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[TOC]

一、环境准备

1.1 硬件设备环境

采用5台腾讯云的CVM作为kubernetes的部署环境，具体信息如下：

主机名 IP 配置 备注
（Old）VM_0_1_centos；（New）tf-k8s-m1 10.0.0.1 4c 8g k8s的master，同时也是etcd节点
（Old）VM_0_2_centos；（New）tf-k8s-m2 10.0.0.2 4c 8g k8s的master，同时也是etcd节点
（Old）VM_0_3_centos；（New）tf-k8s-m3 10.0.0.3 4c 8g k8s的master，同时也是etcd节点
（Old）VM_0_4_centos；（New）tf-k8s-n1 10.0.0.4 4c 8g 工作节点 node，容器编排最终 pod 工作节点
（Old）VM_0_5_centos；（New）tf-k8s-n2 10.0.0.5 4c 8g 工作节点 node，容器编排最终 pod 工作节点

1.2 软件环境

环境 简介
操作系统 CentOS7
kubeadm 1.13.3
kubernetes 1.13.3
Docker docker-ce 18.06.2

1.3 相关系统设置

在正式安装之前，需要在每台机器上对以下配置进行修改：

  • 关闭防火墙，selinux
  • 关闭系统的swap功能
  • 关闭Linux swap空间的swappiness
  • 配置L2网桥在转发包时会被iptables的FORWARD规则所过滤，该配置被CNI插件需要，更多信息请参考Network Plugin Requirements
  • 升级内核到最新（centos7 默认的内核是3.10.0-862.el7.x86_64 ,可以使用命令‘uname -a’进行查看），原因见请问下为什么要用4.18版本内核
  • 开启IPVS
  • 修改主机名（如果主机名中含有一些特殊字符，则需要调整主机名，不然在后续操作中会出现错误）
    具体的配置修改执行脚本如下：
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# ---------- 关闭防火墙和selinux -----------
systemctl stop firewalld
systemctl disable firewalld
setenforce 0
sed -i "s/SELINUX=enforcing/SELINUX=disabled/g" /etc/selinux/config

# ---------- 关闭交换分区 -----------
swapoff -a
yes | cp /etc/fstab /etc/fstab_bak
cat /etc/fstab_bak |grep -v swap > /etc/fstab

# ---------- 设置网桥包经IPTables，core文件生成路径 -----------
echo """
vm.swappiness = 0
net.bridge.bridge-nf-call-ip6tables = 1
net.bridge.bridge-nf-call-iptables = 1
""" > /etc/sysctl.conf
modprobe br_netfilter
sysctl -p

# ---------- 同步时间 -----------
yum install -y ntpdate
ntpdate -u ntp.api.bz

# ---------- 升级内核 -----------
rpm -Uvh http://www.elrepo.org/elrepo-release-7.0-2.el7.elrepo.noarch.rpm ;yum --enablerepo=elrepo-kernel install kernel-ml-devel kernel-ml -y
# 查看启动配置里是否有最新的内核
cat /boot/grub2/grub.cfg | grep menuentry
# 修改默认启动项
grub2-set-default 0
# 检查默认内核版本是否大于4.14，否则请调整默认启动参数
grub2-editenv list
#重启以更换内核
reboot
#查看内核信息
uname -a

# ---------- 确认内核版本后，开启IPVS -----------
uname -a
cat > /etc/sysconfig/modules/ipvs.modules <<EOF
#!/bin/bash
ipvs_modules="ip_vs ip_vs_lc ip_vs_wlc ip_vs_rr ip_vs_wrr ip_vs_lblc ip_vs_lblcr ip_vs_dh ip_vs_sh ip_vs_fo ip_vs_nq ip_vs_sed ip_vs_ftp nf_conntrack"
for kernel_module in \${ipvs_modules}; do
 /sbin/modinfo -F filename \${kernel_module} > /dev/null 2>&1
 if [ $? -eq 0 ]; then
 /sbin/modprobe \${kernel_module}
 fi
done
EOF
chmod 755 /etc/sysconfig/modules/ipvs.modules && bash /etc/sysconfig/modules/ipvs.modules && lsmod | grep ip_vs

# ---------- 修改主机名 -----------
# 这里以VM_0_17_centos主机为例，其他的主机分别修改成相应的主机名
hostnamectl set-hostname tf-k8s-m1

1.3 配置集群内各个机器之间的免密码登录

1.3.1 配置hosts

为了便于后续的操作，我们需要给每一台设备配置下hosts域名信息，具体如下：

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# vi /etc/hosts
10.0.0.1 tf-k8s-m1 api.tf-k8s.xiangwushuo.com
10.0.0.2 tf-k8s-m2
10.0.0.3 tf-k8s-m3
10.0.0.4 tf-k8s-n1
10.0.0.5 tf-k8s-n2
10.0.0.1 dashboard.tf-k8s.xiangwushuo.com

1.3.2 新建用户

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# useradd kube
# visudo
%wheel  ALL=(ALL)       ALL
kube    ALL=(ALL)       NOPASSWD:ALL

备注：visudo命令是用来给kube用户添加sudo密码

1.3.3 设置免密登录

  1. 各个设备的root用户&kube用户（不同用户配置不同的）都生成各自的免密登录的ssh的私钥与公钥
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## 为root用户生成ssh的私钥与公钥
ssh-keygen

在/root目录下，会生成一个.ssh目录，.ssh目录下会生成以下三个文件：

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-rw------- 1 root root 2398 Feb 13 15:18 authorized_keys
-rw------- 1 root root 1679 Feb 13 14:47 id_rsa
-rw-r--r-- 1 root root  401 Feb 13 14:47 id_rsa.pub

authorized_keys文件存储了本设备认证授权的其他设备的公钥信息；id_rsa存储了本设备的私钥信息；id_rsa.pub存储了本设备的公钥信息。

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## 为kube用户生成ssh的私钥与公钥
su kube
ssh-keygen

在/home/kube目录下，会生成一个.ssh目录，并包含相关文件。

  1. 各个设备上都创建好各自的ssh免密登录公钥与私钥后，需要将各自的公钥copy至其他的设备上，并将公钥信息添加到各个设备的authorized_keys文件中。
    备注：也需要将各个节点自己的公钥copy至自己的authorized_keys文件中，这样自己才可以ssh自己。
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## 将每一台节点上的公钥都同步到相应的目录下
# ll
-rw-r--r-- 1 root root  401 Feb 13 15:15 tf-k8s-m1-id_rsa.pub
-rw-r--r-- 1 root root  401 Feb 13 15:15 tf-k8s-m2-id_rsa.pub
-rw-r--r-- 1 root root  401 Feb 13 15:15 tf-k8s-m3-id_rsa.pub
-rw-r--r-- 1 root root  401 Feb 13 15:15 tf-k8s-n1-id_rsa.pub
-rw-r--r-- 1 root root  401 Feb 13 15:15 tf-k8s-n2-id_rsa.pub
## 将每台节点的公钥追加至authorized_keys文件中
cat tf-k8s-m1-id_rsa.pub > authorized_keys
cat tf-k8s-m2-id_rsa.pub > authorized_keys
cat tf-k8s-m3-id_rsa.pub > authorized_keys
cat tf-k8s-n1-id_rsa.pub > authorized_keys
cat tf-k8s-n2-id_rsa.pub > authorized_keys

测试是否能够正常使用ssh免密登录

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ssh root@tf-k8s-m1
ssh root@tf-k8s-m2
ssh root@tf-k8s-m3
ssh root@tf-k8s-n1
ssh root@tf-k8s-n2

提示：如果其他机器上的 root 下的 /root/.ssh/authorized_keys 不存在，可以手动创建。要注意的是：authorized_keys 的权限需要是 600。

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## 如果 authorized_keys 的权限不是 600，执行修改权限的命令。
chmod 600 authorized_keys

