k8s 使用显卡

k8s 显卡不能共用，8张显卡只能调度8个pod 使用，docker 是可以共享使用的

环境

• 硬件：Tesla T4 八张显卡

• 系统：Centos7.9 操作系统

• 服务器安装完操作系统，并安装完显卡驱动
  请参照：https://www.cnblogs.com/lixinliang/p/14705315.html

• k8s 使用gpu 显卡方式

固定gpu 服务器，需要先给指定的gpu 服务器打上污点
shell>kubectl taint node  lgy-dev-gpu-k8s-node7-105  server_type=gpu:NoSchedule

• 准备k8s deployment 配置文件

# cat pengyun-python-test111.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    name: pengyun-python-test111
  name: pengyun-python-test111
  namespace: pengyun
spec:
  replicas: 1
  selector:
    matchLabels:
      name: pengyun-python-test111
  strategy:
    rollingUpdate:
      maxSurge: 25%
      maxUnavailable: 25%
    type: RollingUpdate
  template:
    metadata:
      creationTimestamp: null
      labels:
        name: pengyun-python-test111
    spec:
      containers:
      - image: harbor.k8s.moviebook.cn/pengyun/dev/000003-pengyun/python_dev:20220106140120
        imagePullPolicy: IfNotPresent
        name: pengyun-python-test111
        resources:
          limits:
            cpu: "1"
            nvidia.com/gpu: "1"
          requests:
            cpu: "1"
            memory: 2Gi
            nvidia.com/gpu: "1"
        terminationMessagePath: /dev/termination-log
        terminationMessagePolicy: File
      dnsPolicy: ClusterFirst
      restartPolicy: Always
      schedulerName: default-scheduler
      securityContext: {}
      terminationGracePeriodSeconds: 30
      tolerations:   #设置容忍污点
      - effect: NoSchedule
        key: server_type
        operator: Equal
        value: gpu

• 创建yaml

shell> kubectl apply -f pengyun-python-test111.yaml

• 查看pod 创建情况

确认pod 已调度至指定污点的node 节点
kubectl get pod -n pengyun -o wide  |grep test111

• 查看pod gpu显卡

shell>kubectl exec -it -n pengyun  pengyun-python-test111-d7d895867-txtxp bash
shell>nvidia-smi

• 查看宿主机显卡