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9f877ef2bc
updated winarm to use qnn EP ### Description <!-- Describe your changes. --> ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
160 lines
8.7 KiB
HTML
160 lines
8.7 KiB
HTML
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<title>ONNX Runtime | Windows Dev Kit 2023</title>
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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">ONNX Runtime + Windows Dev Kit 2023
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= NPU powered AI</h1>
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</div>
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<div class="outer-container mx-auto">
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<section class="pt-4 blue-title-columns">
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<div class="container-fluid">
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<div class="row">
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<div class="col-12 col-md-12 pr-5">
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<h2 class="blue-text">Delivering NPU powered AI capabilities in your apps</h2>
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<p>Windows Dev Kit 2023, aka Project Volterra, enables developers to build apps that
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unlock the power of the NPU hardware to accelerate AI/ML workloads delivering
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AI-enhanced features & experiences without compromising app performance.</p>
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<p>You can get started now and access the power of the NPU through the open source
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and cross-platform ONNX Runtime inference engine making it easy to run AI/ML
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models from popular machine learning frameworks like PyTorch and TensorFlow.</p>
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</div>
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</div>
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</div>
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<section class="pb-4 pt-4 blue-title-columns">
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</div>
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<div class="col-12 col-md-6 pr-5">
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<h2 class="blue-text">Get started on your Windows Dev Kit 2023 today</h2>
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<p>Follow these steps to setup your device to use ONNX Runtime (ORT) with the built
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in NPU:
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<ol>
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<li><a href="https://qpm.qualcomm.com/main/tools/details/qualcomm_ai_engine_direct"
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target="_blank">Download</a> the Qualcomm AI Engine Direct SDK (QNN SDK) </li>
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<li><a href="https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.QNN"
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target="_blank">Download</a> and install the ONNX Runtime with QNN
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package</li>
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<li>Start using the ONNX Runtime API in your application.</li>
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</ol>
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</p><br />
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<h2 class="blue-text">Optimizing models for the NPU</h2>
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<p><a href="https://onnx.ai" target="_blank">ONNX</a> is a standard format for
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representing ML models authored in frameworks like PyTorch, TensorFlow, and
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others. ONNX Runtime can run any ONNX model, however to make use of the NPU, you
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currently need to quantize the ONNX model to QDQ model.
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<p>See our <a
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href="https://github.com/microsoft/onnxruntime-inference-examples/tree/main/c_cxx/QNN_EP/mobilenetv2_classification"
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target="_blank">C# tutorial<a> for an example of how this is done.</p>
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<p>Many models can be optimized for the NPU using this process. Even if a model
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cannot be optimized for NPU, it can still be run by ONNX Runtime
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on the CPU.</p>
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<br />
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<h2 class="blue-text">Getting help</h2>
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<p>For help with ONNX Runtime, you can <a
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href="https://github.com/microsoft/onnxruntime/discussions"
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target="_blank">start a discussion</a> on GitHub or <a
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href="https://github.com/microsoft/onnxruntime/issues" target="_blank">file
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an issue</a>.
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<p>
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