Updated on 2024-08-16 GMT+08:00

Using GPU Virtualization

This section describes how to use the GPU virtualization capability to isolate the computing power from the GPU memory and efficiently use GPU device resources.

Prerequisites

Notes and Constraints

  • A single GPU can be virtualized into a maximum of 20 xGPU devices.
  • xGPUs cannot be used in init containers.
  • GPU virtualization supports two isolation modes: GPU memory isolation and isolation between GPU memory and computing power. A single GPU can schedule only workloads in the same isolation mode.
  • Autoscaler cannot be used to automatically scale in or out GPU nodes.
  • xGPU isolation does not allow you to request for GPU memory by calling CUDA API cudaMallocManaged(), which is also known as using UVM. For more information, see NVIDIA official documents. Use other methods to request for GPU memory, for example, by calling cudaMalloc().
  • When a containerized application is initializing, the real-time compute monitored by the nvidia-smi may exceed the upper limit of the available compute of the container.

Creating a GPU Virtualization Application

Using the CCE Console

  1. Log in to the CCE console.
  2. Click the cluster name to go to the cluster console, choose Workloads in the navigation pane, and click the Create Workload in the upper right corner.
  3. Set basic information about the workload.

    Choose Container Settings > Basic Info and configure the GPU quota.

    • Video memory: The unit is MiB. The value must be a positive integer that is a multiple of 128. If the value exceeds the memory of a single GPU, scheduling cannot be performed.
    • Computing power: The unit is %. The value must be a multiple of 5 and cannot exceed 100.
    • If the GPU memory is set to the capacity upper limit of a single GPU or the computing power is set to 100%, the entire GPU will be used.
    • When GPU virtualization is used, the workload scheduler defaults to Volcano and cannot be changed.
    Figure 1 Configuring the xGPU quota

    This section describes how to use GPU virtualization. For details about other parameters, see Workloads.

    After completing the setting, click Create.

  4. After a workload is created, you can try to verify the isolation capability of GPU virtualization.

    1. Log in to the pod and check the total GPU memory allocated to the pod.
      kubectl exec -it gpu-app -- nvidia-smi

      Expected output:

      Wed Apr 12 07:54:59 2023       
      +-----------------------------------------------------------------------------+
      | NVIDIA-SMI 470.141.03   Driver Version: 470.141.03   CUDA Version: 11.4     |
      |-------------------------------+----------------------+----------------------+
      | 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 V100-SXM2...  Off  | 00000000:21:01.0 Off |                    0 |
      | N/A   27C    P0    37W / 300W |   4912MiB /  5120MiB |      0%      Default |
      |                               |                      |                  N/A |
      +-------------------------------+----------------------+----------------------+
      
      +-----------------------------------------------------------------------------+
      | Processes:                                                                  |
      |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
      |        ID   ID                                                   Usage      |
      |=============================================================================|
      +-----------------------------------------------------------------------------+

      The expected output indicates that the total GPU memory allocated to the pod is 5120 MiB, and 4912 MiB is used.

    2. Run the following command on the node to check the isolation status of the GPU memory:
      nvidia-smi

      Expected output:

      Wed Apr 12 09:31:10 2023        
      +-----------------------------------------------------------------------------+
      | NVIDIA-SMI 470.141.03   Driver Version: 470.141.03   CUDA Version: 11.4     |
      |-------------------------------+----------------------+----------------------+
      | 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 V100-SXM2...  Off  | 00000000:21:01.0 Off |                    0 |
      | N/A   27C    P0    37W / 300W |   4957MiB / 16160MiB |      0%      Default |
      |                               |                      |                  N/A |
      +-------------------------------+----------------------+----------------------+
      
      +-----------------------------------------------------------------------------+
      | Processes:                                                                  |
      |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
      |        ID   ID                                                   Usage      |
      |=============================================================================|
      |    0   N/A  N/A    760445      C   python                           4835MiB |
      +-----------------------------------------------------------------------------+

      The expected output indicates that the total GPU memory on the node is 16160 MiB, and the example pod uses 4957 MiB.

Using kubectl

  1. Use kubectl to access the cluster.
  2. Create an application that uses GPU virtualization.

    The GPU memory isolation and isolation between GPU memory and computing power are supported. The computing power cannot be isolated only. volcano.sh and gpu-core.percentage cannot be set separately.

