Default GPU Scheduling in Kubernetes

Prerequisites

• A GPU node has been created. For details, see Creating a Node.
• The CCE AI Suite (NVIDIA GPU) add-on has been installed. During the installation, select the driver corresponding to the GPU model on the node. For details, see CCE AI Suite (NVIDIA GPU).
• When the default GPU scheduling is used in clusters of v1.27 or earlier, the CCE AI Suite (NVIDIA GPU) add-on mounts the driver directory to /usr/local/nvidia/lib64, so you need to add /usr/local/nvidia/lib64 to the LD_LIBRARY_PATH environment variable to use GPUs in a container. You can skip this step for clusters of v1.28 or later.

  You can add environment variables in any of the following ways:

  • Configure the LD_LIBRARY_PATH environment variable in the Dockerfile used for creating an image. (Recommended)

    ENV LD_LIBRARY_PATH /usr/local/nvidia/lib64:$LD_LIBRARY_PATH

  • Configure the LD_LIBRARY_PATH environment variable in the image startup command.

    /bin/bash -c "export LD_LIBRARY_PATH=/usr/local/nvidia/lib64:$LD_LIBRARY_PATH && ..."

  • Define the LD_LIBRARY_PATH environment variable when creating a workload. (Ensure that this variable is not configured in the container. Otherwise, it will be overwritten.)

    ...
              env:
                - name: LD_LIBRARY_PATH
                  value: /usr/local/nvidia/lib64
    ...

Using GPUs

Create a workload and request GPUs. You can specify the number of GPUs as follows:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: gpu-test
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: gpu-test
  template:
    metadata:
      labels:
        app: gpu-test
    spec:
      containers:
      - image: nginx:perl
        name: container-0
        resources:
          requests:
            cpu: 250m
            memory: 512Mi
            nvidia.com/gpu: 1   # Number of requested GPUs
          limits:
            cpu: 250m
            memory: 512Mi
            nvidia.com/gpu: 1   # Maximum number of GPUs that can be used
      imagePullSecrets:
      - name: default-secret

nvidia.com/gpu specifies the number of GPUs to be requested. The value can be smaller than 1. For example, nvidia.com/gpu: 0.5 indicates that multiple pods share a GPU. In this case, all the requested GPU resources come from the same GPU card.

When you use nvidia.com/gpu to specify the number of GPUs, the values of requests and limits must be the same.

After nvidia.com/gpu is specified, workloads will not be scheduled to nodes without GPUs. If the node is GPU-starved, Kubernetes events similar to the following are reported:

• 0/2 nodes are available: 2 Insufficient nvidia.com/gpu.
• 0/4 nodes are available: 1 InsufficientResourceOnSingleGPU, 3 Insufficient nvidia.com/gpu.

To use GPU resources on the CCE console, you only need to configure the GPU quota when creating a workload.

GPU Node Labels

CCE will label GPU-enabled nodes after they are created. Different types of GPU-enabled nodes have different labels.

$ kubectl get node -L accelerator
NAME           STATUS   ROLES    AGE     VERSION                                    ACCELERATOR
10.100.2.179   Ready    <none>   8m43s   v1.19.10-r0-CCE21.11.1.B006-21.11.1.B006   nvidia-t4

When using GPUs, you can enable the affinity between pods and nodes based on labels so that the pods can be scheduled to the correct nodes.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: gpu-test
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: gpu-test
  template:
    metadata:
      labels:
        app: gpu-test
    spec:
      nodeSelector:
        accelerator: nvidia-t4
      containers:
      - image: nginx:perl
        name: container-0
        resources:
          requests:
            cpu: 250m
            memory: 512Mi
            nvidia.com/gpu: 1   # Number of requested GPUs
          limits:
            cpu: 250m
            memory: 512Mi
            nvidia.com/gpu: 1   # Maximum number of GPUs that can be used
      imagePullSecrets:
      - name: default-secret