手把手教你一套完善且高效的k8s离线部署方案

作者:郝建伟

背景

面对更多项目现场交付,偶而会遇到客户环境不具备公网条件,完全内网部署,这就需要有一套完善且高效的离线部署方案。

系统资源

编号主机名称IP资源类型CPU内存磁盘
01k8s-master110.132.10.91CentOS-74c8g40g
02k8s-master110.132.10.92CentOS-74c8g40g
03k8s-master110.132.10.93CentOS-74c8g40g
04k8s-worker110.132.10.94CentOS-78c16g200g
05k8s-worker210.132.10.95CentOS-78c16g200g
06k8s-worker310.132.10.96CentOS-78c16g200g
07k8s-worker410.132.10.97CentOS-78c16g200g
08k8s-worker510.132.10.98CentOS-78c16g200g
09k8s-worker610.132.10.99CentOS-78c16g200g
10k8s-harbor&deploy10.132.10.100CentOS-74c8g500g
11k8s-nfs10.132.10.101CentOS-72c4g2000g
12k8s-lb10.132.10.120lb内网2c4g40g

参数配置

注:在全部节点执行以下操作

系统基础设置

工作、日志及数据存储目录设定

$ mkdir -p /export/servers
$ mkdir -p /export/logs
$ mkdir -p /export/data
$ mkdir -p /export/upload

内核及网络参数优化

$ vim /etc/sysctl.conf

# 设置以下内容
fs.file-max = 1048576
net.ipv4.tcp_syncookies = 1
net.ipv4.tcp_tw_reuse = 1
net.ipv4.tcp_fin_timeout = 5
net.ipv4.neigh.default.gc_stale_time = 120
net.ipv4.conf.default.rp_filter = 0
net.ipv4.conf.all.rp_filter = 0
net.ipv4.conf.all.arp_announce = 2
net.ipv4.conf.lo.arp_announce = 2 
vm.max_map_count = 262144

# 及时生效
sysctl -w vm.max_map_count=262144

ulimit优化

$ vim /etc/security/limits.conf 

# 设置以下内容
* soft memlock unlimited
* hard memlock unlimited
* soft nproc 102400
* hard nproc 102400
* soft nofile 1048576
* hard nofile 1048576

基础环境准备

ansible安装

1. 环境说明

名称说明
操作系统CentOS Linux release 7.8.2003
ansible2.9.27
节点deploy

2. 部署说明

物联管理平台机器数量繁多,需要ansible进行批量操作机器,节省时间,需要从部署节点至其他节点root免密。

注:在不知道root密码情况下,可以手动操作名密,按以下操作步骤执行:
# 需要在部署机器上执行以下命令生成公钥
$ ssh-keygen -t rsa
# 复制~/.ssh/id_rsa.pub内容,并粘贴至其他节点~/.ssh/authorized_keys文件里面
# 如果没有authorized_keys文件,可先执行创建创建在进行粘贴操作
$ touch ~/.ssh/authorized_keys

3. 部署步骤

1) 在线安装

$ yum -y install https://releases.ansible.com/ansible/rpm/release/epel-7-x86_64/ansible-2.9.27-1.el7.ans.noarch.rpm

2) 离线安装

# 提前上传ansible及所有依赖rpm包,并切换至rpm包目录
$ yum -y ./*rpm

3) 查看版本

$ ansible --version
ansible 2.9.27
  config file = /etc/ansible/ansible.cfg
  configured module search path = [u'/root/.ansible/plugins/modules', u'/usr/share/ansible/plugins/modules']
  ansible python module location = /usr/lib/python2.7/site-packages/ansible
  executable location = /usr/bin/ansible
  python version = 2.7.5 (default, Apr  2 2020, 13:16:51) [GCC 4.8.5 20150623 (Red Hat 4.8.5-39)]

4) 设置管理主机列表

$ vim /etc/ansible/hosts

[master]
10.132.10.91    node_name=k8s-master1
10.132.10.92    node_name=k8s-master2
10.132.10.93    node_name=k8s-master3

[worker]
10.132.10.94    node_name=k8s-worker1
10.132.10.95    node_name=k8s-worker2
10.132.10.96    node_name=k8s-worker3
10.132.10.97    node_name=k8s-worker4
10.132.10.98    node_name=k8s-worker5
10.132.10.99    node_name=k8s-worker6

[etcd]
10.132.10.91    etcd_name=etcd1
10.132.10.92    etcd_name=etcd2
10.132.10.93    etcd_name=etcd3

[k8s:children]
master
worker

5) 禁用ssh主机检查

$ vi /etc/ansible/ansible.cfg
# 修改以下设置
# uncomment this to disable SSH key host checking
host_key_checking = False

6) 取消SELINUX设定及放开防火墙

$ ansible k8s -m command -a "setenforce 0"
$ ansible k8s -m command -a "sed --follow-symlinks -i 's/SELINUX=enforcing/SELINUX=disabled/g' /etc/selinux/config"
$ ansible k8s -m command -a "firewall-cmd --set-default-zone=trusted"
$ ansible k8s -m command -a "firewall-cmd --complete-reload"
$ ansible k8s -m command -a "swapoff -a"

7)hosts设置

$ cd /export/upload && vim hosts_set.sh
#设置以下脚本内容
#!/bin/bashcat > /etc/hosts << EOF
127.0.0.1   localhost localhost.localdomain localhost4 localhost4.localdomain4
::1         localhost localhost.localdomain localhost6 localhost6.localdomain6
10.132.10.100 deploy harbor
10.132.10.91 master01
10.132.10.92 master02
10.132.10.93 master03
10.132.10.94 worker01
10.132.10.95 worker02
10.132.10.96 worker03
10.132.10.97 worker04
10.132.10.98 worker05
10.132.10.99 worker06
EOF

$ ansible new_worker -m copy -a 'src=/export/upload/hosts_set.sh dest=/export/upload'
$ ansible new_worker -m command -a 'sh /export/upload/hosts_set.sh'

docker安装

1. 环境说明

名称说明
操作系统CentOS Linux release 7.8.2003
dockerdocker-ce-20.10.17
节点deploy

2. 部署说明

k8s容器运行环境docker部署

3. 部署方法

1) 在线安装

$ yum -y install docker-ce-20.10.17

2) 离线安装

# 提前上传docker及所有依赖rpm包,并切换至rpm包目录
$ yum -y ./*rpm

3) 重新加载配置文件,启动并查看状态

$ systemctl start docker
$ systemctl status docker

4) 设置开机自启

$ systemctl enable docker 

5) 查看版本

$ docker version
Client: Docker Engine - Community
 Version:           20.10.17 
 API version:       1.41 
 Go version:        go1.17.11 
 Git commit:        100c701 
 Built:             Mon Jun  6 23:05:12 2022 
 OS/Arch:           linux/amd64 
 Context:           default 
 Experimental:      true
 
Server: Docker Engine - Community
 Engine:  
  Version:          20.10.17  
  API version:      1.41 (minimum version 1.12)  
  Go version:       go1.17.11  
  Git commit:       a89b842  
  Built:            Mon Jun  6 23:03:33 2022  
  OS/Arch:          linux/amd64  
  Experimental:     false 
 containerd:  
  Version:          1.6.8  
  GitCommit:        9cd3357b7fd7218e4aec3eae239db1f68a5a6ec6 
 runc: 
  Version:          1.1.4  
  GitCommit:        v1.1.4-0-g5fd4c4d 
 docker-init:  
 Version:          0.19.0  
 GitCommit:        de40ad0

docker-compose安装

1. 环境说明

名称说明
操作系统CentOS Linux release 7.8.2003
docker-composedocker-compose-linux-x86_64
节点deploy

