA-156955408-1');
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<meta http-equiv="X-UA-Compatible" content="IE=edge" />
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<title>ONNX Runtime | Home</title>
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<link rel="icon" href="./images/ONNXRuntime-Favicon.png" type="image/gif" sizes="16x16">
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<link rel="stylesheet" href="css/fonts.css">
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<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css">
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<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
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<link rel="stylesheet" href="css/custom.css?v1.6">
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<link rel="stylesheet" href="css/responsive.css?v1.6">
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</head>
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<body>
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<a class="skip-main" id="topContent" href="#skipMain">Skip to main content</a>
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<div class="main-wrapper">
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<div class="top-banner-bg">
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<header class="fixed-top header-content">
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<nav class="navbar navbar-expand-md navbar-custom" aria-label="Main menu">
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<a id="ONNXLogo" class="navbar-brand" href="./index.html">
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<img src="images/svg/ONNX-Runtime-logo.svg" class="d-inline-block align-top onnx-logo" alt="ONNX Runtime Home" />
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</a>
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<button class="navbar-toggler p-0" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
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<span class="navbar-toggler-icon"></span>
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</button>
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<div class="collapse navbar-collapse border-md-top mt-md-0 mt-2" id="navbarNav">
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<div class="mr-auto"></div>
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<div class="my-md-2 mb-0 mt-2 my-lg-0 pl-3 pl-md-0">
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<ul class="navbar-nav navbar-nav mr-auto text-uppercase" id="navigation">
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<li class="nav-item">
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<a class="nav-link pr-3 btn-getStarted" href="JavaScript:void(0);">Get Started</a>
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</li>
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<li class="nav-item">
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<a class="nav-link pr-3 btn-getStarted" href="./docs">Docs</a>
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</li>
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<li class="nav-item">
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<a class="nav-link pr-3" target="_blank" href="https://cloudblogs.microsoft.com/opensource/tag/onnx">News</a>
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</li>
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<li class="nav-item">
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<a class="nav-link pr-3" href="./about.html">About</a>
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</li>
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<li class="nav-item">
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<a class="nav-link" target="_blank" href="http://github.com/microsoft/onnxruntime">GitHub</a>
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</li>
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</ul>
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</div>
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</div>
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</nav>
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</header>
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<div role="main" id="skipMain" tabindex="-1">
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<div class="container px-md-4 px-lg-5 pt-5 mx-auto text-center">
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<h1 class="pt-3 pb-3 pt-md-5 pb-lg-3 px-md-4 px-lg-5 mt-5 mb-0">Optimize and Accelerate Machine Learning Inferencing and Training</h1>
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</div>
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<div class="outer-container mx-auto pt-md-5 pt-3">
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<section class="py-md-5 pt-3 pb-4 blue-title-columns">
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<div class="container-fluid">
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<div class="row equalHeight">
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<div class="col-12 col-md-4 mb-2 mb-md-0">
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<div class="row">
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<div class="col-2 col-md-3 col-xl-2">
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<div class="icon-container">
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<img src="images/svg/icon-4.svg" alt="" />
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</div>
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</div>
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<div class="col-10 col-sm-9 col-xl-10 pl-sm-0 pl-md-3 pl-lg-0 pl-xl-3">
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<h2 class="mr-xl-5 blue-text">Speed up machine learning process</h2>
