diff --git a/anolis-courses/KeenTune/auto-tuning/step1.md b/anolis-courses/KeenTune/auto-tuning/step1.md
index 0e427959ef7ce24b8355f65889bfd34a0c11ca1c..d5ef82f13f67e7dd4683114be08bbffaac623414 100644
--- a/anolis-courses/KeenTune/auto-tuning/step1.md
+++ b/anolis-courses/KeenTune/auto-tuning/step1.md
@@ -1,4 +1,95 @@
-1. 运行下面的命令启动KeenTune服务  
-[[ systemctl restart  keentuned keentune-target]]
+STEP 1. 部署安装KeenTune环境
 
+这一步我们应该部署KeenTune所有组件以及使用的benchmark工具wrk，安装和配置调优目标nginx服务，以及准备KeenTune运行所需的python环境等，为了方便操作，我们提供了一个脚本方便您一键部署  
 
+ACTION: 请将以下脚本copy到文件中并执行
+ps. 如果已经安装过请跳过此步骤
+```s
+#!/bin/bash
+json_name="sysctl.json, nginx.json"
+nginx_conf=/etc/nginx/nginx.conf
+keentune_conf=/etc/keentune/conf/keentuned.conf
+
+
+config_yum_source()
+{
+  if cat /etc/yum.repos.d/epel.repo | grep "\[keentune\]";then
+    echo "keentune already in yum source"
+  else
+    cat >> /etc/yum.repos.d/epel.repo <<EOF
+[keentune]
+name=keentune-os
+baseurl=https://mirrors.openanolis.cn/anolis/8.6/Plus/\$basearch/os
+gpgkey=https://mirrors.openanolis.cn/anolis/RPM-GPG-KEY-ANOLIS
+enabled=1
+gpgcheck=0
+EOF
+    yum clean all
+    yum makecache
+  fi
+}
+
+install_wrk()
+{
+    yum install -y git
+    rm -rf wrk
+    which wrk && return 0
+    git clone https://gitee.com/mirrors/wrk.git wrk
+    cd wrk
+    make && cp wrk /usr/bin/
+    cd -
+    rm -rf wrk
+}
+
+install_nginx()
+{
+    which nginx && return 0
+    yum install -y nginx
+    sed -i "s/access_log.*/access_log off;/g" $nginx_conf
+    systemctl restart nginx
+    systemctl status nginx || return 1
+}
+
+set_python_env()
+{
+  pip3 install --upgrade pip
+  pip3 install numpy==1.19.5 tornado==6.1 hyperopt==0.2.5 requests==2.25.1
+  pip3 install scikit_learn==0.24.2 pynginxconfig shap xgboost
+}
+
+get_preset_files()
+{
+  yum install -y git
+  git clone -b user_courses_0726 https://gitee.com/anolis/keentuned.git
+  cd keentuned/preset_files
+  mkdir -p /var/keentune/profile
+  mkdir -p /var/keentune/data/tuning_data
+  mkdir -p /var/keentune/tuning_workspace/http_long
+
+  cp tuning_jobs.csv /var/keentune
+  cp http_long_group1.conf /var/keentune/profile
+  cp -r http_long /var/keentune/data/tuning_data
+  cd -
+  rm -rf keentuned
+}
+
+set_env()
+{
+  install_wrk
+  install_nginx
+  set_python_env
+  config_yum_source
+}
+
+start_keentune()
+{
+  get_preset_files
+  yum install -y keentuned keentune-brain keentune-target keentune-bench
+  sed -i "s/PARAMETER.*=.*/PARAMETER = ${json_name}/g" $keentune_conf
+  systemctl restart  keentuned keentune-brain keentune-target keentune-bench
+}
+
+set_env
+start_keentune
+echo "start keentune server successfully!"
+```
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/auto-tuning/step2.md b/anolis-courses/KeenTune/auto-tuning/step2.md
index 544b5f45c84401dd60d4010556ac9cea1cc42ef7..30d897634db21b4cb71e2b7e04e14b862b55c83b 100644
--- a/anolis-courses/KeenTune/auto-tuning/step2.md
+++ b/anolis-courses/KeenTune/auto-tuning/step2.md
@@ -1,2 +1,31 @@
-1. KeenTune提供了wrk的执行脚本，运行该脚本获得性能基准值(baseline)  
-[[ python3 /etc/keentune/benchmark/wrk/wrk_http_long.py localhost ]]
\ No newline at end of file
+STEP 2. Parameters Auto-Tuning
+
+1. 运行下面的命令启动KeenTune服务  
+[[ systemctl restart  keentuned keentune-brain keentune-target keentune-bench ]]
+
+2. KeenTune提供了wrk的执行脚本，运行该脚本获得性能基准值(baseline)  
+[[ python3 /etc/keentune/benchmark/wrk/wrk_http_long.py localhost ]]
+
+3. 查询KeenTune内置的可调优的参数配置文件  
+[[ keentune param list ]]
+
+4. 我们将会用到sysctl.json和nginx.json两个文件分别对内核参数和nginx参数进行调优
++ 查看要调优的内核参数
+[[ cat /etc/keentune/parameter/sysctl.json ]]
++ 查看要调优的nginx配置参数
+[[ cat /etc/keentune/parameter/nginx.json ]]
++ 这两个参数已经在keentune的配置文件中指定, 可以通过以下命令查看
+[[ cat /etc/keentune/conf/keentuned.conf ]]
+
+5. 我们直接使用keentune param tune命令发起一次动态调优，使用-i指定调优轮次，--job指定任务名
+[[ keentune param tune -i 20 --job http_long_test ]]
+
+6. 调优过程会保存在日志中，我们使用tail命令查看调优过程日志
+[[ tail -f /var/log/keentune/http_long_test.log ]]
+等待调优结束, 日志中出现``[best] Tuning improvement``数据
+
+7. 我们再一次执行benchmark来查看调优效果
+[[ python3 /etc/keentune/benchmark/wrk/wrk_http_long.py localhost ]]
+
+8. 最后我们使用rollback命令恢复环境默认参数  
+[[ keentune param rollback ]]
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/auto-tuning/step3.md b/anolis-courses/KeenTune/auto-tuning/step3.md
deleted file mode 100644
index 058df2956db11058ecebc056a927e989f2482c5f..0000000000000000000000000000000000000000
--- a/anolis-courses/KeenTune/auto-tuning/step3.md
+++ /dev/null
@@ -1,2 +0,0 @@
-1. 查询KeenTune内置的可调优的参数配置文件  
-[[ keentune param list ]]
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/auto-tuning/step4.md b/anolis-courses/KeenTune/auto-tuning/step4.md
deleted file mode 100644
index 5f1e604412cdfbab0282cc1b7b9d2e8d432a59f0..0000000000000000000000000000000000000000
--- a/anolis-courses/KeenTune/auto-tuning/step4.md
+++ /dev/null
@@ -1,7 +0,0 @@
-我们将会用到sysctl.json和nginx.json两个文件分别对内核参数和nginx参数进行调优
-1. 查看要调优的内核参数
-[[ cat /etc/keentune/parameter/sysctl.json ]]
-2. 查看要调优的nginx配置参数
-[[ cat /etc/keentune/parameter/nginx.json ]]
-3. 这两个参数已经在keentune的配置文件中指定, 可以通过以下命令查看
-[[ cat /etc/keentune/conf/keentuned.conf ]]
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/auto-tuning/step5.md b/anolis-courses/KeenTune/auto-tuning/step5.md
deleted file mode 100644
index 2622c91cc5fa927ae240521ffb250d06441f4d2e..0000000000000000000000000000000000000000
--- a/anolis-courses/KeenTune/auto-tuning/step5.md
+++ /dev/null
@@ -1,2 +0,0 @@
-1. 我们直接使用keentune param tune命令发起一次动态调优，使用-i指定调优轮次，--job指定任务名
-[[ keentune param tune -i 20 --job http_long_test ]]
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/auto-tuning/step6.md b/anolis-courses/KeenTune/auto-tuning/step6.md
deleted file mode 100644