2. 部署说明

harbor私有镜像库依赖。

3. 部署方法

1) 下载docker-compose并上传至服务器

$ curl -L https://github.com/docker/compose/releases/download/v2.9.0/docker-compose-linux-x86_64 -o docker-compose

2) 修改docker-compose执行权限

$ mv docker-compose /usr/local/bin/
$ chmod +x /usr/local/bin/docker-compose
$ docker-compose version

3) 查看版本

$ docker-compose version
Docker Compose version v2.9.0

harbor安装

1. 环境说明

名称说明
操作系统CentOS Linux release 7.8.2003
harborharbor-offline-installer-v2.4.3
节点harbor

2. 部署说明

私有镜像仓库。

3. 下载harbor离线安装包并上传至服务器

$ wget https://github.com/goharbor/harbor/releases/download/v2.4.3/harbor-offline-installer-v2.4.3.tgz

4. 解压安装包

$ tar -xzvf harbor-offline-installer-v2.4.3.tgz -C /export/servers/
$ cd /export/servers/harbor

5. 修改配置文件

$ mv harbor.yml.tmpl harbor.yml
$ vim harbor.yml

6. 设置以下内容

hostname: 10.132.10.100
http.port: 8090
data_volume: /export/data/harbor
log.location: /export/logs/harbor

7. 导入harbor镜像

$ docker load -i harbor.v2.4.3.tar.gz 
# 等待导入harbor依赖镜像文件
$ docker images
REPOSITORY                      TAG       IMAGE ID       CREATED       SIZE
goharbor/harbor-exporter        v2.4.3    776ac6ee91f4   4 weeks ago   81.5MB
goharbor/chartmuseum-photon     v2.4.3    f39a9694988d   4 weeks ago   172MB
goharbor/redis-photon           v2.4.3    b168e9750dc8   4 weeks ago   154MB
goharbor/trivy-adapter-photon   v2.4.3    a406a715461c   4 weeks ago   251MB
goharbor/notary-server-photon   v2.4.3    da89404c7cf9   4 weeks ago   109MB
goharbor/notary-signer-photon   v2.4.3    38468ac13836   4 weeks ago   107MB
goharbor/harbor-registryctl     v2.4.3    61243a84642b   4 weeks ago   135MB
goharbor/registry-photon        v2.4.3    9855479dd6fa   4 weeks ago   77.9MB
goharbor/nginx-photon           v2.4.3    0165c71ef734   4 weeks ago   44.4MB
goharbor/harbor-log             v2.4.3    57ceb170dac4   4 weeks ago   161MB
goharbor/harbor-jobservice      v2.4.3    7fea87c4b884   4 weeks ago   219MB
goharbor/harbor-core            v2.4.3    d864774a3b8f   4 weeks ago   197MB
goharbor/harbor-portal          v2.4.3    85f00db66862   4 weeks ago   53.4MB
goharbor/harbor-db              v2.4.3    7693d44a2ad6   4 weeks ago   225MB
goharbor/prepare                v2.4.3    c882d74725ee   4 weeks ago   268MB

8. 启动harbor

./prepare  # 如果有二次修改harbor.yml文件,请执行使配置文件生效
./install.sh --help # 查看启动参数
./install.sh --with-chartmuseum

运行环境搭建

docker安装

1. 环境说明

名称说明
操作系统CentOS Linux release 7.8.2003
dockerdocker-ce-20.10.17
节点k8s集群全部节点

2. 部署说明

k8s容器运行环境docker部署

3. 部署方法

1) 上传docker及依赖rpm包

$ ls /export/upload/docker-rpm.tgz 

2) 分发安装包

$ ansible k8s -m copy -a "src=/export/upload/docker-rpm.tgz dest=/export/upload/"
# 全部节点返回以下信息
CHANGED => {
    "ansible_facts": {
        "discovered_interpreter_python": "/usr/bin/python"
    }, 
    "changed": true, 
    "checksum": "acd3897edb624cd18a197bcd026e6769797f4f05", 
    "dest": "/export/upload/docker-rpm.tgz", 
    "gid": 0, 
    "group": "root", 
    "md5sum": "3ba6d9fe6b2ac70860b6638b88d3c89d", 
    "mode": "0644", 
    "owner": "root", 
    "secontext": "system_u:object_r:usr_t:s0", 
    "size": 103234394, 
    "src": "/root/.ansible/tmp/ansible-tmp-1661836788.82-13591-17885284311930/source", 
    "state": "file", 
    "uid": 0
}

3) 执行解压并安装

$ ansible k8s -m shell -a "tar xzvf /export/upload/docker-rpm.tgz -C /export/upload/ && yum -y install /export/upload/docker-rpm/*"

4) 设置开机自启并启动

$ ansible k8s -m shell -a "systemctl enable docker && systemctl start docker"

5) 查看版本

$ ansible k8s -m shell -a "docker version"
# 全部节点返回以下信息
CHANGED | rc=0 >>
Client: Docker Engine - Community
 Version:           20.10.17
 API version:       1.41
 Go version:        go1.17.11
 Git commit:        100c701
 Built:             Mon Jun  6 23:05:12 2022
 OS/Arch:           linux/amd64
 Context:           default
 Experimental:      true

Server: Docker Engine - Community
 Engine:
  Version:          20.10.17
  API version:      1.41 (minimum version 1.12)
  Go version:       go1.17.11
  Git commit:       a89b842
  Built:            Mon Jun  6 23:03:33 2022
  OS/Arch:          linux/amd64
  Experimental:     false
 containerd:
  Version:          1.6.8
  GitCommit:        9cd3357b7fd7218e4aec3eae239db1f68a5a6ec6
 runc:
  Version:          1.1.4
  GitCommit:        v1.1.4-0-g5fd4c4d
 docker-init:
  Version:          0.19.0
  GitCommit:        de40ad0

kubernetes安装

有网环境安装

# 添加阿里云YUM的软件源:
cat > /etc/yum.repos.d/kubernetes.repo << EOF
[kubernetes]
name=Kubernetes
baseurl=https://mirrors.aliyun.com/kubernetes/yum/repos/kubernetes-el7-x86_64
enabled=1
gpgcheck=0
repo_gpgcheck=0
gpgkey=https://mirrors.aliyun.com/kubernetes/yum/doc/yum-key.gpg https://mirrors.aliyun.com/kubernetes/yum/doc/rpm-package-key.gpg
EOF

下载离线安装包

# 创建rpm软件存储目录:
mkdir -p /export/download/kubeadm-rpm

# 执行命令:
yum install -y kubelet-1.22.4 kubeadm-1.22.4 kubectl-1.22.4 --downloadonly --downloaddir /export/download/kubeadm-rpm

无网环境安装

1) 上传kubeadm及依赖rpm包

$ ls /export/upload/
kubeadm-rpm.tgz 

2) 分发安装包

$ ansible k8s -m copy -a "src=/export/upload/kubeadm-rpm.tgz dest=/export/upload/"
# 全部节点返回以下信息
CHANGED => {
    "ansible_facts": {
        "discovered_interpreter_python": "/usr/bin/python"
    }, 
    "changed": true, 
    "checksum": "3fe96fe1aa7f4a09d86722f79f36fb8fde69facb", 
    "dest": "/export/upload/kubeadm-rpm.tgz", 
    "gid": 0, 
    "group": "root", 
    "md5sum": "80d5bda420db6ea23ad75dcf0f76e858", 
    "mode": "0644", 
    "owner": "root", 
    "secontext": "system_u:object_r:usr_t:s0", 
    "size": 67423355, 
    "src": "/root/.ansible/tmp/ansible-tmp-1661840257.4-33361-139823848282879/source", 
    "state": "file", 
    "uid": 0
}

