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Ubuntu 18.04安装Docker CE及NVIDIA Container Toolkit步骤

Docker的好处之一,就是在Container里面可以随意瞎搞,不用担心弄崩Host的环境。

为了在docker中支持GPU,NVidia之前是弄了个nvidia-docker2,现在升级为NVIDIA Container Toolkit了。官方说法是"Usage of nvidia-docker2 packages are deprecated since NVIDIA GPUs are now natively supported as devices in the Docker runtime"。

Ubuntu 18.04安装Docker CE及NVIDIA Container Toolkit步骤

安装环境

  • OS:Ubuntu 18.04 64 bit
  • 显卡:NVidia GTX 2080 Ti x 2
  • CUDA:10.0
  • cnDNN:7.4

任务:安装Docker CE及NVIDIA Container Toolkit,以便后期开展深度学习

配置Docker源
# 更新源
$ sudo apt update

# 启用HTTPS
$ sudo apt install -y \
    apt-transport-https \
    ca-certificates \
    curl \
    gnupg-agent \
    software-properties-common

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

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

安装Docker CE

此刻Docker版本需要19.03,此后可能需要更新。
# 更新源
$ sudo apt update

# 安装Docker CE
$ sudo apt install -y docker-ce

如果这种方式安装失败,也有解决方案。
 报错时屏幕上会显示下载失败的deb文件,想办法下载下来,然后挨个手动安装就好。

此刻我需要下载的是下面三个文件,此后更新为当时最新版本即可:

  • containerd.io_1.2.6-3_amd64.deb
  • docker-ce-cli_19.03.0~3-0~ubuntu-bionic_amd64.deb
  • docker-ce_19.03.0~3-0~ubuntu-bionic_amd64.deb

手动依次安装:
$ sudo dpkg -i containerd.io_1.2.6-3_amd64.deb
$ sudo dpkg -i docker-ce-cli_19.03.0~3-0~ubuntu-bionic_amd64.deb
$ sudo dpkg -i docker-ce_19.03.0~3-0~ubuntu-bionic_amd64.deb

验证Docker CE

如果出现下面的内容,说明安装成功。
$ sudo docker run hello-world

Unable to find image 'hello-world:latest' locally
latest: Pulling from library/hello-world
1b930d010525: Pull complete
Digest: sha256:2557e3c07ed1e38f26e389462d03ed943586f744621577a99efb77324b0fe535
Status: Downloaded newer image for hello-world:latest

Hello from Docker!
This message shows that your installation appears to be working correctly.

To generate this message, Docker took the following steps:
 1. The Docker client contacted the Docker daemon.
 2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
    (amd64)
 3. The Docker daemon created a new container from that image which runs the
    executable that produces the output you are currently reading.
 4. The Docker daemon streamed that output to the Docker client, which sent it
    to your terminal.

To try something more ambitious, you can run an Ubuntu container with:
 $ docker run -it ubuntu bash

Share images, automate workflows, and more with a free Docker ID:
 https://hub.docker.com/

For more examples and ideas, visit:
 https://docs.docker.com/get-started/

配置nvidia-docker源
# 添加源
$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
$ curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
$ curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

# 安装并重启docker
$ sudo apt update && sudo apt install -y nvidia-container-toolkit
$ sudo systemctl restart docker

使用
# 在官方CUDA镜像上测试 nvidia-smi
$ sudo docker run --gpus all nvidia/cuda:9.0-base nvidia-smi

# 启动支持双GPU的容器
$ sudo docker run --gpus 2 nvidia/cuda:9.0-base nvidia-smi

# 指定GPU 1,运行容器
$ sudo docker run --gpus device=0 nvidia/cuda:9.0-base nvidia-smi

能看到显卡信息就说明OK了,当前image是基于Ubuntu 16.04的。

原文链接:https://www.linuxidc.com/Linux/2019-07/159613.htm

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