概念

image:镜像就是个包，是个死的东西。相当于python包。

container:把image启动了你就获得了一个独立的容器, 进入这个启动的容器就相当于一个conda环境。

安装

1.查看系统版本

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$ cat /etc/os-release
# NAME="Ubuntu"
# VERSION="18.04.5 LTS (Bionic Beaver)"
# ID=ubuntu
# ID_LIKE=debian
# PRETTY_NAME="Ubuntu 18.04.5 LTS"
# VERSION_ID="18.04"
# HOME_URL="https://www.ubuntu.com/"
# SUPPORT_URL="https://help.ubuntu.com/"
# BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
# PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
# VERSION_CODENAME=bionic
# UBUNTU_CODENAME=bionic

2.ubuntu 安装教程：docker官方文档

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$ sudo apt-get update
$ sudo apt-get install \
    apt-transport-https \
    ca-certificates \
    curl \
    gnupg \
    lsb-release
$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
# 这里会弹出一个warning，不用管
$ uname -a
# 查看系统内核信息
Linux jerry 5.4.0-73-generic #82~18.04.1-Ubuntu SMP Fri Apr 16 15:10:02 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
# 对应安装x86_64
$ echo \
  "deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu \
  $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

$ sudo apt-get update
$ sudo apt-get install docker-ce docker-ce-cli containerd.io
# docker引擎安装，ce对应为社区版本
# 一定要先执行sudo apt-get update，要不然会报错

到现在为止，安装完毕

3.源的切换（optional）

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阿里云：https://y0qd3iq.mirror.aliyuncs.com
网易：http://hub-mirror.c.163.com
中国科技大学：https://docker.mirrors.ustc.edu.cn
………………
修改文件 /etc/docker/daemon.json
{
      "registry-mirrors": ["https://y0qd3iq.mirror.aliyuncs.com",""]
}

执行docker version时报错

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Got permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock: Get http://%2Fvar%2Frun%2Fdocker.sock/v1.24/version: dial unix /var/run/docker.sock: connect: permission denied

这是因为docker进程使用 Unix Socket 而不是 TCP 端口。而默认情况下，Unix socket 属于 root 用户，因此需要 root权限 才能访问。解决方法：

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$ sudo groupadd docker
$ sudo gpasswd -a $jerry docker # 查看是否存在于docker用户组, jerry为账户名
$ sudo gpasswd -a $USER docker  # 将当前用户添加到docker用户组
$ newgrp docker     # 更新用户组
$ docker version    # 成功执行

4.hello-world测试

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$ docker run hello-world
# Unable to find image 'hello-world:latest' locally
# latest: Pulling from library/hello-world
# b8dfde127a29: Pull complete
# Digest: sha256:9f6ad537c5132bcce57f7a0a20e317228d382c3cd61edae14650eec68b2b345c
# Status: Downloaded newer image for hello-world:latest
# 
# Hello from Docker!
# This message shows that your installation appears to be working correctly.
# 
# To generate this message, Docker took the following steps:
#  1. The Docker client contacted the Docker daemon.
#  2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
#     (amd64)
#  3. The Docker daemon created a new container from that image which runs the
#     executable that produces the output you are currently reading.
#  4. The Docker daemon streamed that output to the Docker client, which sent it
#     to your terminal.
# 
# To try something more ambitious, you can run an Ubuntu container with:
#  $ docker run -it ubuntu bash
# 
# Share images, automate workflows, and more with a free Docker ID:
#  https://hub.docker.com/
# 
# For more examples and ideas, visit:
#  https://docs.docker.com/get-started/

5.查看当前镜像

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$ docker images
# REPOSITORY    TAG       IMAGE ID       CREATED        SIZE
# hello-world   latest    d1165f221234   3 months ago   13.3kB
# Ps:
# REPOSITORY： 镜像的仓库源
# TAG：镜像的标签
# IMAGE ID：镜像的id
# CREATED：镜像的创建时间
# SIZE：镜像的大小

从share/docker构建镜像

以两种方式举例

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yolov5_mmdetection_runtime/   # 根据dockerfile进行Build
yolov5_mmdetection_20210421.tar.gz    # 可以直接docker load

1.根据dockerfile构建

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$ docker build -f ./Dockerfile -t yolov5_mmdetection_1:1.0 .
# docker build -f dockerfile文件路径 -t 镜像名:[tag] .
# 经过一段时间的等待
# Successfully built cec3587f725c
# Successfully tagged yolov5_mmdetection_1:1.0

