mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-14 18:12:05 +00:00
fce58d9e26
* fix training pkg urls * Update torch-ort instructions * Remove nightly from Get Started matrix * Add steps for training installation * Add torch-ort configure step for training installation * Add text for ROCm packages * Refine text for ROCm packages * Add ROCm preview packages * Revert ROCm instructions Co-authored-by: Nat Kershaw <nakersha@microsoft.com>
718 lines
44 KiB
HTML
718 lines
44 KiB
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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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Optimize Inferencing
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<p id="decriptionOS" class="sr-only">Platform list contains six items</p>
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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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<h3 id="selectArchitecture" class="align-self-center">Architecture</h3>
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<p id="decriptionArchitecture" class="sr-only">Architecture list contains four items</p>
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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">
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<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>
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<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="X86">
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<span><abbr>X86</abbr></span></div>
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<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="ARM64">
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<span><abbr>ARM64</abbr></span></div>
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<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="ARM32">
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<span><abbr>ARM32</abbr></span></div>
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</div>
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<div class="col-md-3 r-heading">
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<h3 id="selectHardwareAcceleration">Hardware Acceleration</h3>
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<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 r-option version" role="option" tabindex="-1" aria-selected="false" id="CUDA">
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<span><abbr>CUDA</abbr></span></div>
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<div class="col-lg-2dot5 col r-option version" role="option" tabindex="-1" aria-selected="false" id="DirectML">
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<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">
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<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">
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<span>NNAPI </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="ACL">
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<span>ACL (Preview)</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="ArmNN">
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<span>ArmNN (Preview)</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 (Preview)</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="MIGraphX">
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<span>MIGraphX (Preview)</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="NUPHAR">
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<span><abbr>NUPHAR (Preview)</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="RockchipNPU">
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<span>Rockchip NPU (Preview)</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="VitisAI">
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<span>Vitis AI (Preview)</span></div>
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</div>
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</div>
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</div>
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<div class="row r-wrap command-block row-eq-height mr-0">
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<h3 id="selectRunCommand">Installation Instructions</h3>
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<div class="row">
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<div class="col r-option command-container" id="command" role="status">
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<span>
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Please select a combination of resources
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</span>
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<div id="panel2" class="tbl_panel" aria-labelledby="tab2" role="tabpanel" aria-hidden="true">
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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="ot_selectOS">Platform</h3>
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<p id="ot_decriptionOS" class="sr-only">Platform list contains three items</p>
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</div>
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<div class="row ot_os">
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<div class="col r-option selected" role="option" tabindex="0" aria-selected="true" id="ot_linux"><span>Linux</span></div>
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<div class="col r-option" role="option" tabindex="-1" aria-selected="false" id="ot_windows"><span>Windows</span></div>
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<div class="col r-option" role="option" tabindex="-1" aria-selected="false" id="ot_mac"><span>Mac</span></div>
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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="ot_selectLanguage">API</h3>
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<p id="ot_decriptionLanguage" class="sr-only">API list contains three 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="ot_listbox-2" aria-labelledby="ot_selectLanguage" aria-describedby="ot_decriptionLanguage">
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<div class="row ot_language">
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<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>
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<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>
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<div class="col-lg-2dot4 col r-option" role="option" tabindex="-1" aria-selected="false" id="ot_C++"><span>C++</span></div>
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</div>
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</div>
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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="ot_selectArchitecture" class="align-self-center">Architecture</h3>
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<p id="ot_decriptionArchitecture" class="sr-only">Architecture list contains one item</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="ot_listbox-3" aria-labelledby="ot_selectArchitecture" aria-describedby="ot_decriptionArchitecture">
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<div class="row ot_architecture">
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<div class="col r-option selected" role="option" tabindex="0" aria-selected="true" id="ot_X64">
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<span><abbr>X64</abbr></span></div>
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</div>
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</div>
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</div>
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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="ot_selectHardwareAcceleration">Hardware Acceleration</h3>
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<p id="ot_decriptionHardwareAcceleration" class="sr-only">Hardware Acceleration list contains four 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="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_AMD"><span><abbr>ROCm 4.2 (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>
|
||
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|
||
</div>
|
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</div>
|
||
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|
||
</div>
|
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|
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</div>
|
||
|
||
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|
||
</section>
|
||
|
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<section class="py-md-5 pb-4 pt-4 border-top">
|
||
<div class="container-fluid">
|
||
<div class="row ml-0">
|
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|
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<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>
|
||
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|
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|
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<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>
|
||
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|
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|
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<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>
|
||
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|
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<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
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<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">
|
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|
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<div class="col-12 col-md-3 mb-4 mb-md-0">
|
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<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>
|
||
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|
||
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|
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<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
|
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<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>
|
||
</div>
|
||
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|
||
|
||
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|
||
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|
||
<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">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
|
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more</span><span class="link-arrow fa fa-angle-right"></span></a>
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|
||
<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>
|
||
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|
||
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||
<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 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 and simplify Scikit-learn model inference with ONNX Runtime</h3>
|
||
<span class="article-blurb">
|
||
Scikit-learn is one of the most useful libraries for general machine learning in Python. To minimize the cost of deployment and avoid discrepancies, deploying scikit-learn models to production usually leverages Docker containers and pickle, the object serialization module of the Python standard library...
|
||
</span><br/><br/><a href="https://cloudblogs.microsoft.com/opensource/2020/12/17/accelerate-simplify-scikit-learn-model-inference-onnx-runtime/" target="_blank" class="link"><span class="link-content">Read
|
||
more</span><span class="link-arrow fa fa-angle-right"></span></a>
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||
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|
||
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||
<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/skl-ort.png" alt="SKL and ORT logos"></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 scenario highlight: Vespa.ai integration</h3>
|
||
<span class="article-blurb">
|
||
Since its open source debut two years ago, ONNX Runtime has seen strong growth with performance improvements, expanded platform and device compatibility, hardware accelerator support, an extension to training acceleration, and more...
|
||
</span><br/><br/><a href="https://cloudblogs.microsoft.com/opensource/2020/12/14/onnx-runtime-vespa-ai-integration/" target="_blank" class="link"><span class="link-content">Read
|
||
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<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/logos/vespa-logo.png" alt="Vespa logo"></div>
|
||
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|
||
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|
||
|
||
|
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|
||
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||
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|
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|
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|
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|
||
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||
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|
||
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|
||
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|
||
<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>
|
||
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|
||
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|
||
|
||
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|
||
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|
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|
||
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|
||
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|
||
<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/>
|
||
<span class="quote-attribution">– Jonathan Ballon, Vice President and General Manager, Intel Internet of Things Group</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="Rockchip-logo" src="./images/logos/Rockchip-logo.png" alt="Rockchip logo">
|
||
</div></div>
|
||
<div class="col-12 col-md-10 mb-4 mb-md-0">
|
||
<h3 class="mr-xl-5 mb-3 hardware">“With support for ONNX Runtime, our customers and developers can cross the boundaries of the model training framework, easily deploy ML models in Rockchip NPU powered devices.”<br/><br/>
|
||
<span class="quote-attribution">– Feng Chen, Senior Vice President, Rockchip</span></h3>
|
||
</div>
|
||
</div>
|
||
|
||
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|
||
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|
||
<img class="xilinx-logo" src="./images/logos/xilinx-logo.png" alt="Xilinx logo">
|
||
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|
||
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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/>
|
||
<span class="quote-attribution">– Sudip Nag, Corporate Vice President, Software & AI Products, Xilinx</span></h3>
|
||
</div>
|
||
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|
||
|
||
|
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