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GPUs are a popular choice for artificial intelligence computations, because they offer parallel processing capabilities and can often execute vision-based inferencing faster than CPUs. To better support artificial intelligence and machine learning applications, Azure IoT Edge for Linux on Windows (EFLOW) can expose a GPU to the virtual machine's Linux module.
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GPUs are a popular choice for artificial intelligence computations because they offer parallel processing capabilities and often run vision-based inferencing faster than CPUs. To support artificial intelligence and machine learning applications, Azure IoT Edge for Linux on Windows (EFLOW) exposes a GPU to the virtual machine's Linux module.
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Azure IoT Edge for Linux on Windows supports several GPU passthrough technologies, including:
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@@ -25,15 +25,15 @@ Azure IoT Edge for Linux on Windows supports several GPU passthrough technologie
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You must select the appropriate passthrough method during deployment to match the supported capabilities of your device's GPU hardware.
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> [!IMPORTANT]
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> These features may include components developed and owned by NVIDIA Corporation or its licensors. The use of the components is governed by the NVIDIA End-User License Agreement located [on NVIDIA's website](https://www.nvidia.com/content/DriverDownload-March2009/licence.php?lang=us).
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> These features can include components developed and owned by NVIDIA Corporation or its licensors. The use of the components is governed by the NVIDIA End-User License Agreement located [on NVIDIA's website](https://www.nvidia.com/content/DriverDownload-March2009/licence.php?lang=us).
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>
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> By using GPU acceleration features, you are accepting and agreeing to the terms of the NVIDIA End-User License Agreement.
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> By using GPU acceleration features, you accept and agree to the terms of the NVIDIA End-User License Agreement.
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## Prerequisites
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The GPU acceleration features of Azure IoT Edge for Linux on Windows currently supports a select set of GPU hardware. Additionally, use of this feature may require specific versions of Windows.
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The GPU acceleration features of Azure IoT Edge for Linux on Windows currently support a select set of GPU hardware. Also, using this feature can require specific versions of Windows.
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The supported GPUs and required Windows versions are listed below:
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The supported GPUs and required Windows versions are:
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| Supported GPUs | GPU Passthrough Type | Supported Windows Versions |
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| --------- | --------------- | -------- |
@@ -42,42 +42,41 @@ The supported GPUs and required Windows versions are listed below:
>GPU-PV support may be limited to certain generations of processors or GPU architectures as determined by the GPU vendor. For more information, see [Intel's iGPU driver documentation](https://www.intel.com/content/www/us/en/download/19344/intel-graphics-windows-dch-drivers.html) or [NVIDIA's CUDA for WSL Documentation](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#wsl2-system-requirements).
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>GPU-PV support can be limited to certain generations of processors or GPU architectures, as determined by the GPU vendor. For more information, see [Intel's iGPU driver documentation](https://www.intel.com/content/www/us/en/download/19344/intel-graphics-windows-dch-drivers.html) or [NVIDIA's CUDA for WSL Documentation](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#wsl2-system-requirements).
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>
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>Windows Server 2019 users must use minimum build 17763 with all current cumulative updates installed.
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>
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>Windows 10 users must use the [November 2021 update](https://blogs.windows.com/windowsexperience/2021/11/16/how-to-get-the-windows-10-november-2021-update/) build 19044.1620 or higher. After installation, you can verify your build version by running `winver` at the command prompt.
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>Windows 10 users must use the [November 2021 update](https://blogs.windows.com/windowsexperience/2021/11/16/how-to-get-the-windows-10-november-2021-update/) build 19044.1620 or higher. After installation, check your build version by running `winver` at the command prompt.
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>
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> GPU passthrough is not supported with nested virtualization, such as running EFLOW in a Windows virtual machine.
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> GPU passthrough isn't supported with nested virtualization, like running EFLOW in a Windows virtual machine.
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## System setup and installation
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The following sections contain setup and installation information, according to your GPU.