index 6b2a8193fcbbbdf17319a43f036a429e10631425..0000000000000000000000000000000000000000
--- a/anolis-courses/KeenTune/auto-tuning/step6.md
+++ /dev/null
@@ -1,3 +0,0 @@
-1. 调优过程会保存在日志中，我们使用tail命令查看调优过程日志
-[[ tail -f /var/log/keentune/http_long_test.log ]]
-2. 等待调优结束, 日志中出现``[best] Tuning improvement``数据
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/auto-tuning/step7.md b/anolis-courses/KeenTune/auto-tuning/step7.md
deleted file mode 100644
index ff1e629c066da99a9190cdb7135cc08acc199a9e..0000000000000000000000000000000000000000
--- a/anolis-courses/KeenTune/auto-tuning/step7.md
+++ /dev/null
@@ -1,2 +0,0 @@
-1. 我们再一次执行benchmark来查看调优效果
-[[ python3 /etc/keentune/benchmark/wrk/wrk_http_long.py localhost ]]
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/auto-tuning/step8.md b/anolis-courses/KeenTune/auto-tuning/step8.md
deleted file mode 100644
index 1e61488fa5efaa68d3edc413fce5e35363509a7b..0000000000000000000000000000000000000000
--- a/anolis-courses/KeenTune/auto-tuning/step8.md
+++ /dev/null
@@ -1,2 +0,0 @@
-1. 最后我们使用rollback命令恢复环境默认参数  
-[[ keentune param rollback ]]
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/profile-setting/step1.md b/anolis-courses/KeenTune/profile-setting/step1.md
index 900dd88a7c6dd19840e04e58b7aaff03e2666869..d5ef82f13f67e7dd4683114be08bbffaac623414 100644
--- a/anolis-courses/KeenTune/profile-setting/step1.md
+++ b/anolis-courses/KeenTune/profile-setting/step1.md
@@ -1,2 +1,95 @@
-运行下面的命令启动KeenTune服务  
-[[ systemctl restart  keentuned keentune-brain keentune-target keentune-bench ]]
\ No newline at end of file
+STEP 1. 部署安装KeenTune环境
+
+这一步我们应该部署KeenTune所有组件以及使用的benchmark工具wrk，安装和配置调优目标nginx服务，以及准备KeenTune运行所需的python环境等，为了方便操作，我们提供了一个脚本方便您一键部署  
+
+ACTION: 请将以下脚本copy到文件中并执行
+ps. 如果已经安装过请跳过此步骤
+```s
+#!/bin/bash
+json_name="sysctl.json, nginx.json"
+nginx_conf=/etc/nginx/nginx.conf
+keentune_conf=/etc/keentune/conf/keentuned.conf
+
+
+config_yum_source()
+{
+  if cat /etc/yum.repos.d/epel.repo | grep "\[keentune\]";then
+    echo "keentune already in yum source"
+  else
+    cat >> /etc/yum.repos.d/epel.repo <<EOF
+[keentune]
+name=keentune-os
+baseurl=https://mirrors.openanolis.cn/anolis/8.6/Plus/\$basearch/os
+gpgkey=https://mirrors.openanolis.cn/anolis/RPM-GPG-KEY-ANOLIS
+enabled=1
+gpgcheck=0
+EOF
+    yum clean all
+    yum makecache
+  fi
+}
+
+install_wrk()
+{
+    yum install -y git
+    rm -rf wrk
+    which wrk && return 0
+    git clone https://gitee.com/mirrors/wrk.git wrk
+    cd wrk
+    make && cp wrk /usr/bin/
+    cd -
+    rm -rf wrk
+}
+
+install_nginx()
+{
+    which nginx && return 0
+    yum install -y nginx
+    sed -i "s/access_log.*/access_log off;/g" $nginx_conf
+    systemctl restart nginx
+    systemctl status nginx || return 1
+}
+
+set_python_env()
+{
+  pip3 install --upgrade pip
+  pip3 install numpy==1.19.5 tornado==6.1 hyperopt==0.2.5 requests==2.25.1
+  pip3 install scikit_learn==0.24.2 pynginxconfig shap xgboost
+}
+
+get_preset_files()
+{
+  yum install -y git
+  git clone -b user_courses_0726 https://gitee.com/anolis/keentuned.git
+  cd keentuned/preset_files