3) 执行解压并安装

$ ansible k8s -m shell -a "tar xzvf /export/upload/kubeadm-rpm.tgz -C /export/upload/ && yum -y install /export/upload/kubeadm-rpm/*"

4) 设置开机自启并启动

$ ansible k8s -m shell -a "systemctl enable kubelet && systemctl start kubelet"
注:此时kubelet启动失败,会进入不断重启,这个是正常现象,执行init或join后问题会自动解决,对此官网有如下描述,也就是此时不用理会kubelet.service,可执行发下命令查看kubelet状态。
$ journalctl -xefu kubelet

4) 分发依赖镜像至集群节点

# 可以在有公网环境提前下载镜像
$ docker pull registry.cn-hangzhou.aliyuncs.com/google_containers/coredns:v1.8.4
$ docker pull registry.cn-hangzhou.aliyuncs.com/google_containers/etcd:3.5.0-0
$ docker pull registry.cn-hangzhou.aliyuncs.com/google_containers/kube-apiserver:v1.22.4
$ docker pull registry.cn-hangzhou.aliyuncs.com/google_containers/kube-controller-manager:v1.22.4
$ docker pull registry.cn-hangzhou.aliyuncs.com/google_containers/kube-proxy:v1.22.4
$ docker pull registry.cn-hangzhou.aliyuncs.com/google_containers/kube-scheduler:v1.22.4
$ docker pull registry.cn-hangzhou.aliyuncs.com/google_containers/pause:3.5
$ docker pull rancher/mirrored-flannelcni-flannel-cni-plugin:v1.1.0
$ docker pull rancher/mirrored-flannelcni-flannel:v0.19.1
# 导出镜像文件,上传部署节点并导入镜像库
$ ls /export/upload

$ docker load -i google_containers-coredns-v1.8.4.tar
$ docker load -i google_containers-etcd:3.5.0-0.tar
$ docker load -i google_containers-kube-apiserver:v1.22.4.tar
$ docker load -i google_containers-kube-controller-manager-v1.22.4.tar
$ docker load -i google_containers-kube-proxy-v1.22.4.tar
$ docker load -i google_containers-kube-scheduler-v1.22.4.tar
$ docker load -i google_containers-pause-3.5.tar
$ docker load -i rancher-mirrored-flannelcni-flannel-cni-plugin-v1.1.0.tar
$ docker load -i rancher-mirrored-flannelcni-flannel-v0.19.1.tar

# 镜像打harbor镜像库tag
$ docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/coredns:v1.8.4 10.132.10.100:8090/community/coredns:v1.8.4
$ docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/etcd:3.5.0-0 10.132.10.100:8090/community/etcd:3.5.0-0
$ docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/kube-apiserver:v1.22.4 10.132.10.100:8090/community/kube-apiserver:v1.22.4
$ docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/kube-controller-manager:v1.22.4 10.132.10.100:8090/community/kube-controller-manager:v1.22.4
$ docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/kube-proxy:v1.22.4 10.132.10.100:8090/community/kube-proxy:v1.22.4
$ docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/kube-scheduler:v1.22.4 10.132.10.100:8090/community/kube-scheduler:v1.22.4
$ docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/pause:3.5 10.132.10.100:8090/community/pause:3.5
$ docker tag rancher/mirrored-flannelcni-flannel-cni-plugin:v1.1.0 10.132.10.100:8090/community/mirrored-flannelcni-flannel-cni-plugin:v1.1.0
$ docker tag rancher/mirrored-flannelcni-flannel:v0.19.1 10.132.10.100:8090/community/mirrored-flannelcni-flannel:v0.19.1

# 推送至harbor镜像库
$ docker push 192.168.186.120:8090/community/coredns:v1.8.4
$ docker push 192.168.186.120:8090/community/etcd:3.5.0-0
$ docker push 192.168.186.120:8090/community/kube-apiserver:v1.22.4
$ docker push 192.168.186.120:8090/community/kube-controller-manager:v1.22.4
$ docker push 192.168.186.120:8090/community/kube-proxy:v1.22.4
$ docker push 192.168.186.120:8090/community/kube-scheduler:v1.22.4
$ docker push 192.168.186.120:8090/community/pause:3.5
$ docker push 192.168.186.120:8090/community/mirrored-flannelcni-flannel-cni-plugin:v1.1.0
$ docker push 192.168.186.120:8090/community/mirrored-flannelcni-flannel:v0.19.1

5)部署首个master

$ kubeadm init \
--control-plane-endpoint "10.132.10.91:6443" \
--image-repository 10.132.10.100/community \
--kubernetes-version v1.22.4 \
--service-cidr=172.16.0.0/16 \
--pod-network-cidr=10.244.0.0/16 \
--token "abcdef.0123456789abcdef" \
--token-ttl "0" \
--upload-certs

# 显示以下信息
[init] Using Kubernetes version: v1.22.4
[preflight] Running pre-flight checks
	[WARNING Firewalld]: firewalld is active, please ensure ports [6443 10250] are open or your cluster may not function correctly
[preflight] Pulling images required for setting up a Kubernetes cluster
[preflight] This might take a minute or two, depending on the speed of your internet connection
[preflight] You can also perform this action in beforehand using 'kubeadm config images pull'
[certs] Using certificateDir folder "/etc/kubernetes/pki"
[certs] Generating "ca" certificate and key
[certs] Generating "apiserver" certificate and key
[certs] apiserver serving cert is signed for DNS names [kubernetes kubernetes.default kubernetes.default.svc kubernetes.default.svc.cluster.local master01] and IPs [172.16.0.1 10.132.10.91]
[certs] Generating "apiserver-kubelet-client" certificate and key
[certs] Generating "front-proxy-ca" certificate and key
[certs] Generating "front-proxy-client" certificate and key
[certs] Generating "etcd/ca" certificate and key
[certs] Generating "etcd/server" certificate and key
[certs] etcd/server serving cert is signed for DNS names [localhost master01] and IPs [10.132.10.91 127.0.0.1 ::1]
[certs] Generating "etcd/peer" certificate and key
[certs] etcd/peer serving cert is signed for DNS names [localhost master01] and IPs [10.132.10.91 127.0.0.1 ::1]
[certs] Generating "etcd/healthcheck-client" certificate and key
[certs] Generating "apiserver-etcd-client" certificate and key
[certs] Generating "sa" key and public key
[kubeconfig] Using kubeconfig folder "/etc/kubernetes"
[kubeconfig] Writing "admin.conf" kubeconfig file
[kubeconfig] Writing "kubelet.conf" kubeconfig file
[kubeconfig] Writing "controller-manager.conf" kubeconfig file
[kubeconfig] Writing "scheduler.conf" kubeconfig file
[kubelet-start] Writing kubelet environment file with flags to file "/var/lib/kubelet/kubeadm-flags.env"
[kubelet-start] Writing kubelet configuration to file "/var/lib/kubelet/config.yaml"
[kubelet-start] Starting the kubelet
[control-plane] Using manifest folder "/etc/kubernetes/manifests"
[control-plane] Creating static Pod manifest for "kube-apiserver"
[control-plane] Creating static Pod manifest for "kube-controller-manager"
[control-plane] Creating static Pod manifest for "kube-scheduler"
[etcd] Creating static Pod manifest for local etcd in "/etc/kubernetes/manifests"
[wait-control-plane] Waiting for the kubelet to boot up the control plane as static Pods from directory "/etc/kubernetes/manifests". This can take up to 4m0s
[apiclient] All control plane components are healthy after 11.008638 seconds
[upload-config] Storing the configuration used in ConfigMap "kubeadm-config" in the "kube-system" Namespace
[kubelet] Creating a ConfigMap "kubelet-config-1.22" in namespace kube-system with the configuration for the kubelets in the cluster
[upload-certs] Storing the certificates in Secret "kubeadm-certs" in the "kube-system" Namespace
[upload-certs] Using certificate key:
9151ea7bb260a42297f2edc486d5792f67d9868169310b82ef1eb18f6e4c0f13
[mark-control-plane] Marking the node master01 as control-plane by adding the labels: [node-role.kubernetes.io/master(deprecated) node-role.kubernetes.io/control-plane node.kubernetes.io/exclude-from-external-load-balancers]
[mark-control-plane] Marking the node master01 as control-plane by adding the taints [node-role.kubernetes.io/master:NoSchedule]
[bootstrap-token] Using token: abcdef.0123456789abcdef
[bootstrap-token] Configuring bootstrap tokens, cluster-info ConfigMap, RBAC Roles
[bootstrap-token] configured RBAC rules to allow Node Bootstrap tokens to get nodes
[bootstrap-token] configured RBAC rules to allow Node Bootstrap tokens to post CSRs in order for nodes to get long term certificate credentials
[bootstrap-token] configured RBAC rules to allow the csrapprover controller automatically approve CSRs from a Node Bootstrap Token
[bootstrap-token] configured RBAC rules to allow certificate rotation for all node client certificates in the cluster
[bootstrap-token] Creating the "cluster-info" ConfigMap in the "kube-public" namespace
[kubelet-finalize] Updating "/etc/kubernetes/kubelet.conf" to point to a rotatable kubelet client certificate and key
[addons] Applied essential addon: CoreDNS
[addons] Applied essential addon: kube-proxy

