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Prepare GPUs for Azure Local (preview)

  • Artikkeli

Applies to: Azure Local, version 23H2

This article describes how to prepare graphical processing units (GPUs) on your Azure Local instance for computation-intensive workloads running on Arc virtual machines (VMs) and AKS enabled by Azure Arc. GPUs are used for computation-intensive workloads such as machine learning and deep learning.

Important

This feature is currently in PREVIEW. See the Supplemental Terms of Use for Microsoft Azure Previews for legal terms that apply to Azure features that are in beta, preview, or otherwise not yet released into general availability.

Attaching GPUs on Azure Local

You can attach your GPUs in one of two ways for Azure Local:

  • Discrete Device Assignment (DDA) - allows you to dedicate a physical GPU to your workload. In a DDA deployment, virtualized workloads run on the native driver and typically have full access to the GPU's functionality. DDA offers the highest level of app compatibility and potential performance.

  • GPU Partitioning (GPU-P) - allows you to share a GPU with multiple workloads by splitting the GPU into dedicated fractional partitions.

Consider the following functionality and support differences between the two options of using your GPUs:

Description Discrete Device Assignment GPU Partitioning
GPU resource model Entire device Equally partitioned device
VM density Low (one GPU to one VM) High (one GPU to many VMs)
App compatibility All GPU capabilities provided by vendor (DX 12, OpenGL, CUDA) All GPU capabilities provided by vendor (DX 12, OpenGL, CUDA)
GPU VRAM Up to VRAM supported by the GPU Up to VRAM supported by the GPU per partition
GPU driver in guest GPU vendor driver (NVIDIA) GPU vendor driver (NVIDIA)

Supported GPU models

To see the full list of supported solutions and GPUs available, see Azure Local Solutions and select GPU support in the left menu for options.

NVIDIA supports their workloads separately with their virtual GPU software. For more information, see Microsoft Azure Local - Supported NVIDIA GPUs and Validated Server Platforms.

For AKS workloads, see GPUs for AKS for Arc.

The following GPU models are supported using both DDA and GPU-P for Arc VM workloads:

  • NVIDIA A2
  • NVIDIA A16

These additional GPU models are supported using GPU-P (only) for Arc VM workloads:

  • NVIDIA A10
  • NVIDIA A40
  • NVIDIA L4
  • NVIDIA L40
  • NVIDIA L40S

Host requirements

Your Azure Local host must meet the following requirements:

  • Your system must support an Azure Local solution with GPU support. To browse your options, see the Azure Local Catalog.

  • You've access to Azure Local, version 23H2.

  • You must create a homogeneous configuration for GPUs across all the machines in your system. A homogeneous configuration consists of installing the same make and model of GPU.

  • For GPU-P, ensure that the virtualization support and SR-IOV are enabled in the BIOS of each machine in the system. Contact your hardware vendor if you're unable to identify the correct setting in your BIOS.

Prepare GPU drivers on each host

The process for preparing and installing GPU drivers for each machine differs somewhat between DDA and GPU-P. Follow the applicable process for your situation.

Find GPUs on each host

First ensure there is no driver installed for each machine. If there is a host driver installed, uninstall the host driver and restart the machine.

After you uninstalled the host driver or if you did not have any driver installed, run PowerShell as administrator with the following command:

Get-PnpDevice -Status Error | fl FriendlyName, ClusterId

You should see the GPU devices appear in an error state as 3D Video Controller as shown in the example output that lists the friendly name and instance ID of the GPU:

[ASRR1N26R02U46A]: PS C:\Users\HCIDeploymentUser\Documents> Get-PnpDevice - Status Error

Status		Class			FriendlyName
------		-----			------------
Error					SD Host Controller
Error					3D Video Controller
Error					3D Video Controller
Error		USB			Unknown USB Device (Device Descriptor Request Failed)

[ASRR1N26R02U46A]: PS C:\Users\HCIDeploymentUser\Documents> Get-PnpDevice - Status Error | f1 InstanceId

InstanceId : PCI\VEN_8086&DEV_18DB&SUBSYS_7208086REV_11\3&11583659&0&E0

InstanceId : PCI\VEN_10DE&DEV_25B6&SUBSYS_157E10DE&REV_A1\4&23AD3A43&0&0010

InstanceId : PCI\VEN_10DE&DEV_25B6&SUBSYS_157E10DE&REV_A1\4&17F8422A&0&0010

InstanceId : USB\VID_0000&PID_0002\S&E492A46&0&2

Using DDA

Follow this process if using DDA:

1. Disable and dismount GPUs from the host

For DDA, when you uninstall the host driver or have a new Azure Local setup, the physical GPU goes into an error state. You must dismount all the GPU devices to continue. You can use Device Manager or PowerShell to disable and dismount the GPU using the ClusterID obtained in the prior step.

$id1 = "GPU_instance_ID"
Disable-PnpDevice -ClusterId $id1 -Confirm:$false
Dismount-VMHostAssignableDevice -ClusterPath $id1 -Force

Confirm the GPUs were correctly dismounted from the host machine. The GPUs will now be in an Unknown state:

Get-PnpDevice -Status Unknown | fl FriendlyName, ClusterId

Repeat this process for each machine in your system to prepare the GPUs.

