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Deploy a Kubernetes workload using GPU sharing on your Azure Stack Edge Pro

This article describes how containerized workloads can share the GPUs on your Azure Stack Edge Pro GPU device. In this article, you will run two jobs, one without the GPU context-sharing and one with the context-sharing enabled via the Multi-Process Service (MPS) on the device. For more information, see the Multi-Process Service.

Prerequisites

Before you begin, make sure that:

  1. You've access to an Azure Stack Edge Pro GPU device that is activated and has compute configured . You have the Kubernetes API endpoint and you have added this endpoint to the hosts file on your client that will be accessing the device.

  2. You've access to a client system with a Supported operating system. If using a Windows client, the system should run PowerShell 5.0 or later to access the device.

  3. You have created a namespace and a user. You have also granted user the access to this namespace. You have the kubeconfig file of this namespace installed on the client system that you'll use to access your device. For detailed instructions, see Connect to and manage a Kubernetes cluster via kubectl on your Azure Stack Edge Pro GPU device.

  4. Save the following deployment yaml on your local system. You'll use this file to run Kubernetes deployment. This deployment is based on Simple CUDA containers that are publicly available from Nvidia.

    apiVersion: batch/v1
    kind: Job
    metadata:
      name: cuda-sample1
    spec:
      template:
        spec:
          hostPID: true
          hostIPC: true
          containers:
            - name: cuda-sample-container1
              image: nvidia/samples:nbody
              command: ["/tmp/nbody"]
              args: ["-benchmark", "-i=1000"]
              env:
              - name: NVIDIA_VISIBLE_DEVICES
                value: "0"
          restartPolicy: "Never"
      backoffLimit: 1
    ---
    
    apiVersion: batch/v1
    kind: Job
    metadata:
      name: cuda-sample2
    spec:
      template:
        metadata:
        spec:
          hostPID: true
          hostIPC: true
          containers:
            - name: cuda-sample-container2
              image: nvidia/samples:nbody
              command: ["/tmp/nbody"]
              args: ["-benchmark", "-i=1000"]
              env:
              - name: NVIDIA_VISIBLE_DEVICES
                value: "0"
          restartPolicy: "Never"
      backoffLimit: 1
    

Verify GPU driver, CUDA version

The first step is to verify that your device is running required GPU driver and CUDA versions.

  1. Connect to the PowerShell interface of your device.

  2. Run the following command:

    Get-HcsGpuNvidiaSmi
    
  3. In the Nvidia smi output, make a note of the GPU version and the CUDA version on your device. If you are running Azure Stack Edge 2102 software, this version would correspond to the following driver versions:

    • GPU driver version: 460.32.03
    • CUDA version: 11.2

    Here is an example output:

    [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> 
    
  4. Keep this session open as you will use it to view the Nvidia smi output throughout the article.

Job without context-sharing

You'll run the first job to deploy an application on your device in the namespace mynamesp1. This application deployment will also show that the GPU context-sharing is not enabled by default.

  1. List all the pods running in the namespace. Run the following command:

    kubectl get pods -n <Name of the namespace>
    

    Here is an example output:

    PS C:\WINDOWS\system32> kubectl get pods -n mynamesp1
    No resources found.
    
  2. Start a deployment job on your device using the deployment.yaml provided earlier. Run the following command:

    kubectl apply -f <Path to the deployment .yaml> -n <Name of the namespace> 
    

    This job creates two containers and runs an n-body simulation on both the containers. The number of simulation iterations are specified in the .yaml.

    Here is an example output:

    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>
    
  3. To list the pods started in the deployment, run the following command:

    kubectl get pods -n <Name of the namespace>
    

    Here is an example output:

    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>
    

    There are two pods, cuda-sample1-cf979886d-xcwsq and cuda-sample2-68b4899948-vcv68 running on your device.

  4. Fetch the details of the pods. Run the following command:

    kubectl -n <Name of the namespace> describe <Name of the job> 
    

    Here is an example output:

    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>
    

    The output indicates that both the pods were successfully created by the job.

  5. While both the containers are running the n-body simulation, view the GPU utilization from the Nvidia smi output. Go to the PowerShell interface of the device and run Get-HcsGpuNvidiaSmi.

    Here is an example output when both the containers are running the n-body simulation:

    [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>    
    

    As you can see, there are two containers (Type = C) running with n-body simulation on GPU 0.

  6. Monitor the n-body simulation. Run the get pod commands. Here is an example output when the simulation is running.

    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>
    

    When the simulation is complete, the output will indicate that. Here is an example output:

    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>
    
  7. After the simulation is complete, you can view the logs and the total time for the completion of the simulation. Run the following command:

    kubectl logs -n <Name of the namespace> <pod name>
    

    Here is an example output:

    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>
    
    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>    
    
  8. There should be no processes running on the GPU at this time. You can verify this by viewing the GPU utilization using the Nvidia smi output.

    [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>
    

Job with context-sharing

You'll run the second job to deploy the n-body simulation on two CUDA containers when GPU context-sharing is enabled though the MPS. First, you'll enable MPS on the device.

  1. Connect to the PowerShell interface of your device.

  2. To enable MPS on your device, run the Start-HcsGpuMPS command.

    [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>    
    
  3. Run the job using the same deployment yaml you used earlier. You may need to delete the existing deployment. See Delete deployment.

    Here is an example output:

    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>
    
  4. While the simulation is running, you can view the Nvidia smi output. The output shows processes corresponding to the cuda containers (M + C type) with n-body simulation and the MPS service (C type) as running. All these processes share GPU 0.

    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 |
    +-----------------------------------------------------------------------------+
    
  5. After the simulation is complete, you can view the logs and the total time for the completion of the simulation. Run the following command:

        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>
    
  6. After the simulation is complete, you can view the Nvidia smi output again. Only the nvidia-cuda-mps-server process for the MPS service shows as running.

    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 |
    +-----------------------------------------------------------------------------+
    

Delete deployment

You may need to delete deployments when running with MPS enabled and with MPS disable on your device.

To delete the deployment on your device, run the following command:

kubectl delete -f <Path to the deployment .yaml> -n <Name of the namespace> 

Here is an example output:

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>

Next steps