如何在一般docker container 裡用USB硬碟?

首先拿到USB硬碟mount完的路徑

device 是 /dev/usb/bus/001/014

但是我們要拿的是可以存取的資料夾路徑

譬如 /media/lab734/Kingston

在docker create時加上 -v /media/lab734/Kingston:/mnt/usb/Kingston

container裡面會有/mnt/usb/Kingston 裡面就是USB硬碟的資料了

一般docker 操作

去找到你要複製的image 和TAG

tensorflow/tensorflow:2.0.0b1-gpu-py3-jupyter

創一個新的container

sudo nvidia-docker create

--name lab734docker //名子

-p 7777:8888 //本機跟虛擬機的port-forwaording
--privileged=true //開放真的root權限

--device /dev/nvidia-uvm:/dev/nvidia-uvm //把顯卡映射進來

-it //來個可以互動的命令列 方便之後操作

-v /media/lab734/Kingston:/mnt/usb/Kingston

//映射資料夾 這裡是用上面的USB硬碟

tensorflow/tensorflow:2.0.0b1-gpu-py3-jupyter

//來源的image

sudo nvidia-docker create --name lab734docker -p 7777:8888 --privileged=true --device /dev/nvidia-uvm:/dev/nvidia-uvm --device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidiactl:/dev/nvidiactl -it -v /home/lab734:/home/usr tensorflow/tensorflow:2.0.0b1-gpu-py3-jupyter 

docker start lab734docker

//開機~

docker ps//現在開了哪幾台

docker exec -it lab734docker /bin/bash
//進入shell打指令 ctrl +D 退出

docker stop lab734docker
//停掉

docker rm lab734docker

//移除container 
apt-get install update

pip3 install --upgrade pip
pip install matplotlib pillow opencv-python==3.4.2.16
apt-get install libgtk2.0-dev


docker exec -it CH /bin/bash

全新電腦灌ubuntu 18.04和一般深度學習的DOCKER

先到ubuntu 官網抓18.04

灌到USB裡 重灌電腦

網路設定好

sudo apt-get update

sudo apt-get install openssh-server

sudo reboot

sudo apt install gcc



sudo apt install build-essential
去官網載新版的driver
sudo chmod +x N@@@@@
./N@@@@@@ 
sudo reboot
sudo apt-get install \
    apt-transport-https \
    ca-certificates \
    curl \
    gnupg-agent \
    software-properties-common

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

sudo add-apt-repository \
   "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
   $(lsb_release -cs) \
   stable"

sudo apt-get install docker-ce docker-ce-cli containerd.io

sudo docker run hello-world







curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
  sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
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-docker2
sudo pkill -SIGHUP dockerd



sudo usermod -a -G docker $USER



docker run --runtime=nvidia --rm nvidia/cuda:9.0-base nvidia-smi




sudo nvidia-docker create --name lab734docker  -p 7777:8888 -e PASSWORD=pass --privileged=true --device /dev/nvidia-uvm:/dev/nvidia-uvm --device /dev/nvidia0:/dev/nvidia0  --device /dev/nvidiactl:/dev/nvidiactl -it -v /home/lab734:/home/usr tensorflow/tensorflow:2.0.0b1-gpu-py3-jupyter 


docker exec -it lab734docker /bin/bash


 jupyter notebook password 
top
kill 1
docker start lab734docker