Document Details
Azure Stack Edge Gpu Deploy Kubernetes Gpu Sharing
❌
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:
⚠️
powershell_heavy
⚠️
windows_first
⚠️
missing_linux_example
⚠️
windows_tools
Summary:
The documentation demonstrates a strong Windows bias. All command-line examples are shown in PowerShell, with explicit references to Windows paths (e.g., C:\gpu-sharing\...), and all screenshots and outputs are from PowerShell sessions. There are no Linux shell (bash) equivalents, nor are there any references to Linux tools or terminal environments. The documentation assumes the use of Windows clients and PowerShell for all management tasks, and device management commands (e.g., Get-HcsGpuNvidiaSmi, Start-HcsGpuMPS) are only shown in PowerShell, with no mention of Linux alternatives or how to perform these tasks from a Linux client.
Recommendations:
- Provide equivalent Linux shell (bash) command examples alongside PowerShell commands for all kubectl and file operations.
- Include Linux path examples (e.g., /home/user/gpu-sharing/k8-gpusharing.yaml) in addition to Windows paths.
- Clarify whether device management commands (e.g., Get-HcsGpuNvidiaSmi, Start-HcsGpuMPS) are available from Linux clients, and if so, provide Linux usage instructions.
- If device management is only possible from Windows, explicitly state this limitation and suggest workarounds for Linux users (such as using a Windows VM or container).
- Add screenshots or terminal outputs from Linux environments to balance the visual representation.
- Update the prerequisites to explicitly mention supported Linux shells and tools, and provide guidance for Linux users.
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Scan History
| Date | Scan ID | Status | Bias Status |
|---|---|---|---|
| 2025-08-17 00:01 | #83 | in_progress | ✅ Clean |
| 2025-07-13 21:37 | #48 | completed | ✅ Clean |
| 2025-07-09 13:09 | #3 | cancelled | ✅ Clean |
| 2025-07-08 04:23 | #2 | cancelled | ❌ Biased |
Flagged Code Snippets
Get-HcsGpuNvidiaSmi
PS C:\WINDOWS\system32> kubectl get pods -n mynamesp1
No resources found.
PS C:\WINDOWS\system32> kubectl get pods -n mynamesp1
NAME READY STATUS RESTARTS AGE
cuda-sample1-27srm 1/1 Running 0 70s
cuda-sample2-db9vx 1/1 Running 0 69s
PS C:\WINDOWS\system32>
PS C:\WINDOWS\system32> kubectl get pods -n mynamesp1
NAME READY STATUS RESTARTS AGE
cuda-sample1-27srm 0/1 Completed 0 2m54s
cuda-sample2-db9vx 0/1 Completed 0 2m53s
PS C:\WINDOWS\system32>
PS C:\WINDOWS\system32> kubectl get pods -n mynamesp1
NAME READY STATUS RESTARTS AGE
cuda-sample1-27srm 1/1 Running 0 28s
cuda-sample2-db9vx 1/1 Running 0 27s
PS C:\WINDOWS\system32>
[10.100.10.10]: PS>Start-HcsGpuMPS
K8S-1HXQG13CL-1HXQG13:
Set compute mode to EXCLUSIVE_PROCESS for GPU 00002C74:00:00.0.
All done.
Created nvidia-mps.service
[10.100.10.10]: PS>
PS>Get-HcsGpuNvidiaSmi
K8S-1HXQG13CL-1HXQG13:
Mon Mar 3 21:54:50 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 | 0000E00B:00:00.0 Off | 0 |
| N/A 45C P0 68W / 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 144377 M+C /tmp/nbody 107MiB |
| 0 N/A N/A 144379 M+C /tmp/nbody 107MiB |
| 0 N/A N/A 144443 C nvidia-cuda-mps-server 25MiB |
+-----------------------------------------------------------------------------+
PS C:\WINDOWS\system32> kubectl get pods -n mynamesp1
NAME READY STATUS RESTARTS AGE
cuda-sample1-vcznt 0/1 Completed 0 5m44s
cuda-sample2-zkx4w 0/1 Completed 0 5m44s
PS C:\WINDOWS\system32> kubectl logs -n mynamesp1 cuda-sample1-vcznt
Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
===========// CUT //===================// CUT //=====================
> 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: 154979.453 ms
= 108.254 billion interactions per second
= 2165.089 single-precision GFLOP/s at 20 flops per interaction
PS C:\WINDOWS\system32> kubectl logs -n mynamesp1 cuda-sample2-zkx4w
Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
===========// CUT //===================// CUT //=====================
> 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: 154986.734 ms
= 108.249 billion interactions per second
= 2164.987 single-precision GFLOP/s at 20 flops per interaction
PS C:\WINDOWS\system32>
[10.100.10.10]: PS>Get-HcsGpuNvidiaSmi
K8S-1HXQG13CL-1HXQG13:
Wed Mar 3 12:24:27 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 34C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
[10.100.10.10]: PS>
PS C:\WINDOWS\system32> kubectl apply -f -n mynamesp1 C:\gpu-sharing\k8-gpusharing.yaml
job.batch/cuda-sample1 created
job.batch/cuda-sample2 created
PS C:\WINDOWS\system32>
PS C:\WINDOWS\system32> kubectl delete -f 'C:\gpu-sharing\k8-gpusharing.yaml' -n mynamesp1
deployment.apps "cuda-sample1" deleted
deployment.apps "cuda-sample2" deleted
PS C:\WINDOWS\system32>
PS C:\WINDOWS\system32> kubectl logs -n mynamesp1 cuda-sample2-db9vx
Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
===========// CUT //===================// CUT //=====================
> 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: 170368.859 ms
= 98.476 billion interactions per second
= 1969.517 single-precision GFLOP/s at 20 flops per interaction
PS C:\WINDOWS\system32>
PS C:\WINDOWS\system32> kubectl -n mynamesp1 describe job.batch/cuda-sample1; kubectl -n mynamesp1 describe job.batch/cuda-sample2
Name: cuda-sample1
Namespace: mynamesp1
Selector: controller-uid=22783f76-6af1-490d-b6eb-67dd4cda0e1f
Labels: controller-uid=22783f76-6af1-490d-b6eb-67dd4cda0e1f
job-name=cuda-sample1
Annotations: kubectl.kubernetes.io/last-applied-configuration:
{"apiVersion":"batch/v1","kind":"Job","metadata":{"annotations":{},"name":"cuda-sample1","namespace":"mynamesp1"},"spec":{"backoffLimit":1...
Parallelism: 1
Completions: 1
Start Time: Wed, 03 Mar 2021 12:25:34 -0800
Pods Statuses: 1 Running / 0 Succeeded / 0 Failed
Pod Template:
Labels: controller-uid=22783f76-6af1-490d-b6eb-67dd4cda0e1f
job-name=cuda-sample1
Containers:
cuda-sample-container1:
Image: nvidia/samples:nbody
Port: <none>
Host Port: <none>
Command:
/tmp/nbody
Args:
-benchmark
-i=10000
Environment:
NVIDIA_VISIBLE_DEVICES: 0
Mounts: <none>
Volumes: <none>
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SuccessfulCreate 60s job-controller Created pod: cuda-sample1-27srm
Name: cuda-sample2
Namespace: mynamesp1
Selector: controller-uid=e68c8d5a-718e-4880-b53f-26458dc24381
Labels: controller-uid=e68c8d5a-718e-4880-b53f-26458dc24381
job-name=cuda-sample2
Annotations: kubectl.kubernetes.io/last-applied-configuration:
{"apiVersion":"batch/v1","kind":"Job","metadata":{"annotations":{},"name":"cuda-sample2","namespace":"mynamesp1"},"spec":{"backoffLimit":1...
Parallelism: 1
Completions: 1
Start Time: Wed, 03 Mar 2021 12:25:35 -0800
Pods Statuses: 1 Running / 0 Succeeded / 0 Failed
Pod Template:
Labels: controller-uid=e68c8d5a-718e-4880-b53f-26458dc24381
job-name=cuda-sample2
Containers:
cuda-sample-container2:
Image: nvidia/samples:nbody
Port: <none>
Host Port: <none>
Command:
/tmp/nbody
Args:
-benchmark
-i=10000
Environment:
NVIDIA_VISIBLE_DEVICES: 0
Mounts: <none>
Volumes: <none>
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SuccessfulCreate 60s job-controller Created pod: cuda-sample2-db9vx
PS C:\WINDOWS\system32>
[10.100.10.10]: PS>Get-HcsGpuNvidiaSmi
K8S-1HXQG13CL-1HXQG13:
Wed Mar 3 12:26:41 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 64C P0 69W / 70W | 221MiB / 15109MiB | 100% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 197976 C /tmp/nbody 109MiB |
| 0 N/A N/A 198051 C /tmp/nbody 109MiB |
+-----------------------------------------------------------------------------+
[10.100.10.10]: PS>
PS C:\WINDOWS\system32> kubectl logs -n mynamesp1 cuda-sample1-27srm
Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
===========// CUT //===================// CUT //=====================
> 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: 170398.766 ms
= 98.459 billion interactions per second
= 1969.171 single-precision GFLOP/s at 20 flops per interaction
PS C:\WINDOWS\system32>
[10.100.10.10]: PS>Get-HcsGpuNvidiaSmi
K8S-1HXQG13CL-1HXQG13:
Wed Mar 3 12:32:52 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 38C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
[10.100.10.10]: PS>
PS C:\WINDOWS\system32> kubectl -n mynamesp1 delete -f C:\gpu-sharing\k8-gpusharing.yaml
job.batch "cuda-sample1" deleted
job.batch "cuda-sample2" deleted
PS C:\WINDOWS\system32> kubectl get pods -n mynamesp1
No resources found.
