k8s 調度 GPU

最近公司有項目想在 k8s 集群中運行 GPU 任務，於是研究了一下。下面是部署的步驟。

1. 首先得有一個可以運行的 k8s 集群. 集群部署參考 kubeadm安裝k8s 

2. 准備 GPU 節點

2.1 安裝驅動

curl -fsSL https://mirrors.aliyun.com/nvidia-cuda/ubuntu1804/x86_64/7fa2af80.pub | sudo apt-key add -
echo "deb https://mirrors.aliyun.com/nvidia-cuda/ubuntu1804/x86_64/ ./" > /etc/apt/sources.list.d/cuda.list

apt-get update
apt-get install -y cuda-drivers-455 # 按需要安裝對應的版本

2.2 安裝 nvidia-docker2

<!-- Note that you need to install the nvidia-docker2 package and not the nvidia-container-toolkit. This is because the new --gpus options hasn't reached kubernetes yet -->

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt-get update && sudo apt-get install -y nvidia-docker2

## /etc/docker/daemon.json 文件中加入以下內容, 使默認的運行時是 nvidia
{
    "default-runtime": "nvidia",
    "runtimes": {
        "nvidia": {
            "path": "/usr/bin/nvidia-container-runtime",
            "runtimeArgs": []
        }
    }
}

## 重啟 docker
sudo systemctl restart docker

2.3 在 k8s 集群中安裝 nvidia-device-plugin 使集群支持 GPU

kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.7.3/nvidia-device-plugin.yml

# 如果因為網絡問題訪問不到該文件, 可在瀏覽器打開 https://github.com/NVIDIA/k8s-device-plugin/blob/v0.7.3/nvidia-device-plugin.yml
## 把文件內容拷貝到本地執行

    nvidia-device-plugin 做三件事情

• Expose the number of GPUs on each nodes of your cluster

• Keep track of the health of your GPUs

• Run GPU enabled containers in your Kubernetes cluster.

之后把節點加入 k8s 集群

以上步驟成功完成之后, 運行以下命令能看到類似下面圖片中的內容說明插件安裝好了

kubectl get pod --all-namespaces | grep nvidia
kubectl describe node 10.31.0.17

 

 

3. 運行 GPU Jobs

# cat nvidia-gpu-demo.yaml
apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
spec:
  containers:
    - name: cuda-container
      image: nvidia/cuda:9.0-devel
      resources:
        limits:
          nvidia.com/gpu: 2 # requesting 2 GPUs
    - name: digits-container
      image: nvidia/digits:6.0
      resources:
        limits:
          nvidia.com/gpu: 2 # requesting 2 GPUs

kubectl apply -f nvidia-gpu-demo.yaml

kubectl exec -it xxx-76dd5bd849-hlmdr -- bash

# nvidia-smi

  

以上就簡單實現了 k8s 調度 GPU 任務。 

如有遇到問題可在留言區討論。