Linux Docker容器配置

1. Installing the NVIDIA Container Toolkit

Installing the NVIDIA Container Toolkit — container-toolkit 1.14.1 documentation
a. Configure the repository

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list \
  && \
    sudo apt-get update

b. Install the NVIDIA Container Toolkit packages:

sudo apt-get install -y nvidia-container-toolkit

c. Configuring Docker

Configure the container runtime by using the nvidia-ctk command:

sudo nvidia-ctk runtime configure --runtime=docker

The nvidia-ctk command modifies the /etc/docker/daemon.json file on the host. The file is updated so that Docker can use the NVIDIA Container Runtime.

Restart the Docker daemon:

sudo systemctl restart docker

2. docker hub 镜像

a. 添加国内镜像

vi /etc/docker/daemon.json

{
    "runtimes": {
        "nvidia": {
            "args": [],
            "path": "nvidia-container-runtime"
        }
    },
    "registry-mirrors":[
            "https://mirror.ccs.tencentyun.com",
            "http://registry.docker-cn.com",
            "http://docker.mirrors.ustc.edu.cn",
            "http://hub-mirror.c.163.com",
            "https://3laho3y3.mirror.aliyuncs.com",
            "http://f1361db2.m.daocloud.io",
            "https://docker.mirrors.sjtug.sjtu.edu.cn",
            "https://docker.nju.edu.cn",
            "https://dockerproxy.com",
            "https://mirror.baidubce.com"
    ],
    "insecure-registries":[
            "registry.docker-cn.com",
            "docker.mirrors.ustc.edu.cn"
    ],
    "debug":true,
    "experimental":true
}

b. 拉取镜像

sudo docker pull nvidia/cuda:11.7.1-cudnn8-devel-ubuntu20.04

需要等待较长时间1-2小时。

c. 创建容器

docker run -it -d --gpus all --name NLP_env --hostname NLP_env --shm-size 8g -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video -v /home/modeldata:/containerdata -p 6667:22 --restart always nvidia/cuda:11.7.1-cudnn8-devel-ubuntu20.04 /bin/bash

-d 后台运行

--gpus all 选择所有gpu

--name 容器名称

--hostname 主机名称

--shm-size 共享内存

-e 环境设置 这里NVIDIA_DRIVER_CAPABILITIES主要保证容器里能够查看并且使用显卡

-v 目录映射（宿主机：容器内）

-p 端口映射（宿主机：容器内）

--restart always 重启启动

运行

docker ps -a

显示

测试通过

docker exec -it NLP_env nvidia-smi

查看显卡是否在容器内运行正常。

d. 进入容器

docker exec -it NLP_env bash

e. 安装openssh-server

apt update
apt install openssh-server
apt install vim
vim /etc/ssh/sshd_config

将sshd_config内容修改PermitRootLogin yes

service ssh restart
passwd

输入登录密码并确认。

之后可以通过ssh，登录IP，端口号6667,账号root，密码登录即可。