1.上传镜像,并导入,打标签
2.创建dashboard的deployment和service
apiVersion: extensions/v1beta1 kind: Deployment metadata: # Keep the name in sync with image version and # gce/coreos/kube-manifests/addons/dashboard counterparts name: kubernetes-dashboard-latest namespace: kube-system spec: replicas: 1 template: metadata: labels: k8s-app: kubernetes-dashboard version: latest kubernetes.io/cluster-service: "true" spec: containers: - name: kubernetes-dashboard image: 10.0.0.11:5000/kubernetes-dashboard-amd64:v1.4.1 resources: # keep request = limit to keep this container in guaranteed class limits: cpu: 100m memory: 50Mi requests: cpu: 100m memory: 50Mi ports: - containerPort: 9090 args: - --apiserver-host=http://10.0.0.11:8080 livenessProbe: httpGet: path: / port: 9090 initialDelaySeconds: 30 timeoutSeconds: 30
apiVersion: v1 kind: Service metadata: name: kubernetes-dashboard namespace: kube-system labels: k8s-app: kubernetes-dashboard kubernetes.io/cluster-service: "true" spec: selector: k8s-app: kubernetes-dashboard ports: - port: 80 targetPort: 9090
3.通过http://10.0.0.11:8080/ui进行访问
4.资源类型
daemon sets : 每个容器创建一个,特别适合做监控使用该资源类型 (无状态)
pet sets: 有状态,适合数据库之类的
jobs: 一次性容器,适用于定时任务
[root@k8s-master deamonset]# cat k8s_deamon.yml apiVersion: extensions/v1beta1 kind: DaemonSet metadata: name: nginx spec: template: metadata: labels: app: nginx spec: containers: - name: nginx image: 10.0.0.11:5000/nginx:1.13 ports: - containerPort: 80 resources: limits: cpu: 100m requests: cpu: 100m
来源:https://www.cnblogs.com/yangxiaoni/p/12024994.html