二、安装步骤

以下操作，可以都切换至kube用户下进行操作。

2.1 安装docker

由于kubeadm的ha模式对docker的版本是有一定的要求的，因此，本教程中安装官方推荐的docker版本。

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# 安装依赖包
yum install -y yum-utils device-mapper-persistent-data lvm2

# 添加Docker软件包源
yum-config-manager --add-repo https://download.docker.com/linux/centos/docker-ce.repo

#关闭测试版本list（只显示稳定版）
sudo yum-config-manager --enable docker-ce-edge
sudo yum-config-manager --enable docker-ce-test

# 更新yum包索引
yum makecache fast

#NO.1 指定版本安装
yum list docker-ce --showduplicates|sort -r  
yum install docker-ce-18.06.2.ce -y

为了方便操作，我们在tf-k8s-m1节点上，创建一个批量部署docker的脚本。

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## 创建install.docker.sh

#!/bin/sh

vhosts="tf-k8s-m1 tf-k8s-m2 tf-k8s-m3 tf-k8s-n1 tf-k8s-n2"

for h in $vhosts
do
    echo "Install docker for $h"
    ssh kube@$h "sudo yum install docker-ce-18.06.2.ce -y && sudo systemctl enable docker && systemctl start docker"
done

执行install.docker.sh脚本

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chmod a+x install.docker.sh
sh ./install.docker.sh

2.2 安装kubernetes yum源和kubelet、kubeadm、kubectl组件

2.2.1 所有机器上配置 kubernetes.repo yum 源

详细的安装脚本如下：

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## 创建脚本：install.k8s.repo.sh

#!/bin/sh

vhost="tf-k8s-m1 tf-k8s-m2 tf-k8s-m3 tf-k8s-n1 tf-k8s-n2"

## 设置为阿里云 kubernetes 仓库
cat <<EOF > kubernetes.repo
[kubernetes]
name=Kubernetes
baseurl=https://mirrors.aliyun.com/kubernetes/yum/repos/kubernetes-el7-x86_64/
enabled=1
gpgcheck=1
repo_gpgcheck=1
gpgkey=https://mirrors.aliyun.com/kubernetes/yum/doc/yum-key.gpg https://mirrors.aliyun.com/kubernetes/yum/doc/rpm-package-key.gpg
EOF

mvCmd="sudo cp ~/kubernetes.repo /etc/yum.repos.d/"
for h in $vhost
do
  echo "Setup kubernetes repository for $h"
  scp ./kubernetes.repo kube@$h:~
  ssh kube@$h $mvCmd
done

执行install.k8s.repo.sh脚本

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chmod a+x install.k8s.repo.sh
sh ./install.k8s.repo.sh

2.2.2 所有机器上安装 kubelet、kubeadm、kubectl组件

详细安装脚本如下：

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## 创建脚本：install.k8s.basic.sh

#!/bin/sh

vhost="tf-k8s-m1 tf-k8s-m2 tf-k8s-m3 tf-k8s-n1 tf-k8s-n2"

## 安装 kubelet kubeadm kubectl
installCmd="sudo yum install -y kubelet kubeadm kubectl && sudo systemctl enable kubelet"
for h in $vhost
do
  echo "Install kubelet kubeadm kubectl for : $h"
  ssh kube@$h $installCmd
done

执行install.k8s.baisc.sh脚本

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chmod a+x install.k8s.basic.sh
sh ./install.k8s.basic.sh

2.3 初始化kubeadm配置文件

创建三台master机器tf-k8s-m1,tf-k8s-m2,tf-k8s-m3的kubeadm配置文件，其中主要是配置生成证书的域配置、etcd集群配置。

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## 创建脚本：init.kubeadm.config.sh

#!/bin/sh

## 1. 配置参数 
## vhost 主机名和 vhostIP IP 一一对应
vhost=(tf-k8s-m1 tf-k8s-m2 tf-k8s-m3)
vhostIP=(10.0.0.1 10.0.0.2 10.0.0.3)

domain=api.tf-k8s.xiangwushuo.com

## etcd 初始化 m01 m02 m03 集群配置
etcdInitCluster=(
tf-k8s-m1=https://10.0.0.1:2380
tf-k8s-m1=https://10.0.0.1:2380,tf-k8s-m2=https://10.0.0.2:2380
tf-k8s-m1=https://10.0.0.1:2380,tf-k8s-m2=https://10.0.0.2:2380,tf-k8s-m3=https://10.0.0.3:2380
)

## etcd 初始化时，m01 m02 m03 分别的初始化集群状态
initClusterStatus=(
new
existing
existing
)


## 2.遍历 master 主机名和对应 IP
## 生成对应的 kubeadmn 配置文件 
for i in `seq 0 $((${#vhost[*]}-1))`
do

h=${vhost[${i}]} 
ip=${vhostIP[${i}]}

echo "--> $h - $ip"
  
## 生成 kubeadm 配置模板
cat <<EOF > kubeadm-config.$h.yaml
apiVersion: kubeadm.k8s.io/v1beta1
kind: InitConfiguration
localAPIEndpoint:
  advertiseAddress: $ip
  bindPort: 6443
---
apiVersion: kubeadm.k8s.io/v1beta1
kind: ClusterConfiguration
kubernetesVersion: v1.13.3

# 指定阿里云镜像仓库
imageRepository: registry.aliyuncs.com/google_containers

# apiServerCertSANs 填所有的 masterip、lbip、其它可能需要通过它访问 apiserver 的地址、域名或主机名等，
# 如阿里fip，证书中会允许这些ip
# 这里填一个自定义的域名
apiServer:
  certSANs:
  - "$domain"
controlPlaneEndpoint: "$domain:6443"

## Etcd 配置
etcd:
  local:
    extraArgs:
      listen-client-urls: "https://127.0.0.1:2379,https://$ip:2379"
      advertise-client-urls: "https://$ip:2379"
      listen-peer-urls: "https://$ip:2380"
      initial-advertise-peer-urls: "https://$ip:2380"
      initial-cluster: "${etcdInitCluster[${i}]}"
      initial-cluster-state: ${initClusterStatus[${i}]}
    serverCertSANs:
      - $h
      - $ip
    peerCertSANs:
      - $h
      - $ip
networking:
  podSubnet: "10.244.0.0/16"

EOF

echo "kubeadm-config.$h.yaml created ... ok"

## 3. 分发到其他 master 机器 
scp kubeadm-config.$h.yaml kube@$h:~
echo "scp kubeadm-config.$h.yaml ... ok"

done

执行init.kubeadm.config.sh脚本

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chmod a+x init.kubeadm.config.sh
sh ./init.kubeadm.config.sh

执行成功之后，可以在tf-k8s-m1, tf-k8s-m2, tf-k8s-m3的 kube 用户的 home 目录（/home/kube）能看到对应的 kubeadm-config.tf-k8s-m1*.yaml 配置文件。 这个配置文件主要是用于后续初始化集群其他 master 的证书、 etcd 配置、kubelet 配置、kube-apiserver配置、kube-controller-manager 配置等。
各个master节点上对应的kubeadm配置文件：

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cvm tf-k8s-m1：kubeadm-config.tf-k8s-m1.yaml
cvm tf-k8s-m2：kubeadm-config.tf-k8s-m2.yaml
cvm tf-k8s-m3：kubeadm-config.tf-k8s-m3.yaml

2.4 安装master镜像和执行kubeadm初始化

2.4.1 拉取镜像到本地

因为 k8s.gcr.io 国内无法访问，我们可以选择通过阿里云的镜像仓库（kubeadm-config.tf-k8s-m1*.yaml 配置文件中已经指定使用阿里云镜像仓库 registry.aliyuncs.com/google_containers），将所需的镜像 pull 到本地。
我们可以通过以下命令，来查看是否已经成功指定了阿里云的镜像仓库,在tf-k8s-m1机器上，通过kubeadm config images list命令来查看，结果如下:

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[kube@tf-k8s-m1 ~]$ kubeadm config images list --config kubeadm-config.tf-k8s-m1.yaml
registry.aliyuncs.com/google_containers/kube-apiserver:v1.13.3
registry.aliyuncs.com/google_containers/kube-controller-manager:v1.13.3
registry.aliyuncs.com/google_containers/kube-scheduler:v1.13.3
registry.aliyuncs.com/google_containers/kube-proxy:v1.13.3
registry.aliyuncs.com/google_containers/pause:3.1
registry.aliyuncs.com/google_containers/etcd:3.2.24
registry.aliyuncs.com/google_containers/coredns:1.2.6

接下来，分别在tf-k8s-m1、tf-k8s-m2、tf-k8s-m3机器上，拉取相关镜像

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[kube@tf-k8s-m1 ~]$ sudo kubeadm config images pull --config kubeadm-config.tf-k8s-m1.yaml
[kube@tf-k8s-m2 ~]$ sudo kubeadm config images pull --config kubeadm-config.tf-k8s-m2.yaml
[kube@tf-k8s-m3 ~]$ sudo kubeadm config images pull --config kubeadm-config.tf-k8s-m3.yaml

执行成功后，应该能够看到本地已经拉取的镜像

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[kube@tf-k8s-m1 ~]$ sudo docker images
REPOSITORY                                                                     TAG                 IMAGE ID            CREATED             SIZE
registry.aliyuncs.com/google_containers/kube-apiserver                         v1.13.3             fe242e556a99        2 weeks ago         181MB
registry.aliyuncs.com/google_containers/kube-proxy                             v1.13.3             98db19758ad4        2 weeks ago         80.3MB
registry.aliyuncs.com/google_containers/kube-controller-manager                v1.13.3             0482f6400933        2 weeks ago         146MB
registry.aliyuncs.com/google_containers/kube-scheduler                         v1.13.3             3a6f709e97a0        2 weeks ago         79.6MB
quay.io/coreos/flannel                                                         v0.11.0-amd64       ff281650a721        2 weeks ago         52.6MB
registry.cn-hangzhou.aliyuncs.com/google_containers/nginx-ingress-controller   0.21.0              01bd760f276c        2 months ago        568MB
registry.aliyuncs.com/google_containers/coredns                                1.2.6               f59dcacceff4        3 months ago        40MB
registry.aliyuncs.com/google_containers/etcd                                   3.2.24              3cab8e1b9802        5 months ago        220MB
registry.aliyuncs.com/google_containers/pause                                  3.1                 da86e6ba6ca1        14 months ago       742kB

2.4.2 安装master tf-k8s-m1

我们目标是要搭建一个高可用的 master 集群，所以需要在三台 master tf-k8s-m1 tf-k8s-m2 tf-k8s-m3机器上分别通过 kubeadm 进行初始化。
由于 tf-k8s-m2 和 tf-k8s-m3 的初始化需要依赖 tf-k8s-m1 初始化成功后所生成的证书文件，所以这里需要先在 m01 初始化。

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[kube@tf-k8s-m1 ~]$  sudo kubeadm init --config kubeadm-config.tf-k8s-m1.yaml

初始化成功后，会看到如下日志：
备注：如果初始化失败，则可以通过kubeadm reset --force命令重置之前kubeadm init命令的执行结果，恢复一个干净的环境

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[init] Using Kubernetes version: v1.13.3
[preflight] Running pre-flight checks
[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'
[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] Activating the kubelet service
[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 [m01 kubernetes kubernetes.default kubernetes.default.svc kubernetes.default.svc.cluster.local api.k8s.hiko.im api.k8s.hiko.im] and IPs [10.96.0.1 10.0.2.15]
[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 [m01 localhost m01] and IPs [10.0.2.15 127.0.0.1 ::1 192.168.33.10]
[certs] Generating "etcd/peer" certificate and key
[certs] etcd/peer serving cert is signed for DNS names [m01 localhost m01] and IPs [10.0.2.15 127.0.0.1 ::1 192.168.33.10]
[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
[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 19.009523 seconds
[uploadconfig] storing the configuration used in ConfigMap "kubeadm-config" in the "kube-system" Namespace
[kubelet] Creating a ConfigMap "kubelet-config-1.13" in namespace kube-system with the configuration for the kubelets in the cluster
[patchnode] Uploading the CRI Socket information "/var/run/dockershim.sock" to the Node API object "m01" as an annotation
[mark-control-plane] Marking the node m01 as control-plane by adding the label "node-role.kubernetes.io/master=''"
[mark-control-plane] Marking the node m01 as control-plane by adding the taints [node-role.kubernetes.io/master:NoSchedule]
[bootstrap-token] Using token: a1t7c1.mzltpc72dc3wzj9y
[bootstrap-token] Configuring bootstrap tokens, cluster-info ConfigMap, RBAC Roles
[bootstraptoken] configured RBAC rules to allow Node Bootstrap tokens to post CSRs in order for nodes to get long term certificate credentials
[bootstraptoken] configured RBAC rules to allow the csrapprover controller automatically approve CSRs from a Node Bootstrap Token
[bootstraptoken] configured RBAC rules to allow certificate rotation for all node client certificates in the cluster
[bootstraptoken] creating the "cluster-info" ConfigMap in the "kube-public" namespace
[addons] Applied essential addon: CoreDNS
[addons] Applied essential addon: kube-proxy

Your Kubernetes master 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

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 machines by running the following on each node
as root:

  kubeadm join api.k8s.hiko.im:6443 --token a1t7c1.mzltpc72dc3wzj9y --discovery-token-ca-cert-hash sha256:05f44b111174613055975f012fc11fe09bdcd746bd7b3c8d99060c52619f8738

至此，就完成了第一台master的初始化工作。

2.4.3 kube用户配置

为了让tf-k8s-m1的 kube 用户能通过 kubectl 管理集群，接着我们需要给tf-k8s-m1 的 kube 用户配置管理集群的配置。在tf-k8s-m1机器上创建config.using.cluster.sh脚本，具体如下：

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## 创建脚本：config.using.cluster.sh

#!/bin/sh

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

执行config.using.cluster.sh脚本

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chmod a+x config.using.cluster.sh
sh ./config.using.cluster.sh

验证结果，通过kubectl命令查看集群状态，结果如下：

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[kube@tf-k8s-m1 ~]$ kubectl cluster-info
Kubernetes master is running at https://api.tf-k8s.xiangwushuo.com:6443
KubeDNS is running at https://api.tf-k8s.xiangwushuo.com:6443/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy

To further debug and diagnose cluster problems, use 'kubectl cluster-info dump'.

查看集群所有的pods信息，结果如下：

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[kube@tf-k8s-m1 ~]$ kubectl get pods --all-namespaces

NAMESPACE     NAME                          READY   STATUS    RESTARTS   AGE
kube-system   coredns-78d4cf999f-cw79l      0/1     Pending   0          47m
kube-system   coredns-78d4cf999f-w8j47      0/1     Pending   0          47m
kube-system   etcd-m01                      1/1     Running   0          47m
kube-system   kube-apiserver-m01            1/1     Running   0          46m
kube-system   kube-controller-manager-m01   1/1     Running   0          46m
kube-system   kube-proxy-5954k              1/1     Running   0          47m
kube-system   kube-scheduler-m01            1/1     Running   0          47m

其中，由于未安装相关的网络组件，eg:flannel,所有coredn还是显示为pending，暂时没有影响。

2.4.4 安装CNI插件flannel

备注：所有的节点都需要安装
具体的安装脚本如下：

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## 拉取镜像
sudo docker pull quay.io/coreos/flannel:v0.11.0-amd64
## 部署
kubectl apply -f https://raw.githubusercontent.com/coreos/flannel/master/Documentation/kube-flannel.yml

安装成功之后，通过 kubectl get pods --all-namespaces，看到所有 Pod 都正常了.

