通过docker的pytorch镜像打包自定义环境

环境：Ubuntu16.04， TITAN V，CUDA9.0

安装Docker

sudo apt-get install apt-transport-https ca-certificates curl software-properties-common
sudo 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 $(lsb_release -cs) stable"
sudo curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
sudo 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 docker-ce
sudo apt-get install -y nvidia-docker2

设置阿里云镜像加速器

sudo vi /etc/docker/daemon.json

从https://cr.console.aliyun.com找到自己的镜像加速器

{
    "runtimes": {
        "nvidia": {
            "path": "nvidia-container-runtime",
            "runtimeArgs": []
        }
    },
    "registry-mirrors":["https://.mirror.aliyuncs.com"]
}

启动docker

sudo systemctl daemon-reload
sudo systemctl restart docker

拉取pytorch镜像

从https://hub.docker.com/查找需要的镜像，例如我需要的是cuda9.0 pytorch4.1

sudo docker pull pytorch/pytorch:0.4.1-cuda9-cudnn7-runtime

创建并进入容器

sudo nvidia-docker run --rm -ti -v /path/to/some/folder:/workspace --ipc=host 7b329a33d981 /bin/bash

打包新镜像

在容器内做修改后打包镜像并压缩

sudo docker commit -a "yfraquelle"  ioid_env:v1
sudo docker save -o yfraquelle_ioid_env_v1.tar ioid_env:v1

加载新镜像

sudo docker load -i yfraquelle_ioid_env_v1.tar

参考链接：

https://www.jianshu.com/p/5b99bc9b0c64

https://zhuanlan.zhihu.com/p/31742065

https://www.pythonf.cn/read/112154

https://discuss.pytorch.org/t/how-to-use-pytorch-docker-image/15929

https://blog.csdn.net/sunmingyang1987/article/details/104555190