docker容器自动化部署（一）

1、docker容器暴露多个端口

　　To expose just one port, this is what you need to do:

　　docker run -p <host_port>:<container_port>

　　To expose multiple ports, simply provide multiple -p arguments:

　　docker run -p <host_port1>:<container_port1> -p <host_port2>:<container_port2>

　　或者你可以直接桥接网络，然后在dockerfile上面直接expose你所需要的端口，这样可以免去-p参数

2、gitlab-runner自动化部署

　　3.1、注册runner

　　　　命令：sudo gitlab-ci-multi-runner register

➜  ~ sudo gitlab-ci-multi-runner register
Running in system-mode.                            

Please enter the gitlab-ci coordinator URL (e.g. https://gitlab.com/):
http://193.188.2.40/
Please enter the gitlab-ci token for this runner:
Xyx8EEd4YeaMSLv7Snh7
Please enter the gitlab-ci description for this runner:
[sti-DL]: sdk_AutoDeploy
Please enter the gitlab-ci tags for this runner (comma separated):
sdk_AutoDeploy
Whether to run untagged builds [true/false]:
[false]: true
Whether to lock Runner to current project [true/false]:
[false]: true
Registering runner... succeeded                     runner=Xyx8EEd4
Please enter the executor: shell, docker+machine, docker-ssh+machine, docker-ssh, parallels, ssh, virtualbox, kubernetes, docker:
docker
Please enter the default Docker image (e.g. ruby:2.1):
dockerName:v1.0.0
Runner registered successfully. Feel free to start it, but if it's running already the config should be automatically reloaded!

3.2、docker 挂载宿主的GPU驱动、cuda设备文件

　　通过gitlab-ci-multi-runner register注册的Runner配置会存储在/etc/gitlab-runner/config.toml配置文件中，项目中的SDK需要调用英伟达显卡驱动和cuda,通过修改配置文件使得gitlab-runner启动容器时自动加载驱动和cuda。具体配置如下：

　　...
　　1)、gitlab-runner启动容器时挂载宿主机驱动的方法

　　.gitlab-ci.yml文件 gitlab-runner启动容器默认配置文件是不挂载宿主机上的NVIDIA驱动的，若想gitlab-ci启动容器挂载驱动，可以通过下面的方法：

　　修改在*****目录下的config.toml配置文件。
如下：

[[runners]]
  name = "my-runner2"
  url = "http://182.158.10.30/"
  token = "d67392c2aa868f95ddd0256eb42b1b"
  executor = "docker"
  [runners.docker]
    tls_verify = false
    image = "127.0.0.1:5000/rs-sdk-test"
    privileged = false
    devices = ["/dev/nvidia0:/dev/nvidia0","/dev/nvidiactl:/dev/nvidiactl","/dev/nvidia-uvm","/dev/mem:/dev/mem"]
    disable_cache = false
    volumes = ["/cache"]
    shm_size = 0
  [runners.cache]

　　主要是添加devices一行，gitlabrunner启动的容器就会自动挂载宿主机上的NVIDIA驱动。

　　2)、在终端上启动容器挂载驱动和cuda的方法

　　　 sudo docker
run -it --name NAME -v /home/:/mnt/home --privileged=true --device
/dev/nvidia-uvm:/dev/nvidia-uvm --device /dev/nvidia0:/dev/nvidia0
--device /dev/nvidiactl:/dev/nvidiactl myconda:cuda bash
　　　sudo
docker run --privileged=true --device /dev/nvidia0:/dev/nvidia0
--device /dev/nvidiactl:/dev/nvidiactl --device
/dev/nvidia-uvm:/dev/nvidia-uvm --device /dev/mem:/dev/mem -it
127.0.0.1:5000/rs-sdk-test:latest

　　3.3、在master分支中添加.gitlab-ci.yml

　　　　.gitlab-ci.yml文件的格式有特定的语法规则，详细语法见官方文档。本项目中的.yml文件如下：

image: localhost:/***:v1.0.0

before_script:
  - echo "hello world!"
stages:
  - build
  - test

job1:
  stage: build
  script:
    - nvidia-smi
    - cat /usr/local/cuda/version.txt
    - export OpenCV_DIR=/opt/ros/kinetic/include/opencv-3.3.1-dev
    - source /opt/ros/kinetic/setup.bash
    - cd /builds/rs_ws_test/
    - ls
    # - sh /builds/rs_ws_test/rs_perception/compileRos.sh
    - cd ~/PerceptionSDK/rs_ws_2.2.0/src
    - ls
    - rm -rf ~/PerceptionSDK/rs_ws_2.2.0/src/rs_perception
    - ls
    # - gnome-terminal -x ~/PerceptionSDK/rs_ws_2.2.0/roscore
    # - gnome-terminal -x ~/PerceptionSDK/rs_ws_2.2.0/rosparam list
    # - gnome-terminal -x ~/PerceptionSDK/rs_ws_2.2.0/rosparam get/rosdistro
    - mv /builds/rs_ws_test/rs_perception ~/PerceptionSDK/rs_ws_2.2.0/src/
    - mv ~/PerceptionSDK/rs_ws_2.2.0/src/rs_perception/tensorRT ~/PerceptionSDK/rs_ws_2.2.0/src/rs_perception/tensorRT10
    - mv ~/PerceptionSDK/rs_ws_2.2.0/src/rs_perception/tensorRT9 ~/PerceptionSDK/rs_ws_2.2.0/src/rs_perception/tensorRT
    - cd ~/PerceptionSDK/rs_ws_2.2.0
    - catkin_make
    - echo "hello world!"
  tags:
    - perception-runner

　　3.4、写完.yaml配置文件后遇到下面这个问题

　　　　解决方法：搞定在写。。。

巴特西

docker容器自动化部署（一）

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