Your Kubernetes control-plane has initialized successfully!

To start using your cluster, you need to run the following as a regular user:

  mkdir -p $HOME/.kube
  sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
  sudo chown $(id -u):$(id -g) $HOME/.kube/config

Alternatively, if you are the root user, you can run:

  export KUBECONFIG=/etc/kubernetes/admin.conf

You should now deploy a pod network to the cluster.
Run "kubectl apply -f [podnetwork].yaml" with one of the options listed at:
  https://kubernetes.io/docs/concepts/cluster-administration/addons/

You can now join any number of the control-plane node running the following command on each as root:

  kubeadm join 10.132.10.91:6443 --token abcdef.0123456789abcdef \
	--discovery-token-ca-cert-hash sha256:4884a98b0773bc89c36dc5fa51569293103ff093e9124431c4c8c2d5801a96a2 \
	--control-plane --certificate-key 9151ea7bb260a42297f2edc486d5792f67d9868169310b82ef1eb18f6e4c0f13

Please note that the certificate-key gives access to cluster sensitive data, keep it secret!
As a safeguard, uploaded-certs will be deleted in two hours; If necessary, you can use
"kubeadm init phase upload-certs --upload-certs" to reload certs afterward.

Then you can join any number of worker nodes by running the following on each as root:

kubeadm join 10.132.10.91:6443 --token abcdef.0123456789abcdef \
	--discovery-token-ca-cert-hash sha256:4884a98b0773bc89c36dc5fa51569293103ff093e9124431c4c8c2d5801a96a2

6)生成kubelet环境配置文件

# 执行命令
$ mkdir -p $HOME/.kube
$ sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
$ sudo chown $(id -u):$(id -g) $HOME/.kube/config

7)配置网络插件flannel

# 创建flannel.yml文件
$ touch /export/servers/kubernetes/flannel.yml
$ vim /export/servers/kubernetes/flannel.yml
# 设置以下内容,需要关注有网无网时对应的地址切换
---
kind: Namespace
apiVersion: v1
metadata:
  name: kube-flannel
  labels:
    pod-security.kubernetes.io/enforce: privileged
---
kind: ClusterRole
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: flannel
rules:
- apiGroups:
  - ""
  resources:
  - pods
  verbs:
  - get
- apiGroups:
  - ""
  resources:
  - nodes
  verbs:
  - list
  - watch
- apiGroups:
  - ""
  resources:
  - nodes/status
  verbs:
  - patch
---
kind: ClusterRoleBinding
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: flannel
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: flannel
subjects:
- kind: ServiceAccount
  name: flannel
  namespace: kube-flannel
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: flannel
  namespace: kube-flannel
---
kind: ConfigMap
apiVersion: v1
metadata:
  name: kube-flannel-cfg
  namespace: kube-flannel
  labels:
    tier: node
    app: flannel
data:
  cni-conf.json: |
    {
      "name": "cbr0",
      "cniVersion": "0.3.1",
      "plugins": [
        {
          "type": "flannel",
          "delegate": {
            "hairpinMode": true,
            "isDefaultGateway": true
          }
        },
        {
          "type": "portmap",
          "capabilities": {
            "portMappings": true
          }
        }
      ]
    }
  net-conf.json: |
    {
      "Network": "10.244.0.0/16",
      "Backend": {
        "Type": "vxlan"
      }
    }
---
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: kube-flannel-ds
  namespace: kube-flannel
  labels:
    tier: node
    app: flannel
spec:
  selector:
    matchLabels:
      app: flannel
  template:
    metadata:
      labels:
        tier: node
        app: flannel
    spec:
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
            - matchExpressions:
              - key: kubernetes.io/os
                operator: In
                values:
                - linux
      hostNetwork: true
      priorityClassName: system-node-critical
      tolerations:
      - operator: Exists
        effect: NoSchedule
      serviceAccountName: flannel
      initContainers:
      - name: install-cni-plugin
        # 在有网环境下可以切换下面地址
        # image: docker.io/rancher/mirrored-flannelcni-flannel-cni-plugin:v1.1.0
        # 在无网环境下需要使用私有harbor地址
        image: 10.132.10.100:8090/community/mirrored-flannelcni-flannel-cni-plugin:v1.1.0
        command:
        - cp
        args:
        - -f
        - /flannel
        - /opt/cni/bin/flannel
        volumeMounts:
        - name: cni-plugin
          mountPath: /opt/cni/bin
      - name: install-cni
        # 在有网环境下可以切换下面地址
        # image: docker.io/rancher/mirrored-flannelcni-flannel:v0.19.1
        # 在无网环境下需要使用私有harbor地址
        image: 10.132.10.100:8090/community/mirrored-flannelcni-flannel:v0.19.1
        command:
        - cp
        args:
        - -f
        - /etc/kube-flannel/cni-conf.json
        - /etc/cni/net.d/10-flannel.conflist
        volumeMounts:
        - name: cni
          mountPath: /etc/cni/net.d
        - name: flannel-cfg
          mountPath: /etc/kube-flannel/
      containers:
      - name: kube-flannel
        # 在有网环境下可以切换下面地址
        # image: docker.io/rancher/mirrored-flannelcni-flannel:v0.19.1
        # 在无网环境下需要使用私有harbor地址
        image: 10.132.10.100:8090/community/mirrored-flannelcni-flannel:v0.19.1
        command:
        - /opt/bin/flanneld
        args:
        - --ip-masq
        - --kube-subnet-mgr
        resources:
          requests:
            cpu: "100m"
            memory: "50Mi"
          limits:
            cpu: "100m"
            memory: "50Mi"
        securityContext:
          privileged: false
          capabilities:
            add: ["NET_ADMIN", "NET_RAW"]
        env:
        - name: POD_NAME
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        - name: POD_NAMESPACE
          valueFrom:
            fieldRef:
              fieldPath: metadata.namespace
        - name: EVENT_QUEUE_DEPTH
          value: "5000"
        volumeMounts:
        - name: run
          mountPath: /run/flannel
        - name: flannel-cfg
          mountPath: /etc/kube-flannel/
        - name: xtables-lock
          mountPath: /run/xtables.lock
      volumes:
      - name: run
        hostPath:
          path: /run/flannel
      - name: cni-plugin
        hostPath:
          path: /opt/cni/bin
      - name: cni
        hostPath:
          path: /etc/cni/net.d
      - name: flannel-cfg
        configMap:
          name: kube-flannel-cfg
      - name: xtables-lock
        hostPath:
          path: /run/xtables.lock
          type: FileOrCreate

