删除harbor 推送镜像目录

script/harbor/harbor-install.sh

-#!/bin/bash
-harbor_host=192.168.1.32:5000
-echo "解压harbor到/usr/local/目录"
-tar -xvzf /root/k8s/package/harbor_aly.tar.gz -C /usr/local/
-cd /usr/local/harbor/ && sed -i s/harbor_host/$harbor_host/g harbor.cfg && ./prepare && ./install.sh
-
-

script/harbor/k8s-internal-service-push.sh

-#!/bin/bash
-#王庭威
-#用于推送k8s以及业务pod所需的基础镜像，这些镜像一般更新频率很低，所以放在gitlab上做归档。需要部署直接git clone下载镜像执行脚本即可。
-harbor_host=192.168.1.32:5000
-harbor_passwd=admin
-image_path=/root/k8s/images
-for image in `ls $image_path`
-do 
-echo "开始导入镜像到本地"
-docker load -i $image_path/$image
-done
-echo "开始给镜像打tag"
-docker tag autodl/image-proxy:v0.9.0 $harbor_host/k8s/image-proxy:v0.9.0
-docker tag autodl/ubuntu-basic:16.04 $harbor_host/k8s/ubuntu-basic:16.04
-docker tag coredns/coredns:1.5.0 $harbor_host/k8s/coredns:1.5.0
-docker tag nvidia/k8s-device-plugin:1.10 $harbor_host/k8s/k8s-device-plugin:1.10
-docker tag busybox:1.28.3 $harbor_host/k8s/busybox:1.28.3
-docker tag registry.cn-hangzhou.aliyuncs.com/google-containers/pause-amd64:3.0 $harbor_host/k8s/pause-amd64:3.0
-docker tag nginx:latest $harbor_host/k8s/nginx:latest
-docker tag nvidia/cuda:10.0-base-ubuntu16.04 $harbor_host/k8s/cuda:10.0-base-ubuntu16.04
-echo "开始推送镜像到harbor"
-docker login $harbor_host -u admin -p $harbor_passwd
-docker push $harbor_host/k8s/image-proxy:v0.9.0 
-docker push $harbor_host/k8s/ubuntu-basic:16.04
-docker push $harbor_host/k8s/coredns:1.5.0
-docker push $harbor_host/k8s/k8s-device-plugin:1.10
-docker push $harbor_host/k8s/busybox:1.28.3
-docker push $harbor_host/k8s/pause-amd64:3.0
-docker push $harbor_host/k8s/nginx:latest
-docker push $harbor_host/k8s/cuda:10.0-base-ubuntu16.04
-

script/harbor/seetaas-operator-pull-push.sh

-#!/bin/bash
-#王庭威
-# pull参数用于下载更新预处理器镜像以及导出镜像为压缩包、push参数为tag镜像以及推送镜像到私有仓库
-# 如果预处理不需要支持jupter，则除了operator镜像外，其他镜像文件注释。
-harbor_host=192.168.1.32:5000
-harbor_passwd=admin
-image_path=/data/operater-images
-base_url=hb.seetatech.com/k8s
-image_name=(
-operator:0.1.13
-#operator-python:0.0.1
-#dragon:0.3.0.dev20191121-cuda10.0-ubuntu16.04
-#pytorch:cuda9.2-py3-v0.4.1
-#pytorch:cuda10.0-py3-v1.2.0
-#tensorflow:gpu-py3-v1.14.0
-#tensorflow:gpu-py3-v1.5.0
-#tensorflow:gpu-py3-v1.8.0
-)
-####################################################
-pull_save() {
-echo "开始下载列表镜像"
-for image in ${image_name[@]}
-do
-docker pull $base_url/$image
-done
-echo "开始导出镜像"
-test -d $image_path || mkdir -pv $image_path
-for img in ${image_name[@]} 
-do
-echo "开始导出$img镜像"
-docker save $base_url/$img -o $image_path/$img.tar.gz
-done
-echo "开始压缩镜像目录.请耐心等待"
-tar -cvzf $image_path/operater-images.tar.gz $image_path
-}
-#####################################################
-tag_push() {
-for image in `ls $image_path`
-do 
-echo "开始导入镜像到本地"
-docker load -i $image_path/$image
-done
-echo "开始给镜像打tag"
-for i in ${image_name[@]}
-do
-echo $i
-docker tag hb.seetatech.com/k8s/$i $harbor_host/k8s/$i
-echo "开始仓库登录"
-docker login $harbor_host -u admin -p $harbor_passwd
-echo "开始推送镜像到私有部署仓库"
-docker push $harbor_host/k8s/$i
-done
-}
-#####################################################
-case $1 in
-push)
-  tag_push
-  ;;
-pull)
-  pull_save
-  ;;
-*)
-  echo "please input (pull|push)"
-esac
-
-

script/k8s/deploy-gpu-k8s.sh

@@ -14,7 +14,7 @@ image_list=`cat $image_path/image_list.txt`
 image_list_ksy=`cat $image_path/image_list_ksy.txt`
 host_name=`hostname`
 NFS_PATH=
-
+NFS_SERVER=
 ########################################################################################
 #ssh-keygen -y -f id_rsa > id_rsa.pub  私钥生成公钥
 #echo "判断是否允许root登陆"
@@ -454,8 +454,15 @@ echo "###################创建pv pvc#############"
 echo "########################修改pv模板#######################" && sleep 3
 sed -i s/NFS_PATH/$NFS_PATH/g /root/k8s/autodl_kpl_base/autodl-nfs/4-pv_pvc.yaml
 sed -i s/NFS_PATH/$NFS_PATH/g /root/k8s/autodl_kpl_base/kpl-nfs/4-pv_pvc.yaml
+sed -i s/NFS_SERVER/$NFS_SERVER/g /root/k8s/autodl_kpl_base/autodl-nfs/4-pv_pvc.yaml
+sed -i s/NFS_SERVER/$NFS_SERVER/g /root/k8s/autodl_kpl_base/kpl-nfs/4-pv_pvc.yaml
 kubectl apply -f /root/k8s/autodl_kpl_base/autodl-nfs
 kubectl apply -f /root/k8s/autodl_kpl_base/kpl-nfs
+echo  "###################还原pv pvc模板配置#############" && sleep 2
+sed -i s/$NFS_PATH/NFS_PATH/g /root/k8s/autodl_kpl_base/autodl-nfs/4-pv_pvc.yaml
+sed -i s/$NFS_PATH/NFS_PATH/g /root/k8s/autodl_kpl_base/kpl-nfs/4-pv_pvc.yaml
+sed -i s/$NFS_SERVER/NFS_SERVER/g /root/k8s/autodl_kpl_base/autodl-nfs/4-pv_pvc.yaml
+sed -i s/$NFS_SERVER/NFS_SERVER/g /root/k8s/autodl_kpl_base/kpl-nfs/4-pv_pvc.yaml
 echo "kpl-launcher所需证书" && sleep 2
 kubectl apply -f /root/k8s/autodl_kpl_base/kpl-ssl-configmap-autodl.yaml
 kubectl apply -f /root/k8s/autodl_kpl_base/kpl-ssl-configmap.yaml