+  mkdir -p /var/keentune/profile
+  mkdir -p /var/keentune/data/tuning_data
+  mkdir -p /var/keentune/tuning_workspace/http_long
+
+  cp tuning_jobs.csv /var/keentune
+  cp http_long_group1.conf /var/keentune/profile
+  cp -r http_long /var/keentune/data/tuning_data
+  cd -
+  rm -rf keentuned
+}
+
+set_env()
+{
+  install_wrk
+  install_nginx
+  set_python_env
+  config_yum_source
+}
+
+start_keentune()
+{
+  get_preset_files
+  yum install -y keentuned keentune-brain keentune-target keentune-bench
+  sed -i "s/PARAMETER.*=.*/PARAMETER = ${json_name}/g" $keentune_conf
+  systemctl restart  keentuned keentune-brain keentune-target keentune-bench
+}
+
+set_env
+start_keentune
+echo "start keentune server successfully!"
+```
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/profile-setting/step2.md b/anolis-courses/KeenTune/profile-setting/step2.md
index ebfa1f5a76eb53f7535dca84a0916910eaf49e9e..a75d0a37c466ecf47c39fe3e5f56187719a9165c 100644
--- a/anolis-courses/KeenTune/profile-setting/step2.md
+++ b/anolis-courses/KeenTune/profile-setting/step2.md
@@ -1,2 +1,19 @@
-KeenTune提供了wrk的执行脚本，运行该脚本获得性能基准值(baseline)    
-[[ python3 /etc/keentune/benchmark/wrk/wrk_http_long.py localhost ]]
\ No newline at end of file
+STEP 2. Profile Setting
+
+1. 运行下面的命令启动KeenTune服务  
+[[ systemctl restart keentuned keentune-target]]
+
+2. KeenTune提供了wrk的执行脚本，运行该脚本获得性能基准值(baseline)    
+[[ python3 /etc/keentune/benchmark/wrk/wrk_http_long.py localhost ]]
+
+3. 查看KeenTune中可使用的专家调优文件  
+[[ keentune profile list ]]
+
+4. 我们选择``http_long_group1.conf``这个profile文件设置到当前环境中  
+[[ keentune profile set --group1 http_long_group1.conf  ]]
+
+5. 设置成功后，我们再次执行benchmark查看调优后的性能得分  
+[[ python3 /etc/keentune/benchmark/wrk/wrk_http_long.py localhost ]]
+
+6. 最后使用rollback命令恢复环境默认参数  
+[[ keentune profile rollback ]]
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/profile-setting/step3.md b/anolis-courses/KeenTune/profile-setting/step3.md
deleted file mode 100644
index 7b193dff533298b23872ab903db3306d6756d11f..0000000000000000000000000000000000000000
--- a/anolis-courses/KeenTune/profile-setting/step3.md
+++ /dev/null
@@ -1,2 +0,0 @@
-查看KeenTune中可使用的专家调优文件  
-[[ keentune profile list ]]
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/profile-setting/step4.md b/anolis-courses/KeenTune/profile-setting/step4.md
deleted file mode 100644
index 14b9110aeb3031535f47b3397a49dfbd1d16dc8b..0000000000000000000000000000000000000000
--- a/anolis-courses/KeenTune/profile-setting/step4.md
+++ /dev/null
@@ -1,2 +0,0 @@
-我们选择``http_long_group1.conf``这个profile文件设置到当前环境中  
-[[ keentune profile set --group1 http_long_group1.conf  ]]
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/profile-setting/step5.md b/anolis-courses/KeenTune/profile-setting/step5.md
deleted file mode 100644
index 8d0d11da62740ac2eaeeab00ac2ecf1a66bc52bd..0000000000000000000000000000000000000000
--- a/anolis-courses/KeenTune/profile-setting/step5.md
+++ /dev/null
@@ -1,2 +0,0 @@
-设置成功后，我们再次执行benchmark查看调优后的性能得分  
-[[ python3 /etc/keentune/benchmark/wrk/wrk_http_long.py localhost ]]
