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Prasanth Pulavarthi 2021-05-17 11:53:50 -07:00 committed by GitHub
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<div class="row equalHeight-1 blue-title-columns">
<div class="col-12 col-md-4 mb-4 mb-md-0">
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
<img class="huggingface-logo" src="./images/logos/huggingface-logo.png" alt="Hugging Face logo">
<img src="./images/logos/huggingface-logo.png" alt="Hugging Face logo">
</div>
<h3 class="mr-xl-5 mb-3 quote">&ldquo;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.&rdquo;<br/><br/>
<span class="quote-attribution">&ndash; Morgan Funtowicz, Machine Learning Engineer, Hugging Face</span></h3>
</div>
<div class="col-12 col-md-4 mb-4 mb-md-0">
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
<img class="sas-logo" src="./images/logos/sas-logo.png" alt="SAS logo">
<img src="./images/logos/sas-logo.png" alt="SAS logo">
</div>
<h3 class="mr-xl-5 mb-3 quote">&ldquo;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.&rdquo;<br/><br/>
<span class="quote-attribution">&ndash; Saurabh Mishra, Senior Manager, Product Management, Internet of Things, SAS</span></h3>
</div>
<div class="col-12 col-md-4 mb-4 mb-md-0">
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
<img class="oracle-logo" src="./images/logos/oracle-logo.png" alt="Oracle Logo">
<img src="./images/logos/oracle-logo.png" alt="Oracle Logo">
</div>
<h3 class="mr-xl-5 mb-3 quote">&ldquo;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.&rdquo;<br/><br/>
<span class="quote-attribution">&ndash; Stephen Green, Director of Machine Learning Research Group, Oracle</span></h3>
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<div class="col-12 col-md-4 mb-4 mb-md-0">
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
<img class="vespa-logo" src="./images/logos/vespa-logo.png" alt="Vespa logo">
<img src="./images/logos/vespa-logo.png" alt="Vespa logo">
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<h3 class="mr-xl-5 mb-3 quote">&ldquo;ONNX Runtime has vastly increased Vespa.ai’s capacity for evaluating large models, both in performance and model types we support.&rdquo;<br/><br/>
<span class="quote-attribution">&ndash; Lester Solbakken, Principal Engineer, Vespa.ai, Verizon Media</span></h3>
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<div class="col-12 col-md-4 mb-4 mb-md-0">
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
<img class="visual-studio-logo" src="./images/logos/visual-studio-logo.png" alt="Visual Studio logo">
<img src="./images/logos/visual-studio-logo.png" alt="Visual Studio logo">
</div>
<h3 class="mr-xl-4 mb-3 quote">&ldquo;We use ONNX Runtime to accelerate model training for a 300M+ parameters model that powers code autocompletion in Visual Studio IntelliCode.&rdquo;<br/><br/>
<span class="quote-attribution">&ndash; Neel Sundaresan, Director SW Engineering, Data & AI, Developer Division, Microsoft</span></h3>
</div>
<div class="col-12 col-md-4 mb-4 mb-md-0">
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
<img class="peakSpeed-logo" src="./images/logos/PeakSpeed_logo.png" alt="PeakSpeed logo">
<img src="./images/logos/PeakSpeed_logo.png" alt="PeakSpeed logo">
</div>
<h3 class="mr-xl-5 mb-3 quote">&ldquo;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.&rdquo;<br/><br/>
<span class="quote-attribution">&ndash; Oscar Kramer, Chief Geospatial Scientist, Peakspeed</span></h3>
</div>
</div>
<div class="row equalHeight-3 blue-title-columns">
<div class="col-12 col-md-12 mb-4 mb-md-0">
<div class="col-12 col-md-4 mb-4 mb-md-0">
<div class="sponsor-logo pb-4 pb-md-5 mr-xl-5 text-center">
<img class="atlas-logo" src="./images/logos/ATLAS-logo.png" alt="ATLAS logo">
<img src="./images/logos/samtec-logo.png" alt="Samtec logo">
</div>
<h3 class="mr-xl-5 mb-3 quote">&ldquo;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.&rdquo;<br/><br/>
<span class="quote-attribution">&ndash; Bill McCrary, Application Architect, Samtec</span></h3>
</div>
<div class="col-12 col-md-4 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">&ldquo;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.&rdquo;<br/><br/>
<span class="quote-attribution">&ndash; ATLAS Experiment team, CERN (European Organization for Nuclear Research)</span></h3>