Document Details
Gpu Acceleration
❌
This page contains Windows bias
About This Page
This page is part of the Azure documentation. It contains code examples and configuration instructions for working with Azure services.
Bias Analysis
Bias Types:
⚠️
windows_first
⚠️
powershell_heavy
⚠️
windows_tools
⚠️
missing_linux_example
Summary:
The documentation is heavily oriented toward Windows environments, with all setup, configuration, and deployment instructions using Windows tools (PowerShell, Device Manager, winver). There are no Linux-native examples or instructions, and the only Linux context is within a Windows-hosted VM (EFLOW). The documentation assumes Windows as the host and does not provide parity for users who might want to run similar workloads on native Linux systems or need Linux-based setup/configuration steps.
Recommendations:
- Add equivalent Linux-native instructions or clarify that EFLOW is only supported on Windows hosts, and provide references for native Linux GPU acceleration with Azure IoT Edge.
- Include Linux shell (bash) command examples for tasks such as checking GPU availability, driver installation, and deployment, even if only for the guest VM.
- Mention Linux tools (e.g., lspci, nvidia-smi, glxinfo) alongside Windows tools like Device Manager and winver.
- If EFLOW is strictly a Windows-hosted solution, explicitly state this at the beginning and provide links to Linux-native Azure IoT Edge GPU acceleration documentation for users on Linux.
- Ensure that any references to driver installation or system requirements include both Windows and Linux perspectives where applicable.
Create pull request
Scan History
| Date | Scan ID | Status | Bias Status |
|---|---|---|---|
| 2025-08-22 00:01 | #88 | completed | ✅ Clean |
| 2025-08-17 00:01 | #83 | in_progress | ✅ Clean |
| 2025-07-13 21:37 | #48 | completed | ✅ Clean |
| 2025-07-12 23:44 | #41 | in_progress | ❌ Biased |
| 2025-07-09 13:09 | #3 | cancelled | ✅ Clean |
| 2025-07-08 04:23 | #2 | cancelled | ❌ Biased |
Flagged Code Snippets
#Deploys EFLOW with NVIDIA A2 assigned to the EFLOW VM
Deploy-Eflow -gpuPassthroughType DirectDeviceAssignment -gpuCount 1 -gpuName "NVIDIA A2"
#Deploys EFLOW with Intel(R) Iris(R) Xe Graphics assigned to the EFLOW VM
Deploy-Eflow -gpuPassthroughType ParaVirtualization -gpuCount 1 -gpuName "Intel(R) Iris(R) Xe Graphics"
(Get-WmiObject win32_VideoController).caption
# Deploys EFLOW without a GPU assigned to the EFLOW VM
Deploy-Eflow -cpuCount 4 -memoryInMB 16384
# Assigns NVIDIA A2 GPU to the existing deployment (cpu and memory must still be specified, or they're set to the default values)
Set-EflowVM -cpuCount 4 -memoryInMB 16384 -gpuName "NVIDIA A2" -gpuPassthroughType DirectDeviceAssignment -gpuCount 1
# Reduces the cpuCount and memory (GPU must still be specified, or the GPU is removed)
Set-EflowVM -cpuCount 2 -memoryInMB 4096 -gpuName "NVIDIA A2" -gpuPassthroughType DirectDeviceAssignment -gpuCount 1
# Removes NVIDIA A2 GPU from the existing deployment
Set-EflowVM -cpuCount 2 -memoryInMB 4096