2.5 安装剩余的master

2.5.1 同步tf-k8s-m1的ca证书

首先，将 tf-k8s-m1 中的 ca 证书，scp 到其他 master 机器（tf-k8s-m2 tf-k8s-m3）。
为了方便，这里也是通过脚本来执行，具体如下：
注意：需要确认 tf-k8s-m1 上的 root 账号可以免密登录到 tf-k8s-m2 和 tf-k8s-m3 的 root 账号。

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## 创建脚本：sync.master.ca.sh

#!/bin/sh

vhost="tf-k8s-m2 tf-k8s-m3"
usr=root

who=`whoami`
if [[ "$who" != "$usr" ]];then
  echo "请使用 root 用户执行或者 sudo ./sync.master.ca.sh"
  exit 1
fi

echo $who

# 需要从 m01 拷贝的 ca 文件
caFiles=(
/etc/kubernetes/pki/ca.crt
/etc/kubernetes/pki/ca.key
/etc/kubernetes/pki/sa.key
/etc/kubernetes/pki/sa.pub
/etc/kubernetes/pki/front-proxy-ca.crt
/etc/kubernetes/pki/front-proxy-ca.key
/etc/kubernetes/pki/etcd/ca.crt
/etc/kubernetes/pki/etcd/ca.key
/etc/kubernetes/admin.conf
)

pkiDir=/etc/kubernetes/pki/etcd
for h in $vhost 
do

  ssh ${usr}@$h "mkdir -p $pkiDir"
  
  echo "Dirs for ca scp created, start to scp..."

  # scp 文件到目标机
  for f in ${caFiles[@]}
  do 
    echo "scp $f ${usr}@$h:$f"
    scp $f ${usr}@$h:$f
  done

  echo "Ca files transfered for $h ... ok"
done

执行脚本，将 tf-k8s-m1 相关的 ca 文件传到tf-k8s-m2 和 tf-k8s-m3：

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chmod +x sync.master.ca.sh

sudo ./syncaster.ca.sh

2.5.2 安装master tf-k8s-m2

总共分为四个步骤，分别是:总1. 共分为四个步骤，分别是:

  • 配置证书、初始化 kubelet 配置和启动 kubelet
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[kube@tf-k8s-m2 ~]$ sudo kubeadm init phase certs all --config kubeadm-config.tf-k8s-m2.yaml
[kube@tf-k8s-m2 ~]$ sudo kubeadm init phase etcd local --config kubeadm-config.tf-k8s-m2.yaml
[kube@tf-k8s-m2 ~]$ sudo kubeadm init phase kubeconfig kubelet --config kubeadm-config.tf-k8s-m2.yaml
[kube@tf-k8s-m2 ~]$ sudo kubeadm init phase kubelet-start --config kubeadm-config.tf-k8s-m2.yaml
  • 将etcd加入集群
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[kube@tf-k8s-m2 root]$ kubectl exec -n kube-system etcd-tf-k8s-m1 -- etcdctl --ca-file /etc/kubernetes/pki/etcd/ca.crt --cert-file /etc/kubernetes/pki/etcd/peer.crt --key-file /etc/kubernetes/pki/etcd/peer.key --endpoints=https://10.0.0.1:2379 member add tf-k8s-m2 https://10.0.0.2:2380

启动kube-apiserver、kube-controller-manager、kube-scheduler

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[kube@tf-k8s-m2 ~]$ sudo kubeadm init phase kubeconfig all --config kubeadm-config.m02.yaml
[kube@tf-k8s-m2 ~]$ sudo kubeadm init phase control-plane all --config kubeadm-config.m02.yaml

将节点标记为master节点

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[kube@tf-k8s-m2 ~]$ sudo kubeadm init phase mark-control-plane --config kubeadm-config.m02.yaml

2.5.3 安装master tf-k8s-m3

安装过程和安装master tf-k8s-m2是一样的，区别在于使用的kubeadm配置文件为kubeadm-config.tf-k8s-m3.yaml以及etcd加入成员时指定的实例地址不一样。
完整的流程如下:

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# 1.  配置证书、初始化 kubelet 配置和启动 kubelet
sudo kubeadm init phase certs all --config kubeadm-config.tf-k8s-m3.yaml
sudo kubeadm init phase etcd local --config kubeadm-config.tf-k8s-m3.yaml
sudo kubeadm init phase kubeconfig kubelet --config kubeadm-config.tf-k8s-m3.yaml
sudo kubeadm init phase kubelet-start --config kubeadm-config.tf-k8s-m3.yaml

# 2. 将 etcd 加入集群
kubectl exec -n kube-system etcd-tf-k8s-m1 -- etcdctl --ca-file /etc/kubernetes/pki/etcd/ca.crt --cert-file /etc/kubernetes/pki/etcd/peer.crt --key-file /etc/kubernetes/pki/etcd/peer.key --endpoints=https://10.0.0.1:2379 member add tf-k8s-m3 https://10.0.0.3:2380

# 3. 启动 kube-apiserver、kube-controller-manager、kube-scheduler
sudo kubeadm init phase kubeconfig all --config kubeadm-config.tf-k8s-m3.yaml
sudo kubeadm init phase control-plane all --config kubeadm-config.tf-k8s-m3.yaml

# 4. 将节点标记为 master 节点
sudo kubeadm init phase mark-control-plane --config kubeadm-config.tf-k8s-m3.yaml

2.5.4 验证三个master节点

至此，三个 master 节点安装完成，通过 kubectl get pods –all-namespaces 查看当前集群所有 Pod。

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[kube@tf-k8s-m2 ~]$ kubectl  get pods --all-namespaces
NAMESPACE     NAME                          READY   STATUS    RESTARTS   AGE
kube-system   coredns-78d4cf999f-j8zsr      1/1     Running   0          170m
kube-system   coredns-78d4cf999f-lw5qx      1/1     Running   0          171m
kube-system   etcd-m01                      1/1     Running   8          5h11m
kube-system   etcd-m02                      1/1     Running   12         97m
kube-system   etcd-m03                      1/1     Running   0          91m
kube-system   kube-apiserver-m01            1/1     Running   9          5h11m
kube-system   kube-apiserver-m02            1/1     Running   0          95m
kube-system   kube-apiserver-m03            1/1     Running   0          91m
kube-system   kube-controller-manager-m01   1/1     Running   4          5h11m
kube-system   kube-controller-manager-m02   1/1     Running   0          95m
kube-system   kube-controller-manager-m03   1/1     Running   0          91m
kube-system   kube-flannel-ds-amd64-7b86z   1/1     Running   0          3h31m
kube-system   kube-flannel-ds-amd64-98qks   1/1     Running   0          91m
kube-system   kube-flannel-ds-amd64-ljcdp   1/1     Running   0          97m
kube-system   kube-proxy-krnjq              1/1     Running   0          5h12m
kube-system   kube-proxy-scb25              1/1     Running   0          91m
kube-system   kube-proxy-xp4rj              1/1     Running   0          97m
kube-system   kube-scheduler-m01            1/1     Running   4          5h11m
kube-system   kube-scheduler-m02            1/1     Running   0          95m
kube-system   kube-scheduler-m03            1/1     Running   0          91m

2.5.5 加入工作节点

这步很简单，只需要在工作节点 tf-k8s-n1 和 tf-k8s-n2 上执行加入集群的命令即可。

可以使用上面安装 master tf-k8s-m1 成功后打印的命令 kubeadm join api.tf-k8s.xiangwushuo.com:6443 –token a1t7c1.mzltpc72dc3wzj9y –discovery-token-ca-cert-hash sha256:05f44b111174613055975f012fc11fe09bdcd746bd7b3c8d99060c52619f8738，也可以重新生成 Token。
这里演示如何重新生成 Token 和 证书 hash，在 tf-k8s-m1 上执行以下操作：

