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Azure Stack Edge Gpu Deploy Iot Edge Gpu Sharing
Home / Scan #48 / Azure Stack Edge Gpu Deploy Iot Edge Gpu Sharing
Sad Tux - Windows bias detected
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.
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Bias Analysis

Detected Bias Types
powershell_heavy
windows_first
missing_linux_example
windows_tools
Summary
The documentation page demonstrates a strong Windows bias. All command-line examples and device interactions are shown using PowerShell, with explicit references to Windows paths (e.g., C:\Users\<username>\.kube), and no Linux or cross-platform equivalents are provided. The instructions for connecting to the device, managing Kubernetes config files, and running kubectl commands are all shown from a Windows environment. There is no mention of how to perform these steps from a Linux or macOS client, nor are there any bash or shell examples. Windows-specific tools and patterns (such as PowerShell cmdlets and Windows directory structures) are used exclusively throughout the guide.
Recommendations
  • Provide equivalent Linux/macOS instructions and examples for all steps, including connecting to the device, managing Kubernetes config files, and running kubectl commands.
  • Include bash or shell command examples alongside PowerShell examples, especially for kubectl and file operations.
  • Mention Linux and macOS as supported client platforms where applicable, and link to any prerequisites or setup guides for those systems.
  • Show Linux file paths (e.g., ~/.kube/config) in addition to Windows paths when instructing users where to save configuration files.
  • If device management cmdlets are only available on Windows, explicitly state this limitation and, if possible, suggest alternatives for Linux/macOS users.
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Scan History

Date Scan Status Result
2026-01-14 00:00 #250 in_progress Biased Biased
2026-01-13 00:00 #246 completed Biased Biased
2026-01-11 00:00 #240 completed Biased Biased
2026-01-10 00:00 #237 completed Biased Biased
2026-01-09 00:34 #234 completed Biased Biased
2026-01-08 00:53 #231 completed Biased Biased
2026-01-06 18:15 #225 cancelled Clean Clean
2025-08-17 00:01 #83 cancelled Clean Clean
2025-07-13 21:37 #48 completed Biased Biased
2025-07-09 13:09 #3 cancelled Clean Clean
2025-07-08 04:23 #2 cancelled Biased Biased

Flagged Code Snippets

    PS C:\WINDOWS\system32> kubectl -n iotedge  --kubeconfig C:\GPU-sharing\kubeconfigs\configiotuser1 logs cuda-sample1-869989578c-ssng8 cuda-sample1
    Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
    ==============// snipped //===================//  snipped  //=============
    > Windowed mode
    > Simulation data stored in video memory
    > Single precision floating point simulation
    > 1 Devices used for simulation
    GPU Device 0: "Turing" with compute capability 7.5
    
    > Compute 7.5 CUDA device: [Tesla T4]
    40960 bodies, total time for 10000 iterations: 170171.531 ms
    = 98.590 billion interactions per second
    = 1971.801 single-precision GFLOP/s at 20 flops per interaction
    no-op
    PS C:\WINDOWS\system32>
    
    [10.100.10.10]: PS>Start-HcsGpuMPS
    K8S-1HXQG13CL-1HXQG13:
    Set compute mode to EXCLUSIVE_PROCESS for GPU 0000191E:00:00.0.
    All done.
    Created nvidia-mps.service
    [10.100.10.10]: PS>    
    
    [10.100.10.10]: PS>Grant-HcsKubernetesNamespaceAccess -Namespace iotedge -UserName iotedgeuser
    [10.100.10.10]: PS>    
    
    PS C:\WINDOWS\system32> kubectl get pods -n iotedge --kubeconfig C:\GPU-sharing\kubeconfigs\configiotuser1
    NAME                            READY   STATUS    RESTARTS   AGE
    cuda-sample1-869989578c-ssng8   2/2     Running   0          5s
    cuda-sample2-6db6d98689-d74kb   2/2     Running   0          4s
    edgeagent-79f988968b-7p2tv      2/2     Running   0          6d21h
    edgehub-d6c764847-l8v4m         2/2     Running   0          24h
    iotedged-55fdb7b5c6-l9zn8       1/1     Running   1          6d21h
    PS C:\WINDOWS\system32>   
    
    [10.100.10.10]: PS>Get-HcsGpuNvidiaSmi
    K8S-1HXQG13CL-1HXQG13:
    
    Thu Mar  4 12:59:44 2021
    +-----------------------------------------------------------------------------+
    | NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |
    |-------------------------------+----------------------+----------------------+
    | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
    | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
    |                               |                      |               MIG M. |
    |===============================+======================+======================|
    |   0  Tesla T4            On   | 00002C74:00:00.0 Off |                    0 |
    | N/A   54C    P0    69W /  70W |    242MiB / 15109MiB |    100%   E. Process |
    |                               |                      |                  N/A |
    +-------------------------------+----------------------+----------------------+
    
    +-----------------------------------------------------------------------------+
    | Processes:                                                                  |
    |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
    |        ID   ID                                                   Usage      |
    |=============================================================================|
    |    0   N/A  N/A     56832    M+C   /tmp/nbody                        107MiB |
    |    0   N/A  N/A     56900    M+C   /tmp/nbody                        107MiB |
    |    0   N/A  N/A    122792      C   nvidia-cuda-mps-server             25MiB |
    +-----------------------------------------------------------------------------+
    [10.100.10.10]: PS>Get-HcsGpuNvidiaSmi