mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-14 18:12:05 +00:00
a0f3e30de6
* initial setup and rename "how to" to "setup" * move API to main nav * move api to main nav * add get starated, rework nav order * rename to install move mds out of install section * update api nav and home page * add install docs and python qs updates * python get started work * remove c and obj c for now * move java, python, and obj-c docs under api folder * move java api html to iframe (ugh) * remove api docs w/o details, move api text getstar * remove api docs wo detail updates get started * remvoe iframes * move eco system to main nav * fix api buttons * added more examples moved intro to ORT * fix links * fix get started titles * fix get started titles * fix more links * fix more links * more link fixes * fix nav remove inferencing and training subnav * fix top nav remove inference and training nav * fix title * fix tutorials nav hierarchy * fix python api button * add tenorflow keras example * fix quickstart toc * add imports fix spacing * fix links * update nav and python get started page * move ort training example, add coming soon for iot * update C# get started * fix spacing on quantization * Add some js get started content * fix formatting * fix typo * removed onnx-pytorch and onnx-tf * updated pip install torch and added links iot page * added pytorch tutorial heirarchy * updated web to docs soon added release blog link * add web link
203 lines
15 KiB
HTML
203 lines
15 KiB
HTML
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<title>ONNX Runtime | About</title>
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<a class="nav-link pr-3" href="./about.html">About</a>
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<h1 class="mb-3 blue-text">About</h1>
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<p class="mb-2">
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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. It enables acceleration of machine learning inferencing across all of your deployment targets using a single set of <abbr title="Application Program Interface">API</abbr>. ONNX Runtime automatically parses through your model to identify optimization opportunities and provides access to the best hardware acceleration available.
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ONNX Runtime also offers training acceleration, which incorporates innovations from Microsoft Research and is proven across production workloads like Office 365, Bing and Visual Studio.
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<a href="https://github.com/microsoft/onnxruntime" target="_blank" class="link ft-20"><span class="link-content">Join us on Github</span><span class="link-arrow fa fa-angle-right"></span></a>
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<img src="./images/optimization-acceleration.png" alt="Illustration of a computational graph representing ONNX format and the acceleration enabled by ONNX Runtime" class="img-fluid">
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<h2>Optimization and acceleration</h2>
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<p>
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Run any ONNX model using a single set of inference <a href="https://www.onnxruntime.ai/docs/api/" target="_blank" class="link"><abbr title="Application Program Interface">API</abbr>s</a> that provide access to the best hardware acceleration available. Built-in optimization features trim and consolidate nodes without impacting model accuracy. Additionally, full backwards <a href="https://www.onnxruntime.ai/docs/resources/compatibility.html" target="_blank" class="link">compatibility</a> for ONNX and ONNX-<abbr>ML</abbr> ensures all ONNX models can be inferenced.
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<div class="col-12 col-md-6 order-md-1 pr-10">
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<h2><abbr title="Application Program Interface">API</abbr> and platform support</h2>
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<p>
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Take advantage of the benefits of ONNX Runtime without changing your technology stack. Access ONNX Runtime using your preferred <a href="https://www.onnxruntime.ai/docs/api/" target="_blank" class="link"><abbr title="Application Program Interface">API</abbr></a> — <abbr>C#</abbr>, <abbr>C++</abbr>, C, Python, or Java. Support for Linux, Windows and Mac allows you to build and deploy applications without worry.
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<img src="./images/continuous-community-innovation.png" alt="Illustration of a lightbulb above three computers, all connected by lines; represents the community of ONNX Runtime contributors" class="img-fluid">
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</div>
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<div class="col-12 col-md-6 pr-10">
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<h2>Continuous community innovation</h2>
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<p>
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Our community of partners and contributors drives constant innovation. Partners provide ONNX compatible compilers and accelerators to ensure models are as efficient as possible. Our contributor community improves ONNX Runtime by contributing code, ideas and feedback. Join us on <a href="https://github.com/Microsoft/onnxruntime" target="_blank" class="link">GitHub</a>.
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<h2 class="mt-3 mt-md-0">Design principles</h2>
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<p class="mb-md-0 pr-0 pr-md-5">
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ONNX Runtime abstracts custom accelerators and runtimes to maximize their benefits across an ONNX model. To do this, ONNX Runtime partitions the ONNX model graph into subgraphs that align with available custom accelerators and runtimes. When operators are not supported by custom accelerators or runtimes, ONNX Runtime provides a default runtime that is used as the fallback execution — ensuring that any model will run.
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<p class="mr-xl-5 mb-2">Accelerate your machine learning inferencing and training today.</p>
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<p class="mr-xl-5 mb-2">ONNX Runtime is a community project. We encourage you to join the effort and contribute feedback, ideas and code.</p>
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<p class="text-copyright py-4 ml-md-5">Copyright © 2020 Microsoft. All rights reserved.
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