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HTML
234 lines
No EOL
14 KiB
HTML
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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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<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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<!-- Partial header.html Start-->
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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
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</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="col-12 col-md-4 mb-2 mb-md-0">
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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,
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IoT, and even in the browser
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</p>
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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="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
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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 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
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production PyTorch workloads
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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>
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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"
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role="alert">
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Please help us improve ONNX Runtime by participating in our <a
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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="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
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href="https://pytorch.org/docs/stable/onnx.html" target="_blank"
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class="link">torch.onnx</a> APIs to simplify exporting your PyTorch model to
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the portable ONNX format.
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The ONNX Runtime team maintains these exporter APIs to ensure a high level of
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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"
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class="link">Get your PyTorch models ready for optimized deployment >></a>
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</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
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deploying PyTorch models to many production environments, especially Android and
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iOS mobile devices.
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ONNX Runtime is designed for production and provides APIs in C/C++, C#, Java,
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and Objective-C, helping create a bridge from your PyTorch training environment
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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
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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
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costs.
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A wide range of models from computer vision (ResNet, MobileNet, Inception, YOLO,
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super resolution, etc) to speech and NLP (BERT, RoBERTa, GPT-2, T5, etc) can
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benefit from ONNX Runtime's optimized performance.
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The ONNX Runtime team regularly benchmarks and optimizes top models for
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performance.
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ONNX Runtime also integrates with top hardware accelerator libraries like
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TensorRT and OpenVINO so you can get the best performance on the hardware
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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"
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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
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in high scale services like Microsoft Office, Bing, and Azure. It used to take
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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
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>></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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