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<p class="mr-xl-5 mb-md-0">Built-in optimizations that deliver up to 17X faster inferencing and up to 1.4X faster training
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</p>
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</div>
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</div>
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</div>
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<div class="col-12 col-md-4 mb-2 mb-md-0">
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<div class="row">
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<div class="col-2 col-md-3 col-xl-2">
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<div class="icon-container">
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<img src="images/svg/icon-2.svg" alt="" />
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</div>
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</div>
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<div class="col-10 col-sm-9 col-xl-10 pl-sm-0 pl-md-3 pl-lg-0 pl-xl-3">
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<h2 class="mr-xl-5 blue-text">Plug into your existing technology stack</h2>
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<p class="mr-xl-5 mb-md-0">Support for a variety of frameworks, operating systems and hardware platforms</p>
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</div>
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</div>
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</div>
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<div class="col-12 col-md-4">
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<div class="row">
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<div class="col-2 col-md-3 col-xl-2">
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<div class="icon-container">
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||
<img src="images/svg/icon-3.svg" alt="" />
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</div>
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</div>
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<div class="col-10 col-sm-9 col-xl-10 pl-sm-0 pl-md-3 pl-lg-0 pl-xl-3">
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<h2 class="mr-xl-5 blue-text">Build using proven technology</h2>
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<p class="mr-xl-5 mb-0">Used in Office 365, Visual Studio and Bing, delivering half Trillion inferences every day</p>
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</div>
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</div>
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</div>
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</div>
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</div>
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<div class="pt-1 pb-1 pt-md-3 pb-lg-3 px-5 mt-5 mb-0 alert alert-dark alert-dismissible fade show" role="alert">
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Please help us improve ONNX Runtime by participating in our <a href="https://ncv.microsoft.com/UySXuzobM9">customer survey.</a>
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<button type="button" class="close" data-dismiss="alert" aria-label="Close">
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<span aria-hidden="true">×</span>
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</button>
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</div>
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</section>
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<section class="py-md-5 pb-4 pt-4 get-started-section border-top" id="getStartedTable">
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<div class="container-fluid">
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<noscript>
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<div class="javascript-is-disabled row">
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<div class="col">
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<div class="ns-callout">
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<h2>Please enable JavaScript to use the interactive installation guide.</h2>
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<p class="mb-0">Need help enabling JavaScript? Follow the instructions <a href="https://www.whatismybrowser.com/guides/how-to-enable-javascript/auto" target="_blank">here</a>.</p>
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</div>
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</div>
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</div>
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</noscript>
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<div class="row ml-0">
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<div class="col">
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<div class="text-center pb-3">
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<h2 class="section-heading">Get Started Easily</h2>
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</div>
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</div>
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</div>
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<div>
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<ul class="tbl_tablist" role="tablist">
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<li id="OI_tab" class="tbl_tab" aria-controls="panel1" aria-selected="true" role="tab"
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tabindex="0">
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Optimize Inferencing
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</li>
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<li id="OT_tab" class="tbl_tab" aria-controls="panel2" role="tab" aria-selected="false"
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tabindex="0">