8)安装网络插件flannel

# 生效yml配置文件
$ kubectl apply -f kube-flannel.yml

# 查看pods状态
$ kubectl get pods -A
NAMESPACE      NAME                               READY   STATUS    RESTARTS   AGE
kube-flannel   kube-flannel-ds-kjmt4              1/1     Running   0          148m
kube-system    coredns-7f84d7b4b5-7qr8g           1/1     Running   0          4h18m
kube-system    coredns-7f84d7b4b5-fljws           1/1     Running   0          4h18m
kube-system    etcd-master01                      1/1     Running   0          4h19m
kube-system    kube-apiserver-master01            1/1     Running   0          4h19m
kube-system    kube-controller-manager-master01   1/1     Running   0          4h19m
kube-system    kube-proxy-wzq2t                   1/1     Running   0          4h18m
kube-system    kube-scheduler-master01            1/1     Running   0          4h19m

9)加入其他master节点

# 在master01执行如下操作
# 查看token列表
$ kubeadm token list

# master01执行init操作后生成加入命令如下
$ kubeadm join 10.132.10.91:6443 \
--token abcdef.0123456789abcdef \
--discovery-token-ca-cert-hash sha256:4884a98b0773bc89c36dc5fa51569293103ff093e9124431c4c8c2d5801a96a2 \
--control-plane --certificate-key 9151ea7bb260a42297f2edc486d5792f67d9868169310b82ef1eb18f6e4c0f13

# 在其他master节点执行如下操作
# 分别执行上一步的加入命令,加入master节点至集群
$ kubeadm join 10.132.10.91:6443 \
--token abcdef.0123456789abcdef \
--discovery-token-ca-cert-hash sha256:4884a98b0773bc89c36dc5fa51569293103ff093e9124431c4c8c2d5801a96a2 \
--control-plane --certificate-key 9151ea7bb260a42297f2edc486d5792f67d9868169310b82ef1eb18f6e4c0f13

# 此处如果报错,一般是certificate-key过期,可以在master01执行如下命令更新
$ kubeadm init phase upload-certs --upload-certs
3b647155b06311d39faf70cb094d9a5e102afd1398323e820cfb3cfd868ae58f

# 将上面生成的值替换certificate-key值再次在其他master节点执行如下命令
$ kubeadm join 10.132.10.91:6443 \
--token abcdef.0123456789abcdef \
--discovery-token-ca-cert-hash sha256:4884a98b0773bc89c36dc5fa51569293103ff093e9124431c4c8c2d5801a96a2     
--control-plane 
--certificate-key 3b647155b06311d39faf70cb094d9a5e102afd1398323e820cfb3cfd868ae58f

# 生成kubelet环境配置文件
$ mkdir -p $HOME/.kube
$ sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
$ sudo chown $(id -u):$(id -g) $HOME/.kube/config

# 在任意master节点执行查看节点状态命令
$ kubectl get nodes
NAME       STATUS   ROLES                  AGE     VERSION
master01   Ready    control-plane,master   5h58m   v1.22.4
master02   Ready    control-plane,master   45m     v1.22.4
master03   Ready    control-plane,master   44m     v1.22.4

9)加入worker节点

# 在其他worker节点执行master01执行init操作后生成的加入命令如下
# 分别执行上一步的加入命令,加入master节点至集群
$ kubeadm join 10.132.10.91:6443 \
--token abcdef.0123456789abcdef \
--discovery-token-ca-cert-hash sha256:4884a98b0773bc89c36dc5fa51569293103ff093e9124431c4c8c2d5801a96a2

# 此处如果报错,一般是token过期,可以在master01执行如下命令重新生成加入命令
$ kubeadm token create --print-join-command
kubeadm join 10.132.10.91:6443 \
--token abcdef.0123456789abcdef \
--discovery-token-ca-cert-hash sha256:cf30ddd3df1c6215b886df1ea378a68ad5a9faad7933d53ca9891ebbdf9a1c3f

# 将上面生成的加入命令再次在其他worker节点执行
# 查看集成状态
$ kubectl get nodes
NAME       STATUS   ROLES                  AGE     VERSION
master01   Ready    control-plane,master   6h12m   v1.22.4
master02   Ready    control-plane,master   58m     v1.22.4
master03   Ready    control-plane,master   57m     v1.22.4
worker01   Ready    <none>                 5m12s   v1.22.4
worker02   Ready    <none>                 4m10s   v1.22.4
worker03   Ready    <none>                 3m42s   v1.22.4

10)配置kubernetes dashboard

apiVersion: v1
kind: Namespace
metadata:
  name: kubernetes-dashboard

---

apiVersion: v1
kind: ServiceAccount
metadata:
  labels:
    k8s-app: kubernetes-dashboard
  name: kubernetes-dashboard
  namespace: kubernetes-dashboard

---

kind: Service
apiVersion: v1
metadata:
  labels:
    k8s-app: kubernetes-dashboard
  name: kubernetes-dashboard
  namespace: kubernetes-dashboard
spec:
  type: NodePort
  ports:
    - port: 443
      targetPort: 8443
      nodePort: 31001
  selector:
    k8s-app: kubernetes-dashboard

---

apiVersion: v1
kind: Secret
metadata:
  labels:
    k8s-app: kubernetes-dashboard
  name: kubernetes-dashboard-certs
  namespace: kubernetes-dashboard
type: Opaque

---

apiVersion: v1
kind: Secret
metadata:
  labels:
    k8s-app: kubernetes-dashboard
  name: kubernetes-dashboard-key-holder
  namespace: kubernetes-dashboard
type: Opaque

---

kind: ConfigMap
apiVersion: v1
metadata:
  labels:
    k8s-app: kubernetes-dashboard
  name: kubernetes-dashboard-settings
  namespace: kubernetes-dashboard

---

kind: Role
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  labels:
    k8s-app: kubernetes-dashboard
  name: kubernetes-dashboard
  namespace: kubernetes-dashboard
rules:
  # Allow Dashboard to get, update and delete Dashboard exclusive secrets.
  - apiGroups: [""]
    resources: ["secrets"]
    resourceNames: ["kubernetes-dashboard-key-holder", "kubernetes-dashboard-certs", "kubernetes-dashboard-csrf"]
    verbs: ["get", "update", "delete"]
    # Allow Dashboard to get and update 'kubernetes-dashboard-settings' config map.
  - apiGroups: [""]
    resources: ["configmaps"]
    resourceNames: ["kubernetes-dashboard-settings"]
    verbs: ["get", "update"]
    # Allow Dashboard to get metrics.
  - apiGroups: [""]
    resources: ["services"]
    resourceNames: ["heapster", "dashboard-metrics-scraper"]
    verbs: ["proxy"]
  - apiGroups: [""]
    resources: ["services/proxy"]
    resourceNames: ["heapster", "http:heapster:", "https:heapster:", "dashboard-metrics-scraper", "http:dashboard-metrics-scraper"]
    verbs: ["get"]