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### NVIDIA T4/A2 GPUs
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For **T4/A2 GPUs**, Microsoft recommends installing a device mitigation driver from your GPU's vendor. While optional, installing a mitigation driver may improve the security of your deployment. For more information, see [Deploy graphics devices using direct device assignment](/windows-server/virtualization/hyper-v/deploy/deploying-graphics-devices-using-dda#optional---install-the-partitioning-driver).
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For **T4/A2 GPUs**, Microsoft recommends installing a device mitigation driver from your GPU's vendor. While optional, installing a mitigation driver can improve the security of your deployment. For more information, see [Deploy graphics devices using direct device assignment](/windows-server/virtualization/hyper-v/deploy/deploying-graphics-devices-using-dda#optional---install-the-partitioning-driver).
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> [!WARNING]
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> Enabling hardware device passthrough may increase security risks. Microsoft recommends a device mitigation driver from your GPU's vendor, when applicable. For more information, see [Deploy graphics devices using discrete device assignment](/windows-server/virtualization/hyper-v/deploy/deploying-graphics-devices-using-dda).
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> Enabling hardware device passthrough can increase security risks. Microsoft recommends a device mitigation driver from your GPU's vendor, when applicable. For more information, see [Deploy graphics devices using discrete device assignment](/windows-server/virtualization/hyper-v/deploy/deploying-graphics-devices-using-dda).
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### NVIDIA GeForce/Quadro/RTX GPUs
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For **NVIDIA GeForce/Quadro/RTX GPUs**, download and install the [NVIDIA CUDA-enabled driver for Windows Subsystem for Linux (WSL)](https://developer.nvidia.com/cuda/wsl) to use with your existing CUDA ML workflows. Originally developed for WSL, the CUDA for WSL drivers are also used for Azure IoT Edge for Linux on Windows.
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For **NVIDIA GeForce/Quadro/RTX GPUs**, download and install the [NVIDIA CUDA-enabled driver for Windows Subsystem for Linux (WSL)](https://developer.nvidia.com/cuda/wsl) to use with your existing CUDA ML workflows. Originally developed for WSL, the CUDA for WSL drivers are also used with Azure IoT Edge for Linux on Windows.
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Windows 10 users must also [install WSL](/windows/wsl/install) because some of the libraries are shared between WSL and Azure IoT Edge for Linux on Windows.
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Windows 10 users must also [install WSL](/windows/wsl/install) because some libraries are shared between WSL and Azure IoT Edge for Linux on Windows.
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### Intel iGPUs
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For **Intel iGPUs**, download and install the [Intel Graphics Driver with WSL GPU support](https://www.intel.com/content/www/us/en/download/19344/intel-graphics-windows-dch-drivers.html).
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Windows 10 users must also [install WSL](/windows/wsl/install) because some of the libraries are shared between WSL and Azure IoT Edge for Linux on Windows.
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Windows 10 users must also [install WSL](/windows/wsl/install) because some libraries are shared between WSL and Azure IoT Edge for Linux on Windows.
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## Enable GPU acceleration in your Azure IoT Edge Linux on Windows deployment
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Once system setup is complete, you are ready to [create your deployment of Azure IoT Edge for Linux on Windows](how-to-install-iot-edge-on-windows.md). During this process, you must [enable GPU](reference-iot-edge-for-linux-on-windows-functions.md#deploy-eflow) as part of EFLOW deployment.
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After you finish system setup, start to [create your deployment of Azure IoT Edge for Linux on Windows](how-to-install-iot-edge-on-windows.md). During this process, enable [GPU support](reference-iot-edge-for-linux-on-windows-functions.md#deploy-eflow) as part of EFLOW deployment.
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For example, the following commands create a GPU-enabled virtual machine with either an NVIDIA A2 GPU or Intel Iris Xe Graphics card.
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For example, these commands create a GPU-enabled virtual machine with an NVIDIA A2 GPU or an Intel Iris Xe Graphics card.
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```powershell
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#Deploys EFLOW with NVIDIA A2 assigned to the EFLOW VM
To find the name of your GPU, you can run the following command or look for Display adapters in Device Manager.
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To find your GPU name, run this command or look for Display adapters in Device Manager.