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Optimize Training
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</li>
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||
</ul>
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<div id="panel1" class="tbl_panel" aria-labelledby="tab1" role="tabpanel" aria-hidden="false">
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<div class="row ml-0">
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||
<div class="col">
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<div class="row r-wrap mb-1 mb-md-0 mr-0">
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<div class="col-md-3 r-heading">
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<h3 id="selectOS">Platform</h3>
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||
<p id="decriptionOS" class="sr-only">Platform list contains six items</p>
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||
</div>
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<div class="col-md-9 r-content pr-0 pl-md-4" role="listbox" id="listbox-1" aria-labelledby="selectOS" aria-describedby="decriptionOS">
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<div class="row os">
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<div class="col-lg-2dot4 col r-option version" role="option" tabindex="0" aria-selected="false" id="windows">
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<span>Windows</span>
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</div>
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<div class="col-lg-2dot4 col r-option version" role="option" tabindex="-1" aria-selected="false" id="linux">
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<span>Linux</span>
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</div>
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<div class="col-lg-2dot4 col r-option version" role="option" tabindex="-1" aria-selected="false" id="mac">
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<span>Mac</span>
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</div>
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<div class="col-lg-2dot4 col r-option version" role="option" tabindex="-1" aria-selected="false" id="android">
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<span>Android</span>
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</div>
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<div class="col-lg-2dot4 col r-option version" role="option" tabindex="-1" aria-selected="false" id="ios">
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<span>iOS</span>
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</div>
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<div class="col-lg-2dot4 col r-option version" role="option" tabindex="-1" aria-selected="false" id="web">
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<span>Web Browser (Preview)</span>
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||
</div>
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||
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||
</div>
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||
</div>
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||
</div>
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||
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<div class="row r-wrap mb-1 mb-md-0 mr-0">
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||
<div class="col-md-3 r-heading">
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<h3 id="selectLanguage">API</h3>
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||
<p id="decriptionLanguage" class="sr-only">API list contains eight items</p>
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||
</div>
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||
<div class="col-md-9 r-content pr-0 pl-md-4" role="listbox" id="listbox-2" aria-labelledby="selectLanguage" aria-describedby="decriptionLanguage">
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<div class="row language">
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||
<div class="col-lg-2dot4 col r-option" role="option" tabindex="0" aria-selected="false" id="Python"><span>Python</span></div>
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<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="C++"><span>C++</span></div>
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<div class="col-lg-2dot4 col r-option" aria-selected="false" role="option" tabindex="-1" id="C#"><span><abbr>C#</abbr></span></div>
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<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="C-API"><span>C</span></div>
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||
<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="Java"><span>Java</span></div>
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||
<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="JS"><span>JS</span></div>
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||
<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="objectivec"><span>Obj-C</span></div>
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||
<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="WinRT"><span>WinRT</span></div>
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||
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||
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||
</div>
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||
</div>
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||
</div>
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||
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||
<div class="row r-wrap mb-1 mb-md-0 mr-0">
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||
<div class="col-md-3 r-heading ">
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||
<h3 id="selectArchitecture" class="align-self-center">Architecture</h3>
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||
<p id="decriptionArchitecture" class="sr-only">Architecture list contains five items</p>
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||
</div>
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||
<div class="col-md-9 r-content pr-0 pl-md-4" role="listbox" id="listbox-3" aria-labelledby="selectArchitecture" aria-describedby="decriptionArchitecture">
|
||
<div class="row architecture">
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||
<div class="col-lg-2dot4 col r-option" role="option" tabindex="0" aria-selected="false" id="X64">
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||
<span><abbr>X64</abbr></span></div>
|
||
<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="X86">
|
||
<span><abbr>X86</abbr></span></div>
|
||
<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="ARM64">
|
||
<span><abbr>ARM64</abbr></span></div>
|
||
<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="ARM32">
|
||
<span><abbr>ARM32</abbr></span></div>
|
||
<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="Power">
|
||
<span><abbr>IBM Power</abbr></span></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="row r-wrap mb-1 mb-md-0 mr-0">
|
||
<div class="col-md-3 r-heading">
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||
<h3 id="selectHardwareAcceleration">Hardware Acceleration</h3>
|
||
<p id="decriptionHardwareAcceleration" class="sr-only">Hardware Acceleration list contains fourteen items</p>
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||
</div>
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||
<div class="col-md-9 r-content pr-0 pl-md-4" role="listbox" id="listbox-4" aria-labelledby="selectHardwareAcceleration" aria-describedby="decriptionHardwareAcceleration">
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||
<div class="row hardwareAcceleration">
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||
<div class="col-lg-2dot5 col r-option version" role="option" tabindex="0" aria-selected="false" id="DefaultCPU">
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||
<span>Default <abbr>CPU</abbr></span></div>
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||
<div class="col-lg-2dot5 col-md-3 r-option version" role="option" tabindex="-1" aria-selected="false" id="CoreML">
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||
<span>CoreML </span></div>
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||
<div class="col-lg-2dot5 col r-option version" role="option" tabindex="-1" aria-selected="false" id="CUDA">
|
||
<span><abbr>CUDA</abbr></span></div>
|
||
<div class="col-lg-2dot5 col r-option version" role="option" tabindex="-1" aria-selected="false" id="DirectML">
|
||
<span>Direct<abbr>ML</abbr></span></div>
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||
<div class="col-lg-2dot5 col r-option version" role="option" tabindex="-1" aria-selected="false" id="DNNL">
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||
<span><abbr>oneDNN</abbr></span></div>
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||
<div class="col-lg-2dot5 col-md-3 r-option version" role="option" tabindex="-1" aria-selected="false" id="OpenVINO">
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||
<span>OpenVINO</span></div>
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||
<div class="col-lg-2dot5 col r-option version" role="option" tabindex="-1" aria-selected="false" id="TensorRT">
|
||
<span>Tensor<abbr>RT</abbr></span></div>
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||
<div class="col-lg-2dot5 col-md-3 r-option version" role="option" tabindex="-1" aria-selected="false" id="NNAPI">
|
||
<span>NNAPI </span></div>
|
||
<div class="col-lg-2dot5 col-md-3 r-option version" role="option" tabindex="-1" aria-selected="false" id="ACL">
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||
<span>ACL (Preview)</span></div>
|
||
<div class="col-lg-2dot5 col-md-3 r-option version" role="option" tabindex="-1" aria-selected="false" id="ArmNN">
|
||
<span>ArmNN (Preview)</span></div>
|
||
<div class="col-lg-2dot5 col-md-3 r-option version" role="option" tabindex="-1" aria-selected="false" id="MIGraphX">
|
||
<span>MIGraphX (Preview)</span></div>
|
||
<div class="col-lg-2dot5 col-md-3 r-option version" role="option" tabindex="-1" aria-selected="false" id="NUPHAR">
|
||
<span><abbr>NUPHAR (Preview)</abbr></span></div>
|
||
<div class="col-lg-2dot5 col-md-3 r-option version" role="option" tabindex="-1" aria-selected="false" id="RockchipNPU">
|
||
<span>Rockchip NPU (Preview)</span></div>
|
||
<div class="col-lg-2dot5 col-md-3 r-option version" role="option" tabindex="-1" aria-selected="false" id="VitisAI">
|
||
<span>Vitis AI (Preview)</span></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="row r-wrap command-block row-eq-height mr-0">
|
||
<div class="col-md-3 r-heading">
|
||
<h3 id="selectRunCommand">Installation Instructions</h3>
|
||
</div>
|
||
<div class="col-md-9 r-content pr-0 pl-md-4">
|
||
<div class="row">
|
||
<div class="col r-option command-container" id="command" role="status">
|
||
<span>
|
||
Please select a combination of resources
|
||
</span>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
<div id="panel2" class="tbl_panel" aria-labelledby="tab2" role="tabpanel" aria-hidden="true">
|
||
<div class="row ml-0">
|
||
<div class="col">
|
||
<div class="row r-wrap mb-1 mb-md-0 mr-0">
|
||
<div class="col-md-3 r-heading">
|
||
<h3 id="ot_selectOS">Platform</h3>
|
||
<p id="ot_decriptionOS" class="sr-only">Platform list contains three items</p>
|
||
</div>
|
||
<div class="col-md-9 r-content pr-0 pl-md-4" role="listbox" id="ot_listbox-1" aria-labelledby="ot_selectOS" aria-describedby="ot_decriptionOS">
|
||
<div class="row ot_os">
|
||
<div class="col r-option selected" role="option" tabindex="0" aria-selected="true" id="ot_linux"><span>Linux</span></div>
|
||
<div class="col r-option" role="option" tabindex="-1" aria-selected="false" id="ot_windows"><span>Windows</span></div>
|
||
<div class="col r-option" role="option" tabindex="-1" aria-selected="false" id="ot_mac"><span>Mac</span></div>
|
||
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="row r-wrap mb-1 mb-md-0 mr-0">
|
||
<div class="col-md-3 r-heading">
|
||
<h3 id="ot_selectLanguage">API</h3>
|
||
<p id="ot_decriptionLanguage" class="sr-only">API list contains three items</p>
|
||
</div>
|
||
<div class="col-md-9 r-content pr-0 pl-md-4" role="listbox" id="ot_listbox-2" aria-labelledby="ot_selectLanguage" aria-describedby="ot_decriptionLanguage">
|
||
<div class="row ot_language">
|
||
<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="ot_PyTorch18"><span>PyTorch 1.8.1</span></div>
|
||
<div class="col-lg-2dot4 col r-option selected" role="option" tabindex="0" aria-selected="true" id="ot_PyTorch19"><span>PyTorch 1.9</span></div>
|
||
<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="ot_C++"><span>C++</span></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="row r-wrap mb-1 mb-md-0 mr-0">
|
||
<div class="col-md-3 r-heading ">
|
||
<h3 id="ot_selectArchitecture" class="align-self-center">Architecture</h3>
|
||
<p id="ot_decriptionArchitecture" class="sr-only">Architecture list contains one item</p>
|
||
</div>
|
||
<div class="col-md-9 r-content pr-0 pl-md-4" role="listbox" id="ot_listbox-3" aria-labelledby="ot_selectArchitecture" aria-describedby="ot_decriptionArchitecture">
|
||
<div class="row ot_architecture">
|
||
<div class="col r-option selected" role="option" tabindex="0" aria-selected="true" id="ot_X64">
|
||
<span><abbr>X64</abbr></span></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="row r-wrap mb-1 mb-md-0 mr-0">
|
||
<div class="col-md-3 r-heading">
|
||
<h3 id="ot_selectHardwareAcceleration">Hardware Acceleration</h3>
|
||
<p id="ot_decriptionHardwareAcceleration" class="sr-only">Hardware Acceleration list contains four items</p>
|
||
</div>
|
||
<div class="col-md-9 r-content pr-0 pl-md-4" role="listbox" id="ot_listbox-4" aria-labelledby="ot_selectHardwareAcceleration" aria-describedby="ot_decriptionHardwareAcceleration">
|
||
<div class="row ot_hardwareAcceleration">
|
||
<div class="col r-option version" role="option" tabindex="-1" aria-selected="false" id="ot_DefaultCPU"><span><abbr>Default CPU</abbr></span></div>
|
||
<div class="col r-option version selected" role="option" tabindex="0" aria-selected="true" id="ot_CUDA10"><span><abbr>CUDA 10.2</abbr></span></div>
|
||
<div class="col r-option version" role="option" tabindex="-1" aria-selected="false" id="ot_CUDA11"><span><abbr>CUDA 11.1</abbr></span></div>
|
||
<div class="col r-option version" role="option" tabindex="-1" aria-selected="false" id="ot_ROCm42"><span><abbr>ROCm 4.2 (Preview)</abbr></span></div>
|
||
<div class="col r-option version" role="option" tabindex="-1" aria-selected="false" id="ot_ROCm431"><span><abbr>ROCm 4.3.1 (Preview)</abbr></span></div>
|
||
<div class="col r-option version" role="option" tabindex="-1" aria-selected="false" id="ot_DNNL"><span><abbr>oneDNN</abbr></span></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="row r-wrap command-block row-eq-height mr-0">
|
||
<div class="col-md-3 r-heading">
|
||
<h3 id="ot_selectRunCommand">Installation Instructions</h3>
|
||
</div>
|
||
<div class="col-md-9 r-content pr-0 pl-md-4">
|
||
<div class="row">
|
||
<div class="col r-option command-container" id="ot_command" role="status">
|
||
<span>
|
||
Please select a combination of resources
|
||
</span>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</section>
|
||
|
||
<section class="py-md-5 pb-4 pt-4 border-top">
|
||
<div class="container-fluid">
|
||
<div class="row ml-0">
|
||
|
||
</div>
|
||
<div class="row equalHeight-1 blue-title-columns">
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
||
<img src="./images/logos/huggingface-logo.png" alt="Hugging Face logo">
|
||
</div>
|
||
<h3 class="mr-xl-5 mb-3 quote">“We use ONNX Runtime to easily deploy thousands of open-source state-of-the-art models in the Hugging Face model hub and accelerate private models for customers of the Accelerated Inference API on CPU and GPU.”<br/><br/>
|
||
<span class="quote-attribution">– Morgan Funtowicz, Machine Learning Engineer, Hugging Face</span></h3>
|
||
</div>
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
||
<img src="./images/logos/oracle-logo.png" alt="Oracle Logo">
|
||
</div>
|
||
<h3 class="mr-xl-5 mb-3 quote">“The ONNX Runtime API for Java enables Java developers and Oracle customers to seamlessly consume and execute ONNX machine-learning models, while taking advantage of the expressive power, high performance, and scalability of Java.”<br/><br/>
|
||
<span class="quote-attribution">– Stephen Green, Director of Machine Learning Research Group, Oracle</span></h3>
|
||
</div>
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
||
<img src="./images/logos/adobe-logo.png" alt="Adobe logo">
|
||
</div>
|
||
<h3 class="mr-xl-5 mb-3 quote">“With ONNX Runtime, Adobe Target got flexibility and standardization in one package: flexibility for our customers to train ML models in the frameworks of their choice, and standardization to robustly deploy those models at scale for fast inference, to deliver true, real-time personalized experiences.”<br/><br/>
|
||
<span class="quote-attribution">– Georgiana Copil, Senior Computer Scientist, Adobe</span></h3>
|
||
</div>
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
||
<img src="./images/logos/navitaire-amadeus-logo.png" alt="Navitaire Amadeus logo">
|
||
</div>
|
||
<h3 class="mr-xl-5 mb-3 quote">“With customers around the globe, we’re seeing increased interest in deploying more effective models to power pricing solutions via ONNX Runtime. ONNX Runtime’s performance has given us the confidence to use this solution with our customers with more extreme transaction volume requirements.”<br/><br/>
|
||
<span class="quote-attribution">– Jason Coverston, Product Director, Navitaire</span></h3>
|
||
</div>
|
||
</div>
|
||
<div class="row equalHeight-2 blue-title-columns">
|
||
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
||
<img src="./images/logos/vespa-logo.png" alt="Vespa logo">
|
||
</div>
|
||
<h3 class="mr-xl-5 mb-3 quote">“ONNX Runtime has vastly increased Vespa.ai’s capacity for evaluating large models, both in performance and model types we support.”<br/><br/>
|
||
<span class="quote-attribution">– Lester Solbakken, Principal Engineer, Vespa.ai, Verizon Media</span></h3>
|
||
</div>
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
||
<img src="./images/logos/PeakSpeed_logo.png" alt="PeakSpeed logo">
|
||
</div>
|
||
<h3 class="mr-xl-5 mb-3 quote">“Using a common model and code base, the ONNX Runtime allows Peakspeed to easily flip between platforms to help our customers choose the most cost-effective solution based on their infrastructure and requirements.”<br/><br/>
|
||
<span class="quote-attribution">– Oscar Kramer, Chief Geospatial Scientist, Peakspeed</span></h3>
|
||
</div>
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
||
<img src="./images/logos/sas-logo.png" alt="SAS logo">
|
||
</div>
|
||
<h3 class="mr-xl-5 mb-3 quote">“The unique combination of ONNX Runtime and SAS Event Stream Processing changes the game for developers and systems integrators by supporting flexible pipelines and enabling them to target multiple hardware platforms for the same AI models without bundling and packaging changes. This is crucial considering the additional build and test effort saved on an ongoing basis.”<br/><br/>
|
||
<span class="quote-attribution">– Saurabh Mishra, Senior Manager, Product Management, Internet of Things, SAS</span></h3>
|
||
</div>
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
||
<img src="./images/logos/visual-studio-logo.png" alt="Visual Studio logo">
|
||
</div>
|
||
<h3 class="mr-xl-4 mb-3 quote">“We use ONNX Runtime to accelerate model training for a 300M+ parameters model that powers code autocompletion in Visual Studio IntelliCode.”<br/><br/>
|
||
<span class="quote-attribution">– Neel Sundaresan, Director SW Engineering, Data & AI, Developer Division, Microsoft</span></h3>
|
||
</div>
|
||
</div>
|
||
<div class="row equalHeight-3 blue-title-columns">
|
||
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
||
<img src="./images/logos/ATLAS-logo.png" alt="ATLAS logo">
|
||
</div>
|
||
<h3 class="mr-xl-5 mb-3 quote">“At CERN in the ATLAS experiment, we have integrated the C++ API of ONNX Runtime into our software framework: Athena. We are currently performing inferences using ONNX models especially in the reconstruction of electrons and muons. We are benefiting from its C++ compatibility, platform*-to-ONNX converters (* Keras, TensorFlow, PyTorch, etc) and its thread safety.”<br/><br/>
|
||
<span class="quote-attribution">– ATLAS Experiment team, CERN (European Organization for Nuclear Research)</span></h3>
|
||
</div>
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
||
<img src="./images/logos/samtec-logo.png" alt="Samtec logo">
|
||
</div>
|
||
<h3 class="mr-xl-5 mb-3 quote">“We needed a runtime engine to handle the transition from data science land to a high-performance production runtime system. ONNX Runtime (ORT) simply ‘just worked’. Having no previous experience with ORT, I was able to easily convert my models, and had prototypes running inference in multiple languages within just a few hours. ORT will be my go-to runtime engine for the foreseeable future.”<br/><br/>
|
||
<span class="quote-attribution">– Bill McCrary, Application Architect, Samtec</span></h3>
|
||
</div>
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
||
<img src="./images/logos/topazlabs-logo.png" alt="Topaz Labs logo">
|
||
</div>
|
||
<h3 class="mr-xl-5 mb-3 quote">“ONNX Runtime’s simple C API with DirectML provider enabled Topaz Labs to add support for AMD GPUs and NVIDIA Tensor Cores in just a couple of days. Furthermore, our models load many times faster on GPU than any other frameworks. Even our larger models with about 100 million parameters load within seconds.”<br/><br/>
|
||
<span class="quote-attribution">– Suraj Raghuraman, Head of AI Engine, Topaz Labs</span></h3>
|
||
</div>
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
||
<img src="./images/logos/ghostwriter-logo.png" alt="GhostWriter.ai logo">
|
||
</div>
|
||
<h3 class="mr-xl-5 mb-3 quote">“At GhostWriter.AI, we integrate NLP models in different international markets and regulated industries. Our customers use many technology stacks and frameworks, which change over time. With ONNX Runtime, we can provide maximum performance combined with the total flexibility of making inferences using the technology our customers prefer, from Python to C#, deploying where they choose, from cloud to embedded systems.”<br/><br/>
|
||
<span class="quote-attribution">– Mauro Bennici, CTO, Ghostwriter.AI</span></h3>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
<section class="py-md-5 pb-4 pt-4 border-top">
|
||
<div class="container-fluid">
|
||
<div class="row ml-0">
|
||
<div class="col">
|
||
<div class="text-center pb-3">
|
||
<h2 class="section-heading">News & Announcements</h2>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="news row blue-title-columns">
|
||
<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
|
||
<h3 class="mr-xl-5 mb-3">Accelerate PyTorch transformer model training with ONNX Runtime – a deep dive</h3>
|
||
<span class="article-blurb">
|
||
ONNX Runtime (ORT) for PyTorch accelerates training large scale models across multiple GPUs with up to 37% increase in training throughput over PyTorch and up to 86% speed up when combined with DeepSpeed...
|
||
</span><br/><br/><a href="https://techcommunity.microsoft.com/t5/azure-ai/accelerate-pytorch-transformer-model-training-with-onnx-runtime/ba-p/2540471" target="_blank" class="link"><span class="link-content">Read
|
||
more</span><span class="link-arrow fa fa-angle-right"></span></a>
|
||
</div>
|
||
|
||
<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
|
||
<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/ort-pt-training.png" alt="ORT Training performance"></div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="news row blue-title-columns">
|
||
<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
|
||
<h3 class="mr-xl-5 mb-3">Accelerate PyTorch training with torch-ort</h3>
|
||
<span class="article-blurb">
|
||
With a simple change to your PyTorch training script, you can now speed up training large language models with torch_ort.ORTModule, running on the target hardware of your choice...
|
||
</span><br/><br/><a href="https://cloudblogs.microsoft.com/opensource/2021/07/13/accelerate-pytorch-training-with-torch-ort/" target="_blank" class="link"><span class="link-content">Read
|
||
more</span><span class="link-arrow fa fa-angle-right"></span></a>
|
||
</div>
|
||
<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
|
||
<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/ort-training-code.png" alt="Simple code for ORT Training"></div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="news row blue-title-columns ">
|
||
<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
|
||
<h3 class="mr-xl-5 mb-3">Journey to optimize large scale transformer model inference with ONNX Runtime</h3>
|
||
<span class="article-blurb">
|
||
Large-scale transformer models, such as GPT-2 and GPT-3, are among the most useful self-supervised transformer language models for natural language processing tasks such as language translation, question answering, passage summarization, text generation, and so on...</span><br/>
|
||
<br/><a href="https://cloudblogs.microsoft.com/opensource/2021/06/30/journey-to-optimize-large-scale-transformer-model-inference-with-onnx-runtime/" target="_blank" class="link"><span class="link-content">Read
|
||
more</span><span class="link-arrow fa fa-angle-right"></span></a>
|
||
</div>
|
||
<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
|
||
<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/gptc-vscode.png" alt="Deploying GTP-C in VS Code"></div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="news row blue-title-columns ">
|
||
<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
|
||
<h3 class="mr-xl-5 mb-3">ONNX Runtime release 1.8.1 previews support for accelerated training on AMD GPUs with the AMD ROCm™ Open Software Platform</h3>
|
||
<span class="article-blurb">
|
||
ONNX Runtime is an open-source project that is designed to accelerate machine learning across a wide range of frameworks, operating systems, and hardware platforms. Today, we are excited to announce a preview version of ONNX Runtime in release 1.8.1 featuring support for AMD Instinct™ GPUs facilitated by the AMD ROCm™ open software platform...</span>
|
||
<br/><br/><a href="https://cloudblogs.microsoft.com/opensource/2021/07/13/onnx-runtime-release-1-8-1-previews-support-for-accelerated-training-on-amd-gpus-with-the-amd-rocm-open-software-platform/" target="_blank" class="link"><span class="link-content">Read
|
||
more</span><span class="link-arrow fa fa-angle-right"></span></a>
|
||
</div>
|
||
<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
|
||
<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/ort-rocm.png" alt="ORT and ROCm"></div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="news row blue-title-columns ">
|
||
<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
|
||
<h3 class="mr-xl-5 mb-3">SAS and Microsoft collaborate to democratize the use of Deep Learning Models</h3>
|
||
<span class="article-blurb">
|
||
Artificial Intelligence (AI) developers enjoy the flexibility of choosing a model training framework of their choice. This includes both open-source frameworks as well as vendor-specific ones. While this is great for innovation, it does introduce the challenge of operationalization across different hardware platforms...</span>
|
||
<br/><br/><a href="https://communities.sas.com/t5/SAS-Communities-Library/SAS-and-Microsoft-collaborate-to-democratize-the-use-of-Deep/ta-p/730072" target="_blank" class="link"><span class="link-content">Read
|
||
more</span><span class="link-arrow fa fa-angle-right"></span></a>
|
||
</div>
|
||
<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
|
||
<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/sas-ort.png" alt="ORT and SAS"></div>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
<div class="news row blue-title-columns">
|
||
<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
|
||
<h3 class="mr-xl-5 mb-3">Optimizing BERT model for Intel CPU Cores using ONNX runtime default execution provider</h3>
|
||
<span class="article-blurb">
|
||
The performance improvements provided by ONNX Runtime powered by Intel® Deep Learning Boost: Vector Neural Network Instructions (Intel® DL Boost: VNNI) greatly improves performance of machine learning model execution for developers...
