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### Description Use a shared file for the onnxruntime.ai website nav bar instead of implementing on each page. ### Motivation and Context Simplifies site updates. Changes staged at https://prasanthpul.github.io/onnxruntime/
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HTML
169 lines
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10 KiB
HTML
<!DOCTYPE html>
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<html lang="en">
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<head>
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<script async src="https://www.googletagmanager.com/gtag/js?id=UA-156955408-1"></script>
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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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</script>
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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 | PyTorch</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="css/custom.css">
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<link rel="stylesheet" href="css/responsive.css">
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</head>
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<body>
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<a class="skip-main" 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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<div w3-include-html="header.html"></div>
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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">PyTorch + ONNX Runtime = Production</h1>
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</div>
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<div class="outer-container mx-auto">
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<section class="py-md-5 pt-5 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-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">Deploy anywhere</h2>
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<p class="mr-xl-5 mb-md-0">Run PyTorch models on cloud, desktop, mobile, IoT, and even in the browser
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</p>
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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 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">Boost performance</h2>
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<p class="mr-xl-5 mb-md-0">Accelerate PyTorch models to improve user experience and reduce costs
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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-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">Improve time to market</h2>
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<p class="mr-xl-5 mb-md-0">Used by Microsoft and many others for their production PyTorch workloads
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</p>
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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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</section>
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<section class="pb-4 pt-4 blue-title-columns">
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<div class="container-fluid">
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<div class="row pb-4 pb-md-5">
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<div class="col-12 col-md-6 order-md-2 mb-4 mb-md-0 text-center pr-10">
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<img src="./images/pytorch-builtin.png" class="img-fluid">
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</div>
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<div class="col-12 col-md-6 pr-5">
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<h2>Native support in PyTorch</h2>
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<p>PyTorch includes support for ONNX through the <a href="https://pytorch.org/docs/stable/onnx.html" target="_blank" class="link">torch.onnx</a> APIs to simplify exporting your PyTorch model to the portable ONNX format.
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The ONNX Runtime team maintains these exporter APIs to ensure a high level of compatibility with PyTorch models.
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</p>
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<p><a href="https://onnxruntime.ai/docs/tutorials/accelerate-pytorch/pytorch.html#convert-model-to-onnx" class="link">Get your PyTorch models ready for optimized deployment >></a></p>
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</div>
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</div>
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<div class="row pb-4 pb-md-5">
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<div class="col-12 col-md-6 mb-4 mb-md-0 text-center pr-10">
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<img src="./images/python-free.png" class="img-fluid">
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</div>
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<div class="col-12 col-md-6 pr-5">
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<h2>Python not required</h2>
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<p>Training PyTorch models requires Python but that can be a significant obstacle to deploying PyTorch models to many production environments, especially Android and iOS mobile devices.
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ONNX Runtime is designed for production and provides APIs in C/C++, C#, Java, and Objective-C, helping create a bridge from your PyTorch training environment to a successful PyTorch production deployment.
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</p>
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<p><a href="https://onnxruntime.ai/docs/api/" class="link">See ONNX Runtime's many Python-free APIs >></a></p>
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</div>
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</div>
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<div class="row">
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<div class="col-12 col-md-6 order-md-2 mb-4 mb-md-0 text-center pr-10">
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<img src="./images/performance.png" class="img-fluid">
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</div>
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<div class="col-12 col-md-6 pr-5">
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<h2>Lower latency, higher throughput</h2>
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<p>Better performance can help improve your user experience and lower your operating costs.
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A wide range of models from computer vision (ResNet, MobileNet, Inception, YOLO, super resolution, etc) to speech and NLP (BERT, RoBERTa, GPT-2, T5, etc) can benefit from ONNX Runtime's optimized performance.
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The ONNX Runtime team regularly benchmarks and optimizes top models for performance.
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ONNX Runtime also integrates with top hardware accelerator libraries like TensorRT and OpenVINO so you can get the best performance on the hardware available while using the same common APIs across all your target platforms.
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</p>
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<p><a href="https://cloudblogs.microsoft.com/opensource/tag/onnx" target="_blank" class="link">Check the latest on performance enhancements >></a></p>
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</div>
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</div>
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<div class="row pt-4 pt-md-5">
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<div class="col-12 col-md-6 mb-4 mb-md-0 text-center pr-10">
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<img src="./images/agility.png" class="img-fluid">
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</div>
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<div class="col-12 col-md-6 pr-5">
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<h2>Get innovations into production faster</h2>
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<p>Development agility is a key factor in overall costs.
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ONNX Runtime was built on the experience of taking PyTorch models to production in high scale services like Microsoft Office, Bing, and Azure. It used to take weeks and months to take a model from R&D to production.
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With ONNX Runtime, models can be ready to be deployed at scale in hours or days.
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</p>
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<p><a href="community.html" class="link">See what ONNX Runtime users are saying >></a></p>
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</div>
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</div>
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</div>
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</section>
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</div>
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