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