|
||
</span><br/><br/><a href="https://cloudblogs.microsoft.com/opensource/2021/03/01/optimizing-bert-model-for-intel-cpu-cores-using-onnx-runtime-default-execution-provider/" target="_blank" class="link"><span class="link-content">Read
|
||
more</span><span class="link-arrow fa fa-angle-right"></span></a>
|
||
</div>
|
||
|
||
<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
|
||
<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/ort_intel.png" alt="ORT and Intel AI"></div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
|
||
</div>
|
||
</section>
|
||
|
||
<section class="py-md-5 pb-4 pt-4 border-top">
|
||
<div class="container-fluid">
|
||
<div class="row ml-0">
|
||
<div class="col">
|
||
<div class="text-center pb-3">
|
||
<h2 class="section-heading">Resources</h2>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="row blue-title-columns">
|
||
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<button class="resources-img text-center p-0 border-0" data-src="WDww8ce12Mc" aria-label="Faster and Lighter Model Inference with ONNX Runtime from Cloud to Client">
|
||
<img class="btn-trigger" src="./images/video-model-inference-cloud-to-client.jpg" alt="Tech Community" />
|
||
</button>
|
||
|
||
</div>
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<button class="resources-img text-center p-0 border-0" data-src="pmb6cjngbcA" aria-label="ONNX Runtime speeds up Image Embedding model in Bing Semantic Precise Image Search">
|
||
<img class="btn-trigger" src="./images/video-bing-semantic-precise.jpg" alt="Tech Community" />
|
||
</button>
|
||
|
||
</div>
|
||
<div class="col-12 col-md-3 mb-4 mb-md-0">
|
||
<button class="resources-img text-center p-0 border-0" data-src="nRlnSy4Vbnc" aria-label="Scalable ML acceleration with ONNX Runtime">
|
||
<img class="btn-trigger" src="./images/video-scalable-ml.jpg" alt="Tech Community" />
|
||
</button>
|
||
</div>
|
||
|
||
<div class="col-12 col-md-3">
|
||
<button class="resources-img text-center p-0 border-0" data-src="Ij5MoUnLQ0E" aria-label="ONNX and ONNX Runtime">
|
||
<img class="btn-trigger" src="./images/video-research-prod.png" alt="Open Source" />
|
||
</button>
|
||
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
<section class="pt-md-5 pt-4 border-top">
|
||
<div class="container-fluid">
|
||
<div class="row ml-0">
|
||
<div class="col">
|
||
<div class="text-center pb-3">
|
||
<h2 class="section-heading">Hardware Ecosystem</h2>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="row blue-title-columns">
|
||
<div class="col-12 col-md-2 mb-4 mb-md-0"><div class="hw-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
||
<img class="hardware-nvidia-logo" src="./images/logos/nvidia.png" alt="NVIDIA logo">
|
||
</div></div>
|
||
<div class="col-12 col-md-10 mb-4 mb-md-0">
|
||
<h3 class="mr-xl-5 mb-3 hardware">“ONNX Runtime enables our customers to easily apply NVIDIA TensorRT’s powerful optimizations to machine learning models, irrespective of the training framework, and deploy across NVIDIA <abbr title="Graphics Processing Unit">GPUs</abbr> and edge devices.”<br/><br/>
|
||
<span class="quote-attribution">– Kari Ann Briski, Sr. Director, Accelerated Computing Software and <abbr>AI</abbr> Product, NVIDIA</span></h3>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="row blue-title-columns">
|
||
<div class="col-12 col-md-2 mb-4 mb-md-0"><div class="hw-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
||
<img class="intel-logo" src="./images/logos/intel-logo.png" alt="Intel logo">
|
||
</div></div>
|
||
<div class="col-12 col-md-10 mb-4 mb-md-0">
|
||
<h3 class="mr-xl-5 mb-3 hardware">“We are excited to support ONNX Runtime on the Intel® Distribution of OpenVINO™. This accelerates machine learning inference across Intel hardware and gives developers the flexibility to choose the combination of Intel hardware that best meets their needs from <abbr title="Central Processing Unit">CPU</abbr> to <abbr title="Vision Processing Unit">VPU</abbr> or <abbr title="Field Programmable Gate Array">FPGA</abbr>.”<br/><br/>
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<span class="quote-attribution">– Jonathan Ballon, Vice President and General Manager, Intel Internet of Things Group</span></h3>
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<h3 class="mr-xl-5 mb-3 hardware">“Xilinx is excited that Microsoft has announced Vitis™ AI interoperability and runtime support for ONNX Runtime, enabling developers to deploy machine learning models for inference to FPGA IaaS such as Azure NP series VMs and Xilinx edge devices.”<br/><br/>
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