一键deepo|一键pytorch|tf环境

命令行输入，安装docker和nvidia-docker

curl -sSL https://get.docker.com/ | sh
# If you have nvidia-docker 1.0 installed: we need to remove it and all existing GPU containers
docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f
sudo apt-get purge -y nvidia-docker

# Add the package repositories
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

# Install nvidia-docker2 and reload the Docker daemon configuration
sudo apt-get install -y nvidia-docker2
sudo pkill -SIGHUP dockerd

# Test nvidia-smi with the latest official CUDA image
docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi
# about 2.4G need to download
sudo docker pull ufoym/deepo

1、启动命令：
sudo nvidia-docker run ufoym/deepo bash
2、将所有数据共享到docker：
sudo nvidia-docker run -it -v /home:/data -v /host/config:/config ufoym/deepo
bash
3、共享全部数据且共享进程（使用pytorch）：
sudo nvidia-docker run -it -v /home:/data -it --ipc=host ufoym/deepo bash
4、单独使用某一神经网络模型(tensorflow)：
sudo nvidia-docker run -it -v /home:/data --ipc=host ufoym/deepo:tensorflow
bash
5、清理所有处于终止状态的容器:
docker container prune
6、显示创建过的docker：
sudo docker container ls -a
7、退出不关闭docker：
退出时不要用ctrl+c 或者输入exit，用ctrl+P+Q