Docker容器中使用GPU

背景

容器封装了应用程序的依赖项，以提供可重复和可靠的应用程序和服务执行，而无需整个虚拟机的开销。如果您曾经花了一天的时间为一个科学或深度学习应用程序提供一个包含大量软件包的服务器，或者已经花费数周的时间来确保您的应用程序可以在多个 linux 环境中构建和部署，那么 Docker 容器非常值得您花费时间。

安装添加docker源

[root@localhost ~]# sudo yum-config-manager --add-repo=https://download.docker.com/linux/centos/docker-ce.repo

Loaded plugins: fastestmirror, langpacks

adding repo from: https://download.docker.com/linux/centos/docker-ce.repo

grabbing file https://download.docker.com/linux/centos/docker-ce.repo to /etc/yum.repos.d/docker-ce.repo

repo saved to /etc/yum.repos.d/docker-ce.repo

[root@localhost ~]#

[root@localhost ~]# cat /etc/yum.repos.d/docker-ce.repo

[docker-ce-stable]

name=Docker CE Stable - $basearch

baseurl=https://download.docker.com/linux/centos/$releasever/$basearch/stable

enabled=1

gpgcheck=1

gpgkey=https://download.docker.com/linux/centos/gpg

[docker-ce-stable-debuginfo]

name=Docker CE Stable - Debuginfo $basearch

baseurl=https://download.docker.com/linux/centos/$releasever/debug-$basearch/stable

enabled=0

gpgcheck=1

gpgkey=https://download.docker.com/linux/centos/gpg

[docker-ce-stable-source]

name=Docker CE Stable - Sources

baseurl=https://download.docker.com/linux/centos/$releasever/source/stable

enabled=0

gpgcheck=1

gpgkey=https://download.docker.com/linux/centos/gpg

[docker-ce-test]

name=Docker CE Test - $basearch

baseurl=https://download.docker.com/linux/centos/$releasever/$basearch/test

enabled=0

gpgcheck=1

gpgkey=https://download.docker.com/linux/centos/gpg

[docker-ce-test-debuginfo]

name=Docker CE Test - Debuginfo $basearch

baseurl=https://download.docker.com/linux/centos/$releasever/debug-$basearch/test

enabled=0

gpgcheck=1

gpgkey=https://download.docker.com/linux/centos/gpg

[docker-ce-test-source]

name=Docker CE Test - Sources

baseurl=https://download.docker.com/linux/centos/$releasever/source/test

enabled=0

gpgcheck=1

gpgkey=https://download.docker.com/linux/centos/gpg

[docker-ce-nightly]

name=Docker CE Nightly - $basearch

baseurl=https://download.docker.com/linux/centos/$releasever/$basearch/nightly

enabled=0

gpgcheck=1

gpgkey=https://download.docker.com/linux/centos/gpg

[docker-ce-nightly-debuginfo]

name=Docker CE Nightly - Debuginfo $basearch

baseurl=https://download.docker.com/linux/centos/$releasever/debug-$basearch/nightly

enabled=0

gpgcheck=1

gpgkey=https://download.docker.com/linux/centos/gpg

[docker-ce-nightly-source]

name=Docker CE Nightly - Sources

baseurl=https://download.docker.com/linux/centos/$releasever/source/nightly

enabled=0

gpgcheck=1

gpgkey=https://download.docker.com/linux/centos/gpg

[root@localhost ~]#

下载安装包

[root@localhost ~]# cd docker

[root@localhost docker]#

[root@localhost docker]# repotrack docker-ce

安装docker 并设置开机自启

[root@localhost docker]# yum install ./*

[root@localhost docker]# systemctl  start docker

[root@localhost docker]#

[root@localhost docker]# systemctl  enable docker

Created symlink from /etc/systemd/system/multi-user.target.wants/docker.service to /usr/lib/systemd/system/docker.service.

[root@localhost docker]#

配置nvidia-docker的源

[root@localhost docker]# distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \

>    && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo | sudo tee /etc/yum.repos.d/nvidia-docker.repo

[root@localhost docker]# cat /etc/yum.repos.d/nvidia-docker.repo

[libnvidia-container]

name=libnvidia-container

baseurl=https://nvidia.github.io/libnvidia-container/stable/centos7/$basearch

repo_gpgcheck=1

gpgcheck=0

enabled=1

gpgkey=https://nvidia.github.io/libnvidia-container/gpgkey

sslverify=1

sslcacert=/etc/pki/tls/certs/ca-bundle.crt

[libnvidia-container-experimental]

name=libnvidia-container-experimental

baseurl=https://nvidia.github.io/libnvidia-container/experimental/centos7/$basearch

repo_gpgcheck=1

gpgcheck=0

enabled=0

gpgkey=https://nvidia.github.io/libnvidia-container/gpgkey

sslverify=1

sslcacert=/etc/pki/tls/certs/ca-bundle.crt

[nvidia-container-runtime]

name=nvidia-container-runtime

baseurl=https://nvidia.github.io/nvidia-container-runtime/stable/centos7/$basearch

repo_gpgcheck=1

gpgcheck=0

enabled=1

gpgkey=https://nvidia.github.io/nvidia-container-runtime/gpgkey

sslverify=1

sslcacert=/etc/pki/tls/certs/ca-bundle.crt

[nvidia-container-runtime-experimental]

name=nvidia-container-runtime-experimental

baseurl=https://nvidia.github.io/nvidia-container-runtime/experimental/centos7/$basearch

repo_gpgcheck=1

gpgcheck=0

enabled=0

gpgkey=https://nvidia.github.io/nvidia-container-runtime/gpgkey

sslverify=1

sslcacert=/etc/pki/tls/certs/ca-bundle.crt

[nvidia-docker]

name=nvidia-docker

baseurl=https://nvidia.github.io/nvidia-docker/centos7/$basearch

repo_gpgcheck=1

gpgcheck=0

enabled=1

gpgkey=https://nvidia.github.io/nvidia-docker/gpgkey

sslverify=1

sslcacert=/etc/pki/tls/certs/ca-bundle.crt

[root@localhost docker]#

安装下载nvidia-docker

[root@localhost ~]# mkdir nvidia-docker2

[root@localhost ~]# cd nvidia-docker2

[root@localhost nvidia-docker2]# yum update -y

[root@localhost nvidia-docker2]# repotrack nvidia-docker2

[root@localhost nvidia-docker2]# yum install ./*

[root@localhost ~