---

kind: ClusterRole
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  labels:
    k8s-app: kubernetes-dashboard
  name: kubernetes-dashboard
rules:
  # Allow Metrics Scraper to get metrics from the Metrics server
  - apiGroups: ["metrics.k8s.io"]
    resources: ["pods", "nodes"]
    verbs: ["get", "list", "watch"]

---

apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  labels:
    k8s-app: kubernetes-dashboard
  name: kubernetes-dashboard
  namespace: kubernetes-dashboard
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: Role
  name: kubernetes-dashboard
subjects:
  - kind: ServiceAccount
    name: kubernetes-dashboard
    namespace: kubernetes-dashboard

---

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: kubernetes-dashboard
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: kubernetes-dashboard
subjects:
  - kind: ServiceAccount
    name: kubernetes-dashboard
    namespace: kubernetes-dashboard

---

kind: Deployment
apiVersion: apps/v1
metadata:
  labels:
    k8s-app: kubernetes-dashboard
  name: kubernetes-dashboard
  namespace: kubernetes-dashboard
spec:
  replicas: 1
  revisionHistoryLimit: 10
  selector:
    matchLabels:
      k8s-app: kubernetes-dashboard
  template:
    metadata:
      labels:
        k8s-app: kubernetes-dashboard
    spec:
      securityContext:
        seccompProfile:
          type: RuntimeDefault
      containers:
        - name: kubernetes-dashboard
          image: kubernetesui/dashboard:v2.5.0
          imagePullPolicy: Always
          ports:
            - containerPort: 8443
              protocol: TCP
          args:
            - --auto-generate-certificates
            - --namespace=kubernetes-dashboard
            # Uncomment the following line to manually specify Kubernetes API server Host
            # If not specified, Dashboard will attempt to auto discover the API server and connect
            # to it. Uncomment only if the default does not work.
            # - --apiserver-host=http://my-address:port
          volumeMounts:
            - name: kubernetes-dashboard-certs
              mountPath: /certs
              # Create on-disk volume to store exec logs
            - mountPath: /tmp
              name: tmp-volume
          livenessProbe:
            httpGet:
              scheme: HTTPS
              path: /
              port: 8443
            initialDelaySeconds: 30
            timeoutSeconds: 30
          securityContext:
            allowPrivilegeEscalation: false
            readOnlyRootFilesystem: true
            runAsUser: 1001
            runAsGroup: 2001
      volumes:
        - name: kubernetes-dashboard-certs
          secret:
            secretName: kubernetes-dashboard-certs
        - name: tmp-volume
          emptyDir: {}
      serviceAccountName: kubernetes-dashboard
      nodeSelector:
        "kubernetes.io/os": linux
      # Comment the following tolerations if Dashboard must not be deployed on master
      tolerations:
        - key: node-role.kubernetes.io/master
          effect: NoSchedule

---

kind: Service
apiVersion: v1
metadata:
  labels:
    k8s-app: dashboard-metrics-scraper
  name: dashboard-metrics-scraper
  namespace: kubernetes-dashboard
spec:
  ports:
    - port: 8000
      targetPort: 8000
  selector:
    k8s-app: dashboard-metrics-scraper

---

kind: Deployment
apiVersion: apps/v1
metadata:
  labels:
    k8s-app: dashboard-metrics-scraper
  name: dashboard-metrics-scraper
  namespace: kubernetes-dashboard
spec:
  replicas: 1
  revisionHistoryLimit: 10
  selector:
    matchLabels:
      k8s-app: dashboard-metrics-scraper
  template:
    metadata:
      labels:
        k8s-app: dashboard-metrics-scraper
    spec:
      securityContext:
        seccompProfile:
          type: RuntimeDefault
      containers:
        - name: dashboard-metrics-scraper
          image: kubernetesui/metrics-scraper:v1.0.7
          ports:
            - containerPort: 8000
              protocol: TCP
          livenessProbe:
            httpGet:
              scheme: HTTP
              path: /
              port: 8000
            initialDelaySeconds: 30
            timeoutSeconds: 30
          volumeMounts:
          - mountPath: /tmp
            name: tmp-volume
          securityContext:
            allowPrivilegeEscalation: false
            readOnlyRootFilesystem: true
            runAsUser: 1001
            runAsGroup: 2001
      serviceAccountName: kubernetes-dashboard
      nodeSelector:
        "kubernetes.io/os": linux
      # Comment the following tolerations if Dashboard must not be deployed on master
      tolerations:
        - key: node-role.kubernetes.io/master
          effect: NoSchedule
      volumes:
        - name: tmp-volume
          emptyDir: {}

11)生成dashboard自签证书

$ mkdir -p /export/servers/kubernetes/certs && cd /export/servers/kubernetes/certs/
$ openssl genrsa -out dashboard.key 2048
$ openssl req -days 3650 -new -key dashboard.key -out dashboard.csr -subj /C=CN/ST=BEIJING/L=BEIJING/O=JD/OU=JD/CN=172.16.16.42
$ openssl x509 -req -days 3650 -in dashboard.csr -signkey dashboard.key -out dashboard.crt

12)执行以下操作命令

# 去除主节点的污点
$ kubectl taint nodes --all node-role.kubernetes.io/master-
# 创建命名空间
$ kubectl create namespace kubernetes-dashboard
# 创建Secret
$ kubectl create secret tls kubernetes-dashboard-certs -n kubernetes-dashboard --key dashboard.key \
--cert dashboard.crt

13)生效dashboard yml配置文件

$ kubectl apply -f /export/servers/kubernetes/dashboard.yml
# 查看pods状态
$ kubectl get pods -A | grep kubernetes-dashboard
kubernetes-dashboard   dashboard-metrics-scraper-c45b7869d-rbdt4   1/1     Running   0               15m
kubernetes-dashboard   kubernetes-dashboard-764b4dd7-rt66t         1/1     Running   0               15m

14)访问dashboard页面

# web浏览器访问地址:IP地址为集群任意节点(可以是LB地址)https://192.168.186.121:31001/#/login

15)制作访问token

# 新增配置文件 dashboard-adminuser.yaml
$ touch /export/servers/kubernetes/dashboard-adminuser.yaml && vim /export/servers/kubernetes/dashboard-adminuser.yaml
# 输入以下内容
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: admin-user
  namespace: kubernetes-dashboard
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: admin-user
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: cluster-admin
subjects:
- kind: ServiceAccount
  name: admin-user
  namespace: kubernetes-dashboard
  
# 执行yaml文件
$ kubectl create -f /export/servers/kubernetes/dashboard-adminuser.yaml
# 预期输出结果
serviceaccount/admin-user created
clusterrolebinding.rbac.authorization.k8s.io/admin-user created
# 说明:上面创建了一个叫admin-user的服务账号,并放在kubernetes-dashboard命名空间下,并将cluster-admin角色绑定到admin-user账户,这样admin-user账户就有了管理员的权限。默认情况下,kubeadm创建集群时已经创建了cluster-admin角色,直接绑定即可

# 查看admin-user账户的token
$ kubectl -n kubernetes-dashboard describe secret $(kubectl -n kubernetes-dashboard get secret | grep admin-user | awk '{print $1}')
# 预期输出结果
Name:         admin-user-token-9fpps
Namespace:    kubernetes-dashboard
Labels:       <none>
Annotations:  kubernetes.io/service-account.name: admin-user
              kubernetes.io/service-account.uid: 72c1aa28-6385-4d1a-b22c-42427b74b4c7