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/profile-setting/step6.md b/anolis-courses/KeenTune/profile-setting/step6.md
deleted file mode 100644
index 931747604b3614b73c4522b42ae300352cee3a32..0000000000000000000000000000000000000000
--- a/anolis-courses/KeenTune/profile-setting/step6.md
+++ /dev/null
@@ -1,2 +0,0 @@
-最后使用rollback命令恢复环境默认参数  
-[[ keentune profile rollback ]]
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/sensitizing/step1.md b/anolis-courses/KeenTune/sensitizing/step1.md
index e0c0238fae5df276435553838a03e6f693ccab29..d5ef82f13f67e7dd4683114be08bbffaac623414 100644
--- a/anolis-courses/KeenTune/sensitizing/step1.md
+++ b/anolis-courses/KeenTune/sensitizing/step1.md
@@ -1,2 +1,95 @@
-运行下面的命令启动KeenTune服务  
-[[ systemctl restart  keentuned keentune-brain]]
\ No newline at end of file
+STEP 1. 部署安装KeenTune环境
+
+这一步我们应该部署KeenTune所有组件以及使用的benchmark工具wrk，安装和配置调优目标nginx服务，以及准备KeenTune运行所需的python环境等，为了方便操作，我们提供了一个脚本方便您一键部署  
+
+ACTION: 请将以下脚本copy到文件中并执行
+ps. 如果已经安装过请跳过此步骤
+```s
+#!/bin/bash
+json_name="sysctl.json, nginx.json"
+nginx_conf=/etc/nginx/nginx.conf
+keentune_conf=/etc/keentune/conf/keentuned.conf
+
+
+config_yum_source()
+{
+  if cat /etc/yum.repos.d/epel.repo | grep "\[keentune\]";then
+    echo "keentune already in yum source"
+  else
+    cat >> /etc/yum.repos.d/epel.repo <<EOF
+[keentune]
+name=keentune-os
+baseurl=https://mirrors.openanolis.cn/anolis/8.6/Plus/\$basearch/os
+gpgkey=https://mirrors.openanolis.cn/anolis/RPM-GPG-KEY-ANOLIS
+enabled=1
+gpgcheck=0
+EOF
+    yum clean all
+    yum makecache
+  fi
+}
+
+install_wrk()
+{
+    yum install -y git
+    rm -rf wrk
+    which wrk && return 0
+    git clone https://gitee.com/mirrors/wrk.git wrk
+    cd wrk
+    make && cp wrk /usr/bin/
+    cd -
+    rm -rf wrk
+}
+
+install_nginx()
+{
+    which nginx && return 0
+    yum install -y nginx
+    sed -i "s/access_log.*/access_log off;/g" $nginx_conf
+    systemctl restart nginx
+    systemctl status nginx || return 1
+}
+
+set_python_env()
+{
+  pip3 install --upgrade pip
+  pip3 install numpy==1.19.5 tornado==6.1 hyperopt==0.2.5 requests==2.25.1
+  pip3 install scikit_learn==0.24.2 pynginxconfig shap xgboost
+}
+
+get_preset_files()
+{
+  yum install -y git
+  git clone -b user_courses_0726 https://gitee.com/anolis/keentuned.git
+  cd keentuned/preset_files
+  mkdir -p /var/keentune/profile
+  mkdir -p /var/keentune/data/tuning_data
+  mkdir -p /var/keentune/tuning_workspace/http_long
+
+  cp tuning_jobs.csv /var/keentune
+  cp http_long_group1.conf /var/keentune/profile
+  cp -r http_long /var/keentune/data/tuning_data
+  cd -
+  rm -rf keentuned
+}
+
+set_env()
+{
+  install_wrk
+  install_nginx
+  set_python_env
+  config_yum_source
+}
+
+start_keentune()
+{
+  get_preset_files
+  yum install -y keentuned keentune-brain keentune-target keentune-bench
+  sed -i "s/PARAMETER.*=.*/PARAMETER = ${json_name}/g" $keentune_conf
+  systemctl restart  keentuned keentune-brain keentune-target keentune-bench
+}
+
+set_env
+start_keentune
+echo "start keentune server successfully!"