2. Download and install the mitigation driver

The software might include components developed and owned by NVIDIA Corporation or its licensors. The use of these components is governed by the NVIDIA end user license agreement.

See the NVIDIA documentation to download the applicable NVIDIA mitigation driver. After downloading the driver, expand the archive and install the mitigation driver on each host machine. Use the following PowerShell script to download the mitigation driver and extract it:

Invoke-WebRequest -Uri "https://docs.nvidia.com/datacenter/tesla/gpu-passthrough/nvidia_azure_stack_inf_v2022.10.13_public.zip" -OutFile "nvidia_azure_stack_inf_v2022.10.13_public.zip"
mkdir nvidia-mitigation-driver
Expand-Archive .\nvidia_azure_stack_inf_v2022.10.13_public.zip .\nvidia-mitigation-driver

Once the mitigation driver files are extracted, find the version for the correct model of your GPU and install it. For example, if you were installing an NVIDIA A2 mitigation driver, run the following:

pnputil /add-driver nvidia_azure_stack_A2_base.inf /install /force

To confirm the installation of these drivers, run:

pnputil /enum-devices OR pnputil /scan-devices

You should be able to see the correctly identified GPUs in Get-PnpDevice:

Get-PnpDevice -Class Display | fl FriendlyName, ClusterId

Repeat the above steps for each host in your Azure Local.

Using GPU-P

Follow this process if using GPU-P:

Download and install the host driver

GPU-P requires drivers on the host level that differ from DDA. For NVIDIA GPUs, you will need an NVIDIA vGPU software graphics driver on each host and on each VM that will use GPU-P. For more information, see the latest version of NVIDIA vGPU Documentation and details on licensing at Client Licensing User Guide.

After identifying the GPUs as 3D Video Controller on your host machine, download the host vGPU driver. Through your NVIDIA GRID license, you should be able to obtain the proper host driver .zip file.

You will need to obtain and move the following folder to your host machine: \vGPU_<Your_vGPU_version>_GA_Azure_Stack_HCI_Host_Drivers

Navigate to \vGPU_<Your_vGPU_version>_GA_Azure_Stack_HCI_Host_Drivers\Display.Driver and install the driver.

pnputil /add-driver .\nvgridswhci.inf /install /force

To confirm the installation of these drivers, run:

pnputil /enum-devices

You should be able to see the correctly identified GPUs in Get-PnpDevice:

Get-PnpDevice -Class Display | fl FriendlyName, ClusterId

You can also run the NVIDIA System Management Interface nvidia-smi to list the GPUs on the host machine as follows:

nvidia-smi

If the driver is correctly installed, you will see an output similar to the following sample:

Wed Nov 30 15:22:36 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 527.27       Driver Version: 527.27       CUDA Version: N/A      |
|-------------------------------+----------------------+----------------------+
| GPU  Name            TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA A2          WDDM  | 00000000:65:00.0 Off |                    0 |
|  0%   24C    P8     5W /  60W |  15192MiB / 15356MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   1  NVIDIA A2          WDDM  | 00000000:66:00.0 Off |                    0 |
|  0%   24C    P8     5W /  60W |  15192MiB / 15356MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
 
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

Configure GPU partition count

Follow these steps to configure the GPU partition count in PowerShell:

Note

When using PowerShell, you must manually ensure the GPU configuration is homogenous across all machines in your Azure Local.

  1. Connect to the machine whose GPU partition count you want to configure.

  2. Run the Get-VMHostPartitionableGpu command and refer to the Name and ValidPartitionCounts values.

  3. Run the following command to configure the partition count. Replace GPU-name with the Name value and partition-count with one of the supported counts from the ValidPartitionCounts value:

    Set-VMHostPartitionableGpu -Name "<GPU-name>" -PartitionCount <partition-count>

    For example, the following command configures the partition count to 4:

    PS C:\Users> Set-VMHostPartitionableGpu -Name "\\?\PCI#VEN_10DE&DEV_25B6&SUBSYS_157E10DE&REV_A1#4&18416dc3&0&0000#{064092b3-625e-43bf-9eb5-dc845897dd59}" -PartitionCount 4

    You can run the command Get-VMHostPartitionableGpu | FL Name,ValidPartitionCounts,PartitionCount again to verify that the partition count is set to 4.

    Here's a sample output:

    PS C:\Users> Get-VMHostPartitionableGpu | FL Name,ValidPartitionCounts,PartitionCount

Name                    : \\?\PCI#VEN_10DE&DEV_25B6&SUBSYS_157E10DE&REV_A1#4&18416dc3&0&0000#{064092b3-625e-43bf-9eb5-dc845897dd59}
ValidPartitionCounts    : {16, 8, 4, 2...}
PartitionCount          : 4

Name                    : \\?\PCI#VEN_10DE&DEV_25B6&SUBSYS_157E10DE&REV_A1#4&5906f5e&0&0010#{064092b3-625e-43bf-9eb5-dc845897dd59}
ValidPartitionCounts    : {16, 8, 4, 2...}
PartitionCount          : 4

  4. To keep the configuration homogeneous, repeat the partition count configuration steps on each machine in your system.

Guest requirements

GPU management is supported for the following Arc VM workloads:

Next steps