    Create a gpu-app.yaml file with the following content.

    • Isolate GPU memory only:
      apiVersion: apps/v1
      kind: Deployment
      metadata:
        name: gpu-app
        labels:
          app: gpu-app
      spec:
        replicas: 1
        selector:
          matchLabels:
            app: gpu-app
        template: 
          metadata:
            labels:
              app: gpu-app
          spec:
            containers:
            - name: container-1
              image: <your_image_address>     # Replace it with your image address.
              resources:
                limits:
                  volcano.sh/gpu-mem.128Mi: 40  # GPU memory allocated to the pod. The value is a multiple of 128 MiB (40 x 128 = 5120 MiB).
            imagePullSecrets:
              - name: default-secret
            schedulerName: volcano
    • Isolate the GPU memory from computing power:
      apiVersion: apps/v1
      kind: Deployment
      metadata:
        name: gpu-app
        labels:
          app: gpu-app
      spec:
        replicas: 1
        selector:
          matchLabels:
            app: gpu-app
        template: 
          metadata:
            labels:
              app: gpu-app
          spec:
            containers:
            - name: container-1
              image: <your_image_address>     # Replace it with your image address.
              resources:
                limits:
                  volcano.sh/gpu-mem.128Mi: 40  # GPU memory allocated to the pod. The value is a multiple of 128 MiB (40 x 128 = 5120 MiB).
                  volcano.sh/gpu-core.percentage: 25    # Computing power allocated to the pod
            imagePullSecrets:
              - name: default-secret
            schedulerName: volcano
    Table 1 Key parameters

    Parameter

    Mandatory

    Description

    volcano.sh/gpu-mem.128Mi

    No

    The value is a positive integer that is a multiple of 128 in the unit of MiB. If the value exceeds the memory of a single GPU, scheduling cannot be performed.

    volcano.sh/gpu-core.percentage

    No

    The unit of the computing power is %. The value must be a multiple of 5 and cannot exceed 100.

    • If the GPU memory is set to the capacity upper limit of a single GPU or the computing power is set to 100%, the entire GPU will be used.
    • When GPU virtualization is used, the workload scheduler defaults to Volcano and cannot be changed.

  3. Run the following command to create an application:

    kubectl apply -f gpu-app.yaml

  4. Verify the isolation capability of GPU virtualization.

    1. Log in to the pod and check the total GPU memory allocated to the pod.
      kubectl exec -it gpu-app -- nvidia-smi

      Expected output:

      Wed Apr 12 07:54:59 2023       
      +-----------------------------------------------------------------------------+
      | NVIDIA-SMI 470.141.03   Driver Version: 470.141.03   CUDA Version: 11.4     |
      |-------------------------------+----------------------+----------------------+
      | 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 V100-SXM2...  Off  | 00000000:21:01.0 Off |                    0 |
      | N/A   27C    P0    37W / 300W |   4912MiB /  5120MiB |      0%      Default |
      |                               |                      |                  N/A |
      +-------------------------------+----------------------+----------------------+
      
      +-----------------------------------------------------------------------------+
      | Processes:                                                                  |
      |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
      |        ID   ID                                                   Usage      |
      |=============================================================================|
      +-----------------------------------------------------------------------------+

      The expected output indicates that the total GPU memory allocated to the pod is 5120 MiB, and 4912 MiB is used.

    2. Run the following command on the node to check the isolation status of the GPU memory:
      nvidia-smi

      Expected output:

      Wed Apr 12 09:31:10 2023        
      +-----------------------------------------------------------------------------+
      | NVIDIA-SMI 470.141.03   Driver Version: 470.141.03   CUDA Version: 11.4     |
      |-------------------------------+----------------------+----------------------+
      | 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 V100-SXM2...  Off  | 00000000:21:01.0 Off |                    0 |
      | N/A   27C    P0    37W / 300W |   4957MiB / 16160MiB |      0%      Default |
      |                               |                      |                  N/A |
      +-------------------------------+----------------------+----------------------+
      
      +-----------------------------------------------------------------------------+
      | Processes:                                                                  |
      |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
      |        ID   ID                                                   Usage      |
      |=============================================================================|
      |    0   N/A  N/A    760445      C   python                           4835MiB |
      +-----------------------------------------------------------------------------+

      The expected output indicates that the total GPU memory on the node is 16160 MiB, and the example pod uses 4957 MiB.