PS C:\WINDOWS\system32> kubectl -n mynamesp1 apply -f C:\gpu-sharing\k8-gpusharing.yaml
job.batch/cuda-sample1 created
job.batch/cuda-sample2 created
PS C:\WINDOWS\system32> kubectl get pods -n mynamesp1
NAME READY STATUS RESTARTS AGE
cuda-sample1-vcznt 1/1 Running 0 21s
cuda-sample2-zkx4w 1/1 Running 0 21s
PS C:\WINDOWS\system32> kubectl -n mynamesp1 describe job.batch/cuda-sample1; kubectl -n mynamesp1 describe job.batch/cuda-sample2
Name: cuda-sample1
Namespace: mynamesp1
Selector: controller-uid=ed06bdf0-a282-4b35-a2a0-c0d36303a35e
Labels: controller-uid=ed06bdf0-a282-4b35-a2a0-c0d36303a35e
job-name=cuda-sample1
Annotations: kubectl.kubernetes.io/last-applied-configuration:
{"apiVersion":"batch/v1","kind":"Job","metadata":{"annotations":{},"name":"cuda-sample1","namespace":"mynamesp1"},"spec":{"backoffLimit":1...
Parallelism: 1
Completions: 1
Start Time: Wed, 03 Mar 2021 21:51:51 -0800
Pods Statuses: 1 Running / 0 Succeeded / 0 Failed
Pod Template:
Labels: controller-uid=ed06bdf0-a282-4b35-a2a0-c0d36303a35e
job-name=cuda-sample1
Containers:
cuda-sample-container1:
Image: nvidia/samples:nbody
Port: <none>
Host Port: <none>
Command:
/tmp/nbody
Args:
-benchmark
-i=10000
Environment:
NVIDIA_VISIBLE_DEVICES: 0
Mounts: <none>
Volumes: <none>
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SuccessfulCreate 46s job-controller Created pod: cuda-sample1-vcznt
Name: cuda-sample2
Namespace: mynamesp1
Selector: controller-uid=6282b8fa-e76d-4f45-aa85-653ee0212b29
Labels: controller-uid=6282b8fa-e76d-4f45-aa85-653ee0212b29
job-name=cuda-sample2
Annotations: kubectl.kubernetes.io/last-applied-configuration:
{"apiVersion":"batch/v1","kind":"Job","metadata":{"annotations":{},"name":"cuda-sample2","namespace":"mynamesp1"},"spec":{"backoffLimit":1...
Parallelism: 1
Completions: 1
Start Time: Wed, 03 Mar 2021 21:51:51 -0800
Pods Statuses: 1 Running / 0 Succeeded / 0 Failed
Pod Template:
Labels: controller-uid=6282b8fa-e76d-4f45-aa85-653ee0212b29
job-name=cuda-sample2
Containers:
cuda-sample-container2:
Image: nvidia/samples:nbody
Port: <none>
Host Port: <none>
Command:
/tmp/nbody
Args:
-benchmark
-i=10000
Environment:
NVIDIA_VISIBLE_DEVICES: 0
Mounts: <none>
Volumes: <none>
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SuccessfulCreate 47s job-controller Created pod: cuda-sample2-zkx4w
PS C:\WINDOWS\system32>
PS>Get-HcsGpuNvidiaSmi
K8S-1HXQG13CL-1HXQG13:
Mon Mar 3 21:59:55 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 | 0000E00B:00:00.0 Off | 0 |
| N/A 37C P8 9W / 70W | 28MiB / 15109MiB | 0% E. Process |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 144443 C nvidia-cuda-mps-server 25MiB |
+-----------------------------------------------------------------------------+