# 查看当前镜像
$ docker images
# REPOSITORY             TAG                             IMAGE ID       CREATED         SIZE
# yolov5_mmdetection_1   1.0                             cec3587f725c   2 minutes ago   13.7GB
# hello-world            latest                          d1165f221234   3 months ago    13.3kB
# nvidia/cuda            11.0-cudnn8-devel-ubuntu16.04   a42cfaadafba   5 months ago    7.72GB

2.docker load

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$ docker load -i yolov5_mmdetection_20210421.tar.gz
$ docker images
# REPOSITORY             TAG       IMAGE ID       CREATED             SIZE
# micro_i_baseruntime    1.1b      f9be0ed18d82   6 weeks ago         13.9GB
# 已成功Load

3.启动容器

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docker run [可选参数] imageid
# 参数说明
--name="Name"           # 容器名字，用来区分容器
-d                      # 表明以后台方式运行
-it                     # -i以交互模式运行，通常与 -t 同时使用
-p  127.0.0.1:80:8080   # 将本地主机127.0.0.1的80端口  映射到  容器8080端口 
-P                      # 大写是随机指定端口

4.启动刚刚创建的镜像(简化版)

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$ docker run -it cec3587f725c /bin/bash  # 即可进入容器
ctrl+p+q 不关闭容器退出
$ docker ps  # 查看当前运行中的容器
# CONTAINER ID   IMAGE          COMMAND       CREATED              STATUS              PORTS     NAMES
# 0c6c70c56875   cec3587f725c   "/bin/bash"   About a minute ago   Up About a minute   22/tcp    gallant_nobel
$ docker exec -it 0c6c70c56875 /bin/bash    # 再次进入刚刚的容器
$ docker exec -it xzh_torch bash
$ docker attach container_id

docker run/commit

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$ docker run --gpus all --name="yolo_test_demo" -td -v /home/adt/wyProject/mmdetection-master:/workspace/mmdetection --restart=always --ipc=host -p 7832:22 micro_i_baseruntime:1.1b
# 执行之后容器中的/workspace/mmdetection已经和本地的/home/adt/wyProject/mmdetection-master建立了映射，所以可以直接在本地的/home/adt/wyProject/mmdetection-master目录下进行coding，在容器中运行代码
$ pip list  # 查看当前容器的环境，已经包含了yolov5和mmdet的对应环境，直接python运行即可

# 当安装新的包或者需要迭代新的版本时，提交容器成为一个新的副本
docker commit   # 提交容器成为一个新的副本
docker commit -m "" -a "作者" 容器id 目标镜像名:[TAG]    # -m当前版本提交的描述信息
# 比如：
$ docker commit -m="add test.py" -a="rainbows" 0c6c70c56875 yolov5_mmdetection_1:1.1
$ docker images
# REPOSITORY             TAG                             IMAGE ID       CREATED          SIZE
# yolov5_mmdetection_1   1.1                             d46d1f8b2838   7 seconds ago    13.7GB
# yolov5_mmdetection_1   1.0                             cec3587f725c   27 minutes ago   13.7GB
# hello-world            latest                          d1165f221234   3 months ago     13.3kB
# nvidia/cuda            11.0-cudnn8-devel-ubuntu16.04   a42cfaadafba   5 months ago     7.72GB

docker stop/rm

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docker stop 容器id    # 停止当前正在运行的容器
docker rm 容器id      # 删除容器
docker rmi 镜像id     # 删除镜像

docker push

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docker login
docker tag yolox:v1 jerryzz668/tensorrt7.2.2:latest
docker push jerryzz668/tensorrt7.2.2:latest

docker import/export

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docker export 3fdd28cd1201 > yolox_demo.tar  # 将容器导出为image
docker import yolox_demo.tar  yolox_demo:v2  # 将image导入

remove docker

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sudo apt-get remove docker docker-engine docker.io containerd runc
# 如果提示没有删除，则进入/var/lib/docker手动删除
sudo apt-get purge docker-ce docker-ce-cli containerd.io
sudo rm -rf /var/lib/docker
sudo rm -rf /var/lib/containerd

现场机台一键安装docker(工作时，现场机器安装记录)

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cd
# install docker
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh

# delete sudo limit
sudo groupadd docker
sudo gpasswd -a ${USER} docker
sudo systemctl restart docker
sudo chmod a+rw /var/run/docker.sock

# install packages about GPU
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker