# 安装
sudo apt install docker.io nvidia-docker2

# 创建 docker 用户组
sudo groupadd docker

# 将用户加入 docker 用户组
sudo gpasswd -a ${username} docker

# 重启 docker 应用
sudo systemctl restart docker

# 添加访问和执行权限
sudo chmod a+rw /var/run/docker.sock

# 更换 docker 默认存储位置及镜像源
docker info

# 将默认位置的 docker 文件夹复制到新目录
sudo mv /var/lib/docker /home/data1/

sudo vim /etc/docker/daemon.json

# 以下为修改内容
{
    "runtimes": {
        "nvidia": {
            "path": "nvidia-container-runtime",
            "runtimeArgs": []
        }
    },
    "data-root":"/home/data1/docker",
    "registry-mirrors":[
                        "https://hub-mirror.c.163.com/",
                        "https://docker.mirrors.ustc.edu.cn/"
                        ]
}

# 重启 docker 服务
sudo systemctl daemon-reload
sudo systemctl restart docker


# 查看当前文件夹及文件大小
sudo du -h --max-depth=1

# df 以磁盘分区为单位查看文件系统
df -lh

# 拉取镜像:https://hub.docker.com/r/nvidia/cuda/tags?page=3&name=18
docker pull nvidia/cuda:11.4.0-devel-ubuntu18.04

# test
sudo docker run --rm --gpus all nvidia/cuda:11.4.0-devel-ubuntu18.04 nvidia-smi

# -i 控制台交互  -t 支持终端登录  -d 指定容器运行于后台
# -v 给容器挂载存储卷，挂载到容器某个目录（前者是宿主机目录，后者是容器目录，中间以:分隔）
# -p 端口映射（前者是宿主机目录，后者是容器目录，中间以:分隔，例如 -p 80:80）
# -m  --memory=[] 指定容器内存上限
# --name=[] 指定容器名字
# --gpus all 使用宿主机gpu
# --rm 容器停止后自动删除容器，常用来测试
docker run -itd -v ~/work:/work --name=Ubuntu20.04-CUDA --gpus all nvidia/cuda:11.4.2-cudnn8-devel-ubuntu20.04

# Titan X
docker run --gpus all -itd --privileged=true -p 50097:22 --name voldor-imu \
       -v /home/data1/projects:/home/projects \
       -e DISPLAY=$DISPLAY \
       -e GDK_SCALE \
       -e GDK_DPI_SCALE \
       nvidia/cuda:11.4.0-devel-ubuntu18.04 /bin/bash
       

# A6000
docker run --gpus all -itd --privileged=true -p 50097:22 --name voldor-imu \
       -v /data/zhangjiguang/wzq/voldor-imu:/home/voldor-imu \
       -e DISPLAY=$DISPLAY \
       -e GDK_SCALE \
       -e GDK_DPI_SCALE \
       nvidia/cuda:11.4.0-devel-ubuntu18.04 /bin/bash

# 查看创建的容器ID
docker ps -a 

# 进入已创建的容器
docker attach [ID]

# 设置 root 密码
passwd

# 配置ssh
apt update
apt install openssh-server vim wget curl cmake

# 设置 ssh 配置文件
sudo vim /etc/ssh/sshd_config

Port 22
PermitRootLogin yes  
UsePAM no

# 
service ssh restart

# connect
ssh [email protected] -X -p 50096

# DISPLAY
export DISPLAY=10.1.16.227:0.0
echo $DISPLAY

# test
apt install x11-apps
xclock

docker commit 07da voldor-imu:v1

# tag
docker tag voldor-imu:v1 wzq11011/voldor-imu:v1

# push
docker push wzq11011/voldor-imu:v1

# 递归修改文件所有者
sudo chown -R sebas projects/

# 更改用户组
chgrp -R sebas projects/
git clone https://github.com/htkseason/VOLDOR.git
git clone https://github.com/mbrossar/ai-imu-dr.git

# python 3.6.9
python3

# pip
apt install python3-pip

pip3 install --upgrade pip

# ceres-solver
# http://ceres-solver.org/installation.html#linux
apt install cmake libgoogle-glog-dev libgflags-dev libatlas-base-dev libeigen3-dev libsuitesparse-dev

# boost
# https://blog.csdn.net/qq_41854911/article/details/119454212
apt install libboost-all-dev
docker exec -it 07da /bin/bash

Exception in thread Thread-29:
Traceback (most recent call last):
  File "/usr/lib/python3.6/threading.py", line 916, in _bootstrap_inner
    self.run()
  File "/usr/lib/python3.6/threading.py", line 864, in run
    self._target(*self._args, **self._kwargs)
  File "../slam_py/voldor_slam.py", line 718, in vo_thread
    cv2.imshow('tmpkf_depth', (self.basefocal*0.04)/self.frames[self.fid_cur_tmpkf].get_scaled_depth())
cv2.error: OpenCV(4.7.0) /tmp/pip-install-ftep5xuc/opencv-python_faa811634b06405b915df5c9319b9c00/opencv/modules/highgui/src/window.cpp:1272: error: (-2:Unspecified error) The function is not implemented. Rebuild the library with Windows, GTK+ 2.x or Cocoa support. If you are on Ubuntu or Debian, install libgtk2.0-dev and pkg-config, then re-run cmake or configure script in function 'cvShowImage'

# 查看 opencv-python 版本
pip show opencv-python

# 安装对应版本的 opencv-contrib
pip install opencv-contrib-python==4.7.0.72