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# 1. 创建 token
[kube@tf-k8s-m1 ~]$ kubeadm token create 

# 控制台打印如：
gz1v4w.sulpuxkqtnyci92f

# 2.  查看我们创建的 k8s 集群的证书 hash
[kube@tf-k8s-m1 ~]$ openssl x509 -pubkey -in /etc/kubernetes/pki/ca.crt | openssl rsa -pubin -outform der 2>/dev/null | openssl dgst -sha256 -hex | sed 's/^.* //'

# 控制台打印如：
b125cd0c80462353d8fa3e4f5034f1e1a1e3cc9bade32acfb235daa867c60f61

然后使用kubeadm join,分别在工作节点tf-k8s-n1与tf-k8s-n2上执行，将节点加入
集群，如下：

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在 tf-k8s-m1 上通过 kubectl get nodes 查看，将看到节点已被加进来（节点刚加进来时，状态可能会是 NotReady，稍等一会就回变成 Ready）。

2.6 部署高可用CoreDNS

默认安装的 CoreDNS 存在单点问题。在 m01 上通过 kubectl get pods -n kube-system -owide 查看当前集群 CoreDNS Pod 分布（如下）。

从列表中，可以看到 CoreDNS 的两个 Pod 都在 m01 上，存在单点问题。

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[kube@tf-k8s-m1 ~]$ kubectl get pods -n kube-system -owide
NAME                                    READY   STATUS    RESTARTS   AGE     IP            NODE        NOMINATED NODE   READINESS GATES
coredns-6c67f849c7-h7lcr                1/1     Running   0          4d3h    10.244.3.2    tf-k8s-m1   <none>           <none>
coredns-6c67f849c7-mx9k9                1/1     Running   0          4d3h    10.244.4.2    tf-k8s-m1   <none>           <none>
etcd-tf-k8s-m1                          1/1     Running   1          4d5h    10.0.0.1   tf-k8s-m1   <none>           <none>
etcd-tf-k8s-m2                          1/1     Running   7          4d3h    10.0.0.2   tf-k8s-m2   <none>           <none>
etcd-tf-k8s-m3                          1/1     Running   7          4d3h    10.0.0.3   tf-k8s-m3   <none>           <none>
kube-apiserver-tf-k8s-m1                1/1     Running   0          4d5h    10.0.0.1   tf-k8s-m1   <none>           <none>
kube-apiserver-tf-k8s-m2                1/1     Running   0          4d3h    10.0.0.2   tf-k8s-m2   <none>           <none>
kube-apiserver-tf-k8s-m3                1/1     Running   0          4d3h    10.0.0.3   tf-k8s-m3   <none>           <none>
kube-controller-manager-tf-k8s-m1       1/1     Running   1          4d5h    10.0.0.1   tf-k8s-m1   <none>           <none>
kube-controller-manager-tf-k8s-m2       1/1     Running   0          4d3h    10.0.0.2   tf-k8s-m2   <none>           <none>
kube-controller-manager-tf-k8s-m3       1/1     Running   0          4d3h    10.0.0.3   tf-k8s-m3   <none>           <none>
kube-flannel-ds-amd64-4v6dd             1/1     Running   1          4d3h    10.0.0.5   tf-k8s-n2   <none>           <none>
kube-flannel-ds-amd64-g6sg5             1/1     Running   0          4d3h    10.0.0.3   tf-k8s-m3   <none>           <none>
kube-flannel-ds-amd64-ml4w7             1/1     Running   1          4d3h    10.0.0.4   tf-k8s-n1   <none>           <none>
kube-flannel-ds-amd64-tb27x             1/1     Running   0          4d3h    10.0.0.2   tf-k8s-m2   <none>           <none>
kube-flannel-ds-amd64-x5dqj             1/1     Running   0          4d4h    10.0.0.1   tf-k8s-m1   <none>           <none>
kube-proxy-4wbn7                        1/1     Running   0          4d3h    10.0.0.4   tf-k8s-n1   <none>           <none>
kube-proxy-8dhtz                        1/1     Running   0          4d3h    10.0.0.2   tf-k8s-m2   <none>           <none>
kube-proxy-l8727                        1/1     Running   0          4d5h    10.0.0.1   tf-k8s-m1   <none>           <none>
kube-proxy-tz924                        1/1     Running   0          4d3h    10.0.0.5   tf-k8s-n2   <none>           <none>
kube-proxy-w7tmn                        1/1     Running   0          4d3h    10.0.0.3   tf-k8s-m3   <none>           <none>
kube-scheduler-tf-k8s-m1                1/1     Running   1          4d5h    10.0.0.1   tf-k8s-m1   <none>           <none>
kube-scheduler-tf-k8s-m2                1/1     Running   0          4d3h    10.0.0.2   tf-k8s-m2   <none>           <none>
kube-scheduler-tf-k8s-m3                1/1     Running   0          4d3h    10.0.0.3   tf-k8s-m3   <none>           <none>
kubernetes-dashboard-847f8cb7b8-hmf9m   1/1     Running   0          3d23h   10.244.4.4    tf-k8s-n2   <none>           <none>
metrics-server-8658466f94-pzl6z         1/1     Running   0          4d2h    10.244.3.3    tf-k8s-n1   <none>           <none>

首先删除CoreDNS的deploy，然后创建新的CoreDNS-HA.yaml配置文件，如下

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apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    k8s-app: kube-dns
  name: coredns
  namespace: kube-system
spec:
  #集群规模可自行配置
  replicas: 2
  selector:
    matchLabels:
      k8s-app: kube-dns
  strategy:
    rollingUpdate:
      maxSurge: 25%
      maxUnavailable: 1
    type: RollingUpdate
  template:
    metadata:
      labels:
        k8s-app: kube-dns
    spec:
      affinity:
        podAntiAffinity:
          preferredDuringSchedulingIgnoredDuringExecution:
          - weight: 100
            podAffinityTerm:
              labelSelector:
                matchExpressions:
                - key: k8s-app
                  operator: In
                  values:
                  - kube-dns
              topologyKey: kubernetes.io/hostname
      containers:
      - args:
        - -conf
        - /etc/coredns/Corefile
        image: registry.cn-hangzhou.aliyuncs.com/google_containers/coredns:1.2.6
        imagePullPolicy: IfNotPresent
        livenessProbe:
          failureThreshold: 5
          httpGet:
            path: /health
            port: 8080
            scheme: HTTP
          initialDelaySeconds: 60
          periodSeconds: 10
          successThreshold: 1
          timeoutSeconds: 5
        name: coredns
        ports:
        - containerPort: 53
          name: dns
          protocol: UDP
        - containerPort: 53
          name: dns-tcp
          protocol: TCP
        - containerPort: 9153
          name: metrics
          protocol: TCP
        resources:
          limits:
            memory: 170Mi
          requests:
            cpu: 100m
            memory: 70Mi
        securityContext:
          allowPrivilegeEscalation: false
          capabilities:
            add:
            - NET_BIND_SERVICE
            drop:
            - all
          readOnlyRootFilesystem: true
        terminationMessagePath: /dev/termination-log
        terminationMessagePolicy: File
        volumeMounts:
        - mountPath: /etc/coredns
          name: config-volume
          readOnly: true
      dnsPolicy: Default
      restartPolicy: Always
      schedulerName: default-scheduler
      securityContext: {}
      serviceAccount: coredns
      serviceAccountName: coredns
      terminationGracePeriodSeconds: 30
      tolerations:
      - key: CriticalAddonsOnly
        operator: Exists
      - effect: NoSchedule
        key: node-role.kubernetes.io/master
      volumes:
      - configMap:
          defaultMode: 420
          items:
          - key: Corefile
            path: Corefile
          name: coredns
        name: config-volume

部署新的CoreDNS 

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kubectl apply -f CoreDNS-HA.yaml

2.7 部署监控组件metrics-server

kubernetesv1.11 以后不再支持通过 heaspter 采集监控数据。使用新的监控数据采集组件metrics-server。 metrics-server 比 heaspter 轻量很多，也不做数据的持久化存储，提供实时的监控数据查询。

先将所有文件下载，保存在一个文件夹 metrics-server 里。

修改 metrics-server-deployment.yaml 两处地方，分别是：apiVersion 和 image，最终修改后的 metrics-server-deployment.yaml 如下：

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---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: metrics-server
  namespace: kube-system
---
# 将extensions/v1beta1修改为apps/v1
apiVersion: apps/v1
kind: Deployment
metadata:
  name: metrics-server
  namespace: kube-system
  labels:
    k8s-app: metrics-server
spec:
  selector:
    matchLabels:
      k8s-app: metrics-server
  template:
    metadata:
      name: metrics-server
      labels:
        k8s-app: metrics-server
    spec:
      serviceAccountName: metrics-server
      volumes:
      # mount in tmp so we can safely use from-scratch images and/or read-only containers
      - name: tmp-dir
        emptyDir: {}
      containers:
      - name: metrics-server
        image: cloudnil/metrics-server-amd64:v0.3.1
        command:
          - /metrics-server
          - --kubelet-insecure-tls
          - --kubelet-preferred-address-types=InternalIP
        imagePullPolicy: Always
        volumeMounts:
        - name: tmp-dir
          mountPath: /tmp

进入刚创建的 metrics-server，执行 kubectl apply -f . 进行部署（注意 -f 后面有个点）,如下：

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[kube@tf-k8s-m1 metrics-server]$ kubectl apply -f .

clusterrole.rbac.authorization.k8s.io/system:aggregated-metrics-reader created
clusterrolebinding.rbac.authorization.k8s.io/metrics-server:system:auth-delegator created
rolebinding.rbac.authorization.k8s.io/metrics-server-auth-reader created
apiservice.apiregistration.k8s.io/v1beta1.metrics.k8s.io created
serviceaccount/metrics-server created
deployment.apps/metrics-server created
service/metrics-server created
clusterrole.rbac.authorization.k8s.io/system:metrics-server created
clusterrolebinding.rbac.authorization.k8s.io/system:metrics-server created

运行kubectl get pods -n kube-system，确定metrics-server的pods是否正常running。

2.8 部署Nginx-ingress-controller

Nginx-ingress-controller 是 kubernetes 官方提供的集成了 Ingress-controller 和 Nginx 的一个 docker 镜像。

本次部署中，将 Nginx-ingress 部署到 tf-k8s-m1、tf-k8s-m2、tf-k8s-m3上，监听宿主机的 80 端口。

创建 nginx-ingress.yaml 文件，内容如下：

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apiVersion: v1
kind: Namespace
metadata:
  name: ingress-nginx
---
kind: ConfigMap
apiVersion: v1
metadata:
  name: nginx-configuration
  namespace: ingress-nginx
  labels:
    app.kubernetes.io/name: ingress-nginx
    app.kubernetes.io/part-of: ingress-nginx
---
kind: ConfigMap
apiVersion: v1
metadata:
  name: tcp-services
  namespace: ingress-nginx
  labels:
    app.kubernetes.io/name: ingress-nginx
    app.kubernetes.io/part-of: ingress-nginx
---
kind: ConfigMap
apiVersion: v1
metadata:
  name: udp-services
  namespace: ingress-nginx
  labels:
    app.kubernetes.io/name: ingress-nginx
    app.kubernetes.io/part-of: ingress-nginx
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: nginx-ingress-serviceaccount
  namespace: ingress-nginx
  labels:
    app.kubernetes.io/name: ingress-nginx
    app.kubernetes.io/part-of: ingress-nginx
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRole
metadata:
  name: nginx-ingress-clusterrole
  labels:
    app.kubernetes.io/name: ingress-nginx
    app.kubernetes.io/part-of: ingress-nginx
rules:
  - apiGroups:
      - ""
    resources:
      - configmaps
      - endpoints
      - nodes
      - pods
      - secrets
    verbs:
      - list
      - watch
  - apiGroups:
      - ""
    resources:
      - nodes
    verbs:
      - get
  - apiGroups:
      - ""
    resources:
      - services
    verbs:
      - get
      - list
      - watch
  - apiGroups:
      - "extensions"
    resources:
      - ingresses
    verbs:
      - get
      - list
      - watch
  - apiGroups:
      - ""
    resources:
      - events
    verbs:
      - create
      - patch
  - apiGroups:
      - "extensions"
    resources:
      - ingresses/status
    verbs:
      - update
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: Role
metadata:
  name: nginx-ingress-role
  namespace: ingress-nginx
  labels:
    app.kubernetes.io/name: ingress-nginx
    app.kubernetes.io/part-of: ingress-nginx
rules:
  - apiGroups:
      - ""
    resources:
      - configmaps
      - pods
      - secrets
      - namespaces
    verbs:
      - get
  - apiGroups:
      - ""
    resources:
      - configmaps
    resourceNames:
      - "ingress-controller-leader-nginx"
    verbs:
      - get
      - update
  - apiGroups:
      - ""
    resources:
      - configmaps
    verbs:
      - create
  - apiGroups:
      - ""
    resources:
      - endpoints
    verbs:
      - get
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: RoleBinding
metadata:
  name: nginx-ingress-role-nisa-binding
  namespace: ingress-nginx
  labels:
    app.kubernetes.io/name: ingress-nginx
    app.kubernetes.io/part-of: ingress-nginx
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: Role
  name: nginx-ingress-role
subjects:
  - kind: ServiceAccount
    name: nginx-ingress-serviceaccount
    namespace: ingress-nginx
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRoleBinding
metadata:
  name: nginx-ingress-clusterrole-nisa-binding
  labels:
    app.kubernetes.io/name: ingress-nginx
    app.kubernetes.io/part-of: ingress-nginx
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: nginx-ingress-clusterrole
subjects:
  - kind: ServiceAccount
    name: nginx-ingress-serviceaccount
    namespace: ingress-nginx
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-ingress-controller
  namespace: ingress-nginx
  labels:
    app.kubernetes.io/name: ingress-nginx
    app.kubernetes.io/part-of: ingress-nginx
spec:
  replicas: 3
  selector:
    matchLabels:
      app.kubernetes.io/name: ingress-nginx
      app.kubernetes.io/part-of: ingress-nginx
  template:
    metadata:
      labels:
        app.kubernetes.io/name: ingress-nginx
        app.kubernetes.io/part-of: ingress-nginx
      annotations:
        prometheus.io/port: "10254"
        prometheus.io/scrape: "true"
    spec:
      hostNetwork: true
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
            - matchExpressions:
              - key: kubernetes.io/hostname
                operator: In
                # 指定部署到三台 master 上
                values:
                - tf-k8s-m1
                - tf-k8s-m2
                - tf-k8s-m3
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: app.kubernetes.io/name
                    operator: In
                    values: 
                    - ingress-nginx
              topologyKey: "kubernetes.io/hostname"
      tolerations:
      - key: node-role.kubernetes.io/master
        effect: NoSchedule
      serviceAccountName: nginx-ingress-serviceaccount
      containers:
        - name: nginx-ingress-controller
          image: registry.cn-hangzhou.aliyuncs.com/google_containers/nginx-ingress-controller:0.21.0
          args:
            - /nginx-ingress-controller
            - --configmap=/nginx-configuration
            - --tcp-services-configmap=/tcp-services
            - --udp-services-configmap=/udp-services
            # - --publish-service=/ingress-nginx
            - --annotations-prefix=nginx.ingress.kubernetes.io
          securityContext:
            capabilities:
              drop:
                - ALL
              add:
                - NET_BIND_SERVICE
            # www-data -> 33
            runAsUser: 33
          env:
            - name: POD_NAME
              valueFrom:
                fieldRef:
                  fieldPath: metadata.name
            - name: POD_NAMESPACE
              valueFrom:
                fieldRef:
                  fieldPath: metadata.namespace
          ports:
            - name: http
              containerPort: 80
            - name: https
              containerPort: 443
          livenessProbe:
            failureThreshold: 3
            httpGet:
              path: /healthz
              port: 10254
              scheme: HTTP
            initialDelaySeconds: 10
            periodSeconds: 10
            successThreshold: 1
            timeoutSeconds: 1
          readinessProbe:
            failureThreshold: 3
            httpGet:
              path: /healthz
              port: 10254
              scheme: HTTP
            periodSeconds: 10
            successThreshold: 1
            timeoutSeconds: 1
          resources:
            limits:
              cpu: 1
              memory: 1024Mi
            requests:
              cpu: 0.25
              memory: 512Mi

部署 nginx ingress，执行命令 kubectl apply -f nginx-ingress.yaml

2.9 部署kubernetes-dashboard

2.9.1 Dashboard 配置

新建部署 dashboard 的资源配置文件：kubernetes-dashboard.yaml，内容如下：

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apiVersion: v1
kind: Secret
metadata:
  labels:
    k8s-app: kubernetes-dashboard
  name: kubernetes-dashboard-certs
  namespace: kube-system
type: Opaque
---
apiVersion: v1
kind: ServiceAccount
metadata:
  labels:
    k8s-app: kubernetes-dashboard
  name: kubernetes-dashboard
  namespace: kube-system
---
kind: Role
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: kubernetes-dashboard-minimal
  namespace: kube-system
rules:
  # Allow Dashboard to create 'kubernetes-dashboard-key-holder' secret.
- apiGroups: [""]
  resources: ["secrets"]
  verbs: ["create"]
  # Allow Dashboard to create 'kubernetes-dashboard-settings' config map.
- apiGroups: [""]
  resources: ["configmaps"]
  verbs: ["create"]
  # Allow Dashboard to get, update and delete Dashboard exclusive secrets.
- apiGroups: [""]
  resources: ["secrets"]
  resourceNames: ["kubernetes-dashboard-key-holder", "kubernetes-dashboard-certs"]
  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 from heapster.
- apiGroups: [""]
  resources: ["services"]
  resourceNames: ["heapster"]
  verbs: ["proxy"]
- apiGroups: [""]
  resources: ["services/proxy"]
  resourceNames: ["heapster", "http:heapster:", "https:heapster:"]
  verbs: ["get"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: kubernetes-dashboard-minimal
  namespace: kube-system
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: Role
  name: kubernetes-dashboard-minimal
subjects:
- kind: ServiceAccount
  name: kubernetes-dashboard
  namespace: kube-system
---
kind: Deployment
apiVersion: apps/v1
metadata:
  labels:
    k8s-app: kubernetes-dashboard
  name: kubernetes-dashboard
  namespace: kube-system
spec:
  replicas: 1
  revisionHistoryLimit: 10
  selector:
    matchLabels:
      k8s-app: kubernetes-dashboard
  template:
    metadata:
      labels:
        k8s-app: kubernetes-dashboard
    spec:
      containers:
      - name: kubernetes-dashboard
        # 使用阿里云的镜像
        image: registry.cn-hangzhou.aliyuncs.com/google_containers/kubernetes-dashboard-amd64:v1.10.0
        ports:
        - containerPort: 8443
          protocol: TCP
        args:
          - --auto-generate-certificates
        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
      volumes:
      - name: kubernetes-dashboard-certs
        secret:
          secretName: kubernetes-dashboard-certs
      - name: tmp-volume
        emptyDir: {}
      serviceAccountName: kubernetes-dashboard
      tolerations:
      - key: node-role.kubernetes.io/master
        effect: NoSchedule
---
kind: Service
apiVersion: v1
metadata:
  labels:
    k8s-app: kubernetes-dashboard
  name: kubernetes-dashboard
  namespace: kube-system
spec:
  ports:
    - port: 443
      targetPort: 8443
  selector:
    k8s-app: kubernetes-dashboard
---
# 配置 ingress 配置，待会部署完 ingress 之后，就可以通过以下配置的域名访问
apiVersion: extensions/v1beta1
kind: Ingress
metadata:
  name: dashboard-ingress
  namespace: kube-system
  annotations:
    # 如果通过 HTTP 访问，跳转到 HTTPS
    nginx.ingress.kubernetes.io/ssl-redirect: "true"
    nginx.ingress.kubernetes.io/rewrite-target: /
    # 指定转发协议为 HTTPS，因为 ingress 默认转发协议是 HTTP，而 kubernetes-dashboard 默认是 HTTPS
    nginx.ingress.kubernetes.io/backend-protocol: "HTTPS"
spec:
  # 指定使用的 secret (刚刚创建的 secret)
  tls:
   - secretName: secret-ca-tf-k8s-xiangwushuo-com
  rules:
  # 指定访问 dashboard 的域名
  - host: dashboard.tf-k8s.xiangwushuo.com
    http:
      paths:
      - path: /
        backend:
          serviceName: kubernetes-dashboard
          servicePort: 443

执行部署 kubernetes-dashboard，命令 kubectl apply -f kubernetes-dashboard.yaml.

在本地笔记本电脑上访问dashboard的时候，需要将dashboard.tf-k8s.xiangwushuo.com域名解析到三台master的IP（配置代理），简单地，可以直接在本地/etc/hosts添加

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## 172.66.23.13 为tf-k8s-m1的外网IP
172.66.23.13 dashboard.tf-k8s.xiangwushuo.com

从浏览器访问: http://dashboard.tf-k8s.xiangwushuo.com

2.9.2 HTTPS 访问 Dashboard

由于通过 HTTP 访问 dashboard 会无法登录进去 dashboard 的问题，所以这里我们将 dashboard 的服务配置成 HTTPS 进行访问。
总共三步:
签证书（或者使用权威的证书机构颁发的证书）

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openssl req -x509 -nodes -days 3650 -newkey rsa:2048 -keyout ./tf-k8s.xiangwushuo.com.key -out ./tf-k8s.xiangwushuo.com.crt -subj "/CN=*.xiangwushuo.com"

创建 k8s Secret 资源

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kubectl -n kube-system create secret tls secret-ca-tf-k8s-xiangwushuo-com --key ./tf-k8s.xiangwushuo.com.key --cert tf-k8s.xiangwushuo.com.crt

配置 dashboard 的 ingress 为 HTTPS 访问服务,修改 kubernetes-dashboard.yaml，将其中的 Ingress 配置改为支持 HTTPS，具体配置如下：

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...省略...

apiVersion: extensions/v1beta1
kind: Ingress
metadata:
  name: dashboard-ingress
  namespace: kube-system
  annotations:
    # 如果通过 HTTP 访问，跳转到 HTTPS
    nginx.ingress.kubernetes.io/ssl-redirect: "true"
    nginx.ingress.kubernetes.io/rewrite-target: /
    # 指定转发协议为 HTTPS，因为 ingress 默认转发协议是 HTTP，而 kubernetes-dashboard 默认是 HTTPS
    nginx.ingress.kubernetes.io/backend-protocol: "HTTPS"
spec:
  # 指定使用的 secret (刚刚创建的 secret)
  tls:
   - secretName: secret-ca-k8s-hiko-im
  rules:
  # 指定访问 dashboard 的域名
  - host: dashboard.k8s.hiko.im
    http:
      paths:
      - path: /
        backend:
          serviceName: kubernetes-dashboard
          servicePort: 443

使用 kubectl apply -f kubernetes-dashboard.yaml 让配置生效。

2.9.3 .3 登录 Dashboard

登录 dashboard 需要做几个事情（不用担心，一个脚本搞定）:

新建 sa 的账号（也叫 serviceaccount）
集群角色绑定（将第 1 步新建的账号，绑定到 cluster-admin 这个角色上）
查看 Token 以及 Token 中的 secrect （secrect 中的 token 字段就是来登录的）
执行以下脚本，获得登录的 Token:

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## 创建脚本：create.dashboard.token.sh

#!/bin/sh

kubectl create sa dashboard-admin -n kube-system
kubectl create clusterrolebinding dashboard-admin --clusterrole=cluster-admin --serviceaccount=kube-system:dashboard-admin
ADMIN_SECRET=$(kubectl get secrets -n kube-system | grep dashboard-admin | awk '{print $1}')
DASHBOARD_LOGIN_TOKEN=$(kubectl describe secret -n kube-system ${ADMIN_SECRET} | grep -E '^token' | awk '{print $2}')
echo ${DASHBOARD_LOGIN_TOKEN}

复制 Token 去登录就行，Token 样例：

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eyJhbGciOiJSUzI1NiIsImtpZCI6IiJ9.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.ry4xYI6TFF6J8xXsilu0qhuBeRjSNqVPq3OUzl62Ad3e2wM-biC5pPlKNmJLfJzurxnQrqp59VjmVeTA8BZiF7S6hqlrk8XE9_LFlItUvq3rp5wFuhJuVol8Yoi4UJFzUYQF6baH0O3R10aK33g2WmWLIg79OFAkeMMHrLthbL2pc_p_kG13_qDXlEuVgnIAFsKzxnrCCUfZ2GwGsHEFEqTGBCb0u6x3AZqfQgbN3DALkjjNTyTLP5Ok-LJ3Ug8SZZQBksvTeXCGXZDfk2LDDIvp_DyM7nTL3CTT5cQ3g4aBTFAae47NAkQkmjZg0mxvJH0xVnxrvXLND8FLLkzMxg

3. 参考文献

1. kubeadm 1.13 安装高可用 kubernetes v1.13.1 集群
2. 如何在CentOS 7上修改主机名
3. Linux之ssh免密登录
4. sudo与visudo的超细用法说明
5. kubeadm HA master(v1.13.0)离线包 + 自动化脚本 + 常用插件 For Centos/Fedora
6. github.coreos.flannel
7. Kubernetes Handbook——Kubernetes中文指南/云原生应用架构实践手册

发表于 | 分类于

github地址

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https://github.com/manifoldai/docker-cookiecutter-data-science

使用方案

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<!--安装cookiecutter-->
pip install cookiecutter

<!--创建项目-->
cookiecutter https://github.com/manifoldai/docker-cookiecutter-data-science.git
<!--后续步骤根据提示一步一步来操作即可-->

<!--启动-->
./start.sh

<!--启动pycharm-->
charm .

项目的目录结构解释解释如下

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├── LICENSE
├── Dockerfile            <- New project Dockerfile that sources from base ML dev image
├── docker-compose.yml    <- Docker Compose configuration file
├── docker_clean_all.sh   <- Helper script to remove all containers and images from your system
├── start.sh              <- Script to run docker compose and any other project specific initialization steps 
├── Makefile              <- Makefile with commands like `make data` or `make train`
├── README.md             <- The top-level README for developers using this project.
├── data
│   ├── external          <- Data from third party sources.
│   ├── interim           <- Intermediate data that has been transformed.
│   ├── processed         <- The final, canonical data sets for modeling.
│   └── raw               <- The original, immutable data dump.
│
├── docs                  <- A default Sphinx project; see sphinx-doc.org for details
│
├── models                <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks             <- Jupyter notebooks. Naming convention is a number (for ordering),
│                            the creator's initials, and a short `-` delimited description, e.g.
│                            `1.0-jqp-initial-data-exploration`.
│
├── references            <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports               <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures           <- Generated graphics and figures to be used in reporting
│
├── requirements.txt      <- The requirements file for reproducing the analysis environment, e.g.
│                            generated with `pip freeze > requirements.txt`
│
├── src                   <- Source code for use in this project.
│   ├── __init__.py       <- Makes src a Python module
│   │
│   ├── data              <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features          <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models            <- Scripts to train models and then use trained models to make
│   │   │                    predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

发表于 | 分类于

优化算法是机器学习中的“方法论”，优化算法会告诉机器应该如何优化学习的进程，让自己能够更好地掌握学习到的知识，本文将针对机器学习领域中常用的几种优化算法进行总结。

梯度下降法

梯度下降法与梯度、导数的概念

梯度下降法是用来求解无约束优化问题的一种数学方法，通过梯度下降法可以获取到函数的局部极小值。这里存在一个概念“梯度”，梯度的本意实际上是一个向量，及具有方向性和数值性，其表示的是一个函数在改点沿着该方向变化最快（这个方向是往函数值变大的方向），这个变化率实际上就是该梯度的模。因此，在使用梯度下降法的时候，实际上是要求我们选择梯度的反方向进行计算，只有这样才能保证我们能够取到极小值，这也就是为什么在使用导数（导数与梯度实际上是同一个概念，只不过梯度是一种抽象的表述概念，而导数是实际上用来求解梯度的一种数学表示方式，导数值前面的正负号实际上是决定了梯度的方向）进行参数迭代的时候是要在导数的前面加上负号。

如上图所示，我们对函数f在A点对自变量x求解右导数$f^\prime$，其值为正数，那是因为在A点右侧函数值是增大的，因此，如果要想f值变小，则需要让往左移$x=x_a-\delta f^\prime$；对B点求解右导数，其值为负数，那是因为在B点右侧函数值是变小的，因此，如果想要f值变小，则需要让x往右移$x=x_a-\delta f^\prime$，因此，如果是要使用梯度下降法，则在迭代过程中要在导数的前面加上负号。

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发表于 | 分类于

Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub.

创建GitHub仓库

  1. 登录https://github.com/
  2. 注册账号
  3. 创建一个新Repositorie(仓库名必须要为以下格式：xxx.github.io，其中，xxx为用户名小写)

    hexo部署

    什么是hexo?

    hexo是一个简单但强大的博客系统，Hexo 使用 Markdown（或其他渲染引擎）解析文章，在几秒内，即可利用靓丽的主题生成静态网页。

    部署

  4. 安装node.js

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    <!--MacOS-->
    <!--安装了node之后，默认也会安装npm-->
    brew install node

  5. 安装hexo

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    npm install hexo-cli

3.创建本地项目

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hexo init xxx.github.io
cd xxx.github.io
<!--修改项目配置文件中的deploy配置-->
	deploy:
	  type: git
  repo: https://github.com/HsiaChubby/hsiachubby.github.io.git,master

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发表于 | 分类于

开篇背景

目前，工作中的事情相对少一点，富余时间比较足，因此，可以拿出一部分时间用来学习下机器学习方面的理论知识与实践知识。这样做主要出于两个方面的目的，一方面是想通过本次整理的系列来增强与梳理自己在机器学习领域中的理解，梳理出自己在机器学习领域中的方法论，另一方面，提升自己的实际工作效率。

好了，以上说明了本次系列的背景，接下来将主要阐述本系列的主要内容：

* 希望能够以自己的理解阐述什么是机器学习，机器学习的框架，机器学习可以用来解决什么样的问题等等
* 阐述机器学习中常用的优化方法
* 从模型的角度出发，深入阐述不同模型，主要从以下几个方面来阐述模型：a. 模型是什么（这种阐述是一种通用型的表述）；b.模型产生的背景与原因（通常是为了解决另一个模型中的缺点，而产生的一种新的模型）；c.模型的应用场景限制或者说是前提条件以及常见的应用场景；d.模型涉及的梳理理论背景知识；e.阐述模型的理论推导
* 最后，尽可能地能够输出一份相对完整的模型间的整合对比

以上就是本系列希望能够实现的一个目标，希望接下来会有系列2，系列3…，接下来将正式进入本系列的内容当中.

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