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```powershell
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(Get-WmiObject win32_VideoController).caption
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```
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Once installation is complete, you are ready to deploy and run GPU-accelerated Linux modules through Azure IoT Edge for Linux on Windows.
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After you finish installation, deploy and run GPU-accelerated Linux modules through Azure IoT Edge for Linux on Windows.
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## Configure GPU acceleration in an existing Azure IoT Edge Linux on Windows deployment
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Assigning the GPU at deployment time will result in the most straightforward experience. However, to enable or disable the GPU after deployment use the 'set-eflowvm' command. When using 'set-eflowvm' the default parameter will be used for any argument not specified. For example,
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Assign the GPU at deployment time for the most straightforward experience. To enable or disable the GPU after deployment, use the `set-eflowvm` command. When you use `set-eflowvm`, the default parameter is used for any argument you don't specify. For example,
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```powershell
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#Deploys EFLOW without a GPU assigned to the EFLOW VM
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#Deploys EFLOW without a GPU assigned to the EFLOW VM
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Deploy-Eflow -cpuCount 4 -memoryInMB 16384
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#Assigns NVIDIA A2 GPU to the existing deployment (cpu and memory must still be specified, otherwise they will be set to the default values)
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#Assigns NVIDIA A2 GPU to the existing deployment (cpu and memory must still be specified, or they're set to the default values)
#Removes NVIDIA A2 GPU from the existing deployment
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#Removes NVIDIA A2 GPU from the existing deployment
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Set-EflowVM -cpuCount 2 -memoryInMB 4096
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```
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## Next steps
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### Get Started with Samples
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Visit our [EFLOW Samples Page](https://github.com/Azure/iotedge-eflow/tree/main/samples) to discover several GPU samples which you can try and use. These samples illustrate common manufacturing and retail scenarios such as defect detection, worker safety, and inventory management. Thee open-source samples can serve as a solution template for building your own vision-based machine learning application.
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Go to our [EFLOW Samples Page](https://github.com/Azure/iotedge-eflow/tree/main/samples) to find several GPU samples you can try. These samples show common manufacturing and retail scenarios like defect detection, worker safety, and inventory management. These open-source samples can be a solution template for building your own vision-based machine learning application.
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### Learn More from our Partners
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Several GPU vendors have provided user guides on getting the most of their hardware and software with EFLOW.
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Several GPU vendors provide user guides on getting the most from their hardware and software with EFLOW.
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* Learn how to run Intel OpenVINO™ applications on EFLOW by following [Intel's guide on iGPU with Azure IoT Edge for Linux on Windows (EFLOW) & OpenVINO™ Toolkit](https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Witness-the-power-of-Intel-iGPU-with-Azure-IoT-Edge-for-Linux-on/post/1382405) and [reference implementations](https://www.intel.com/content/www/us/en/developer/articles/technical/deploy-reference-implementation-to-azure-iot-eflow.html).
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* Get started with deploying CUDA-accelerated applications on EFLOW by following [NVIDIA's EFLOW User Guide for GeForce/Quadro/RTX GPUs](https://docs.nvidia.com/cuda/eflow-users-guide/index.html).
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> [!NOTE]
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> This guide does not cover DDA-based GPUs such as NVIDIA T4 or A2.
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> This guide doesn't cover DDA-based GPUs like NVIDIA T4 or A2.
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### GPU passthrough technology
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### Dive into the Technology
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Learn more about GPU passthrough technologies by visiting the [DDA documentation](/windows-server/virtualization/hyper-v/plan/plan-for-gpu-acceleration-in-windows-server#discrete-device-assignment-dda) and [GPU-PV blog post](https://devblogs.microsoft.com/directx/directx-heart-linux/#gpu-virtualization).
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Learn more about GPU passthrough technologies in the [DDA documentation](/windows-server/virtualization/hyper-v/plan/plan-for-gpu-acceleration-in-windows-server#discrete-device-assignment-dda) and [GPU-PV blog post](https://devblogs.microsoft.com/directx/directx-heart-linux/#gpu-virtualization).
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