+```
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/sensitizing/step2.md b/anolis-courses/KeenTune/sensitizing/step2.md
index 8edc9aaf912ee80a7d9b691ba6bb5095e896e8dd..8468233780409b5d28474b64e1b502d04a1e5419 100644
--- a/anolis-courses/KeenTune/sensitizing/step2.md
+++ b/anolis-courses/KeenTune/sensitizing/step2.md
@@ -1,2 +1,14 @@
-查看可用的数据列表，我们已经提前完成了一个名为http_long的参数调优任务，并保留了它的数据作为敏感参数识别的输入  
-[[ keentune param jobs ]]
\ No newline at end of file
+STEP 2. Sensitivity identification
+
+1. 运行下面的命令启动KeenTune服务  
+[[ systemctl restart keentuned keentune-brain]]
+
+2. 我们使用http_long调优任务的数据发起一次参数敏感性识别，--data指定使用的数据，--job表明本次参数敏感性识别任务的名称  
+[[ keentune sensitize train --data http_long --job http_long_train ]]
+
+3. 我们使用http_long调优任务的数据发起一次参数敏感性识别，--data指定使用的数据，--job表明本次参数敏感性识别任务的名称  
+[[ keentune sensitize train --data http_long --job http_long_train ]]
+
+4. 使用tail命令查看参数识别过程日志  
+[[ tail -f /var/log/keentune/keentuned-sensitize-train-http_long_train.log ]]
+等待参数识别完成，我们就可以在日志中看到参数敏感性排序  
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/sensitizing/step3.md b/anolis-courses/KeenTune/sensitizing/step3.md
deleted file mode 100644
index de6e72d409e641506ca5862fb657cd1b6bfd7a3d..0000000000000000000000000000000000000000
--- a/anolis-courses/KeenTune/sensitizing/step3.md
+++ /dev/null
@@ -1,2 +0,0 @@
-我们使用http_long调优任务的数据发起一次参数敏感性识别，--data指定使用的数据，--job表明本次参数敏感性识别任务的名称  
-[[ keentune sensitize train --data http_long --job http_long_train ]]
\ No newline at end of file
diff --git a/anolis-courses/KeenTune/sensitizing/step4.md b/anolis-courses/KeenTune/sensitizing/step4.md
deleted file mode 100644
index 41b831762d552e81c3fdd7b808db31be332ac277..0000000000000000000000000000000000000000
--- a/anolis-courses/KeenTune/sensitizing/step4.md
+++ /dev/null
@@ -1,3 +0,0 @@
-1. 使用tail命令查看参数识别过程日志  
-[[ tail -f /var/log/keentune/keentuned-sensitize-train-http_long_train.log ]]
-2. 等待参数识别完成，我们就可以在日志中看到参数敏感性排序  
\ No newline at end of file