Type:  kubernetes.io/service-account-token

Data
====
ca.crt:     1099 bytes
namespace:  20 bytes
token:      eyJhbGciOiJSUzI1NiIsImtpZCI6IjFEckU0NXB5Yno5UV9MUFkxSUpPenJhcTFuektHazM1c2QzTGFmRzNES0EifQ.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.oA3NLhhTaXd2qvWrPDXat2w9ywdWi_77SINk4vWkfIIzMmxBEHnqvDIBvhRC3frIokNSvT71y6mXN0KHu32hBa1YWi0MuzF165ZNFtM_rSQiq9OnPxeFvLaKS-0Vzr2nWuBx_-fTt7gESReSMLEJStbPb1wOnR6kqtY66ajKK5ILeIQ77I0KXYIi7GlPEyc6q4bIjweZ0HSXDPR4JSnEAhrP8Qslrv3Oft4QZVNj47x7xKC4dyyZOMHUIj9QhkpI2gMbiZ8XDUmNok070yDc0TCxeTZKDuvdsigxCMQx6AesD-8dca5Hb8Sm4mEPkGJekvMzkLkM97y_pOBPkfTAIA

# 把上面命令执行获取到的Token复制到登录界面的Token输入框中,即可正常登录dashboard

13)登录dashboard如下





kubectl安装

1. 环境说明

名称说明
操作系统CentOS Linux release 7.8.2003
kubectlkubectl-1.22.4-0.x86_64
节点deploy

2. 部署说明

Kubernetes kubectl客户端。

3. 解压之前上传的kubadm-rpm包

$ tar xzvf kubeadm-rpm.tgz 

4. 执行安装

$ rpm -ivh bc7a9f8e7c6844cfeab2066a84b8fecf8cf608581e56f6f96f80211250f9a5e7-kubectl-1.22.4-0.x86_64.rpm

5. 增加执行权限

# 生成kubelet环境配置文件
$ mkdir -p $HOME/.kube
$ sudo touch $HOME/.kube/config
$ sudo chown $(id -u):$(id -g) $HOME/.kube/config
# 从任意master节点复制内容至上面的配置文件

6. 查看版本

$ kubectl version
Client Version: version.Info{Major:"1", Minor:"22", GitVersion:"v1.22.4", GitCommit:"b695d79d4f967c403a96986f1750a35eb75e75f1", GitTreeState:"clean", BuildDate:"2021-11-17T15:48:33Z", GoVersion:"go1.16.10", Compiler:"gc", Platform:"linux/amd64"}
Server Version: version.Info{Major:"1", Minor:"22", GitVersion:"v1.22.4", GitCommit:"b695d79d4f967c403a96986f1750a35eb75e75f1", GitTreeState:"clean", BuildDate:"2021-11-17T15:42:41Z", GoVersion:"go1.16.10", Compiler:"gc", Platform:"linux/amd64"}

helm安装

1. 环境说明

名称说明
操作系统CentOS Linux release 7.8.2003
helmhelm-v3.9.3-linux-amd64.tar.gz
节点deploy

2. 部署说明

Kubernetes资源包及配置管理工具。

3. 下载helm离线安装包并上传至服务器

$ wget https://get.helm.sh/helm-v3.9.3-linux-amd64.tar.gz

4. 解压安装包

$ tar -zxvf helm-v3.9.3-linux-amd64.tar.gz -C /export/servers/
$ cd /export/servers/linux-amd64

5. 增加执行权限

$ cp linux-amd64/helm /usr/local/bin/
$ chmod +x /usr/local/bin/helm

6. 查看版本

$ helm version
version.BuildInfo{Version:"v3.9.3", GitCommit:"414ff28d4029ae8c8b05d62aa06c7fe3dee2bc58", GitTreeState:"clean", GoVersion:"go1.17.13"}

设置本地存储挂载nas

$ mkdir /export/servers/helm_chart/local-path-storage && cd /export/servers/helm_chart/local-path-storage/local-path-storage.yaml
$ vim local-path-storage.yaml
# 设置以下内容,设置"paths":["/home/admin/local-path-provisioner"] 为nas目录,没有目录需要创建
apiVersion: v1
kind: Namespace
metadata:
  name: local-path-storage
 
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: local-path-provisioner-service-account
  namespace: local-path-storage
 
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: local-path-provisioner-role
rules:
  - apiGroups: [ "" ]
    resources: [ "nodes", "persistentvolumeclaims", "configmaps" ]
    verbs: [ "get", "list", "watch" ]
  - apiGroups: [ "" ]
    resources: [ "endpoints", "persistentvolumes", "pods" ]
    verbs: [ "*" ]
  - apiGroups: [ "" ]
    resources: [ "events" ]
    verbs: [ "create", "patch" ]
  - apiGroups: [ "storage.k8s.io" ]
    resources: [ "storageclasses" ]
    verbs: [ "get", "list", "watch" ]
 
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: local-path-provisioner-bind
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: local-path-provisioner-role
subjects:
  - kind: ServiceAccount
    name: local-path-provisioner-service-account
    namespace: local-path-storage
 
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: local-path-provisioner
  namespace: local-path-storage
spec:
  replicas: 1
  selector:
    matchLabels:
      app: local-path-provisioner
  template:
    metadata:
      labels:
        app: local-path-provisioner
    spec:
      serviceAccountName: local-path-provisioner-service-account
      containers:
        - name: local-path-provisioner
          image: rancher/local-path-provisioner:v0.0.21
          imagePullPolicy: IfNotPresent
          command:
            - local-path-provisioner
            - --debug
            - start
            - --config
            - /etc/config/config.json
          volumeMounts:
            - name: config-volume
              mountPath: /etc/config/
          env:
            - name: POD_NAMESPACE
              valueFrom:
                fieldRef:
                  fieldPath: metadata.namespace
      volumes:
        - name: config-volume
          configMap:
            name: local-path-config
 
---
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: local-path
provisioner: rancher.io/local-path
volumeBindingMode: WaitForFirstConsumer
reclaimPolicy: Delete
 
---
kind: ConfigMap
apiVersion: v1
metadata:
  name: local-path-config
  namespace: local-path-storage
data:
  config.json: |-
    {
            "nodePathMap":[
            {
                    "node":"DEFAULT_PATH_FOR_NON_LISTED_NODES",
                    "paths":["/nas_data/jdiot/local-path-provisioner"] 
            }
            ]
    }
  setup: |-
    #!/bin/sh
    while getopts "m:s:p:" opt
    do
        case $opt in
            p)
            absolutePath=$OPTARG
            ;;
            s)
            sizeInBytes=$OPTARG
            ;;
            m)
            volMode=$OPTARG
            ;;
        esac
    done
 
    mkdir -m 0777 -p ${absolutePath}
  teardown: |-
    #!/bin/sh
    while getopts "m:s:p:" opt
    do
        case $opt in
            p)
            absolutePath=$OPTARG
            ;;
            s)
            sizeInBytes=$OPTARG
            ;;
            m)
            volMode=$OPTARG
            ;;
        esac
    done
 
    rm -rf ${absolutePath}
  helperPod.yaml: |-
    apiVersion: v1
    kind: Pod
    metadata:
      name: helper-pod
    spec:
      containers:
      - name: helper-pod
        image: busybox

注:以上依赖镜像需要从公网环境下载依赖并导入镜像库,需要设置以上对应镜像地址从私有镜像库拉取镜像

生效本地存储yaml

$ kubectl apply -f local-path-storage.yaml -n local-path-storage

设置k8s默认存储

$ kubectl patch storageclass local-path  -p '{"metadata": {"annotations":{"storageclass.kubernetes.io/is-default-class":"true"}}}'

注:后面部署的中间件及服务需要修改对应的存储为本地存储:"storageClass": "local-path"

 

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京东科技开发者 294

ICML 2026|NaviAgent:面向 Oxygen 智能体的可扩展工具编排

大型语言模型(LLM)越来越多地作为函数调用代理,调用外部工具来处理超出其静态知识范围的任务。然而,它们通常一次只调用一个工具,缺乏对任务结构的全局视图。由于工具之间往往相互依赖,这会导致错误累积和可扩展性差,尤其是在扩展到数百或数千个工具时。为了解决这些局限性,我们提出了NaviAgent,一种显式的双层架构,它通过基于图的工具关系建模将任务规划与工具执行解耦。在规划层,基于 LLM 的代理决定是直接响应、澄清意图,还是检索并执行一个独立于工具间复杂性的工具链。

京东科技开发者 383

实测 9 款 AI 架构图工具:从 Mermaid 美化到 GPT-Image2,一份选型清单

支持 5 类图(架构图、工作流图、时序图、数据流图、生命周期/状态机图),主题 4 种(Classic、Signal Flow、Blueprint、Editorial)+ 3 种色彩模式(Dark / Light / System Auto),输出格式含 HTML、PNG、SVG、WebM、Share Card PNG、JSON IR。它把提示词图库、图像生成案例、参考图编辑流程和命令行工具整合在一起,让 AI Agent 能直接调用 GPT Image 2 模型完成文生图、图生图、参考图编辑等任务。

京东科技开发者 895

别再守着 Claude Code 了——学会指挥它自主干活

回到开头那句:别再一句一句地喂它、然后守着屏幕。真正的用法是——把一件事想清楚、划好边界、给它一个能自我验证的目标,然后交出去。你会发现,省下来的时间不是一点半点。把重复的判断和执行还给机器,把真正需要你的判断留给自己。

京东科技开发者 584

CVPR 2026|Oxygen XR 团队在原生鱼眼高斯泼溅技术上带来新突破

京东零售技术团队联合清华大学提出DirectFisheye-GS框架,创新性地将鱼眼相机模型原生嵌入3D高斯泼溅管线,通过跨视角联合优化策略直接处理鱼眼图像,避免了传统去畸变预处理导致的信息损失。该方案在多个公开数据集上达到SOTA性能,有效解决了单视角优化引起的几何畸变和视角不一致问题,同时保持与现有3DGS工具链的兼容性。技术已应用于电商3D重建和4D体积视频等场景,显著提升了沉浸式体验。相关论文被CVPR 2026接收,为宽视场场景重建提供了高效解决方案。

京东科技开发者 353

拆解海博 AI-Native 落地保障:Harness、双 Loop、知识库与技能自主迭代实践

海博研发RoadMap 的落地,彻底终结了 AI 编程“对话式玄学”的旧模式,真正落地了AI Native 研发工程化。它证明 AI 规模化赋能研发的核心,不在于堆砌大模型能力,而在于用工程思维重构 AI 的工作范式:用结构化流程替代随机对话,用固化规则约束模型行为,用持续沉淀实现能力复利,用分层架构支撑规模化落地。

京东科技开发者 633

让 AI 快速「读懂」你的代码仓:Joy-Code-Graph 云端图谱服务的三次进化

代码知识图谱不是要取代 AI 的智能,而是要补齐它对代码全局关系的认知盲区。当 AI 能一眼看清「谁调用了谁、改动会波及哪里」,它写出的代码才真正靠谱;当这份认知安全地留在自己手里、随时被 Joygen 调用、并且永久沉淀在代码仓里——AI 编程的体验,才算真正上了一个台阶。这,就是我们做 Joy-Code-Graph 云端服务的初衷。能力共享,数据自主;一次建图,长期受用。

京东科技开发者 751

【Coding生态】从代码托管到 AI 能力底座:与Coding一起共建 AI 研发生态

前两种框架能够很好地支撑对话式能力和预设流程式能力。它们适合基于提示词或预设工作流完成研发问答、知识查询、流程编排等任务。但在更复杂的研发场景中,仅仅“回答问题”还不够。当用户业务逻辑复杂时,一旦场景进入真实落地阶段,例如下载代码、修改文件、执行命令、安装依赖、运行测试、读取完整代码文件,或者与自有系统联动、将结果渲染到 Coding 页面、推送到京 ME,AI 应用需要的不只是知识能力,还需要真实的代码库操作能力和独立可执行环境。

京东科技开发者 768

GPT-5.6、ChatGPT Work 与 Codex 更新食用指南

三档模型当前都提供 1,050,000 tokens 上下文、922,000 tokens 最大输入和 128,000 tokens 最大输出,接受文本与图像输入、输出文本,并支持 web search、file search、Computer Use、MCP、Tool Search、Skills、Apply Patch 等工具。Work 可以读取你提供的文件,通过插件连接 Slack、Google Drive、SharePoint、邮箱、日历、CRM 或项目系统,调用获批工具,并在执行中展示进度。

京东科技开发者 860

把业务流程沉淀成高质量 Skill 的实践路径

使用Record & Replay创建的Skill,尽管创建流程简单,但它的本质实际是让 AI 去操作页面,这不但有可能影响我们对电脑的操作,还有可能无法稳定复现。创建Skill时,可以使用 Codex 的 Record & Replay 插件,把一次手工操作录下来,再让 AI 生成可复用流程。刚开始切换时,很容易遇到接口报错,可以让Codex排查原因进行优化,直到可以稳定执行;补充相应业务背景,说明这件事为什么要做,谁会用,输入从哪来,输出给谁看。电脑休眠、关机、软件没开时,本地任务可能无法执行。

京东科技开发者 835

政企前台AI-Agent的架构实践:打造可复用的 AI 服务平台-【灯塔计划】

不止是爆火的龙虾,还是各种Skills,MCP、Agent,脱离的具体的场景谈方案,最后只会是一场空。这一套可复用的 AI 服务平台,截止目前,通过前后端对接规范文档,多项能力已快速接入到各个场域的不同端平台,大幅降低研发成本, 并且各业务线新场景的接入从原来的 “开发驱动” 转为 “配置驱动”,接入周期从天维度缩短至小时级。

京东科技开发者 860

京东 Oxygen xLLM 大模型推理引擎正式捐赠开放原子开源基金会,共建国产 AI Infra 生态

下一阶段 AI 基础设施的核心命题,不是简单的算力堆叠,而是让工程本身变得智能——让调度系统能自主感知负载特征并动态优化,让推理引擎能根据模型结构和硬件特性自动生成最优执行方案,让整个 AI 工程链路具备自感知、自决策、自优化的能力。未来,Oxygen xLLM 的项目官网、社区沟通渠道及微信社区将陆续向开发者开放,诚邀芯片厂商、模型厂商、云厂商、企业用户及个人开发者共同参与,让国产大模型推理引擎在开放生态中加速成长。硬件层,原生支持 GPU / NPU / MLU 多类 AI 芯片,深度适配国产芯片。

京东科技开发者 1000

【京东云】全球首个!京东全栈开源JoyAI-VL-Interaction,让大模型从“一问一答”走向“边看边说”

开源一套系统,而不只是一个模型在实时视频流中,JoyAI-VL-Interaction每秒都会做一次判断,比如,继续观察、保持沉默,发现关键事件、主动回应,遇到复杂任务,交给后台Agent处理。一个好的AI 助手,不应该一直打扰用户,而应该知道什么时候该出现,什么时候该安静,以及什么时候自己解决,什么时候交由agent解决。深耕零售、物流、健康、工业等实体产业二十余年,京东拥有全球领先的物理世界运营网络,覆盖仓储、配送、门店、直播、客服、售后海量真实场景,每天都在发生人、货、场的实时互动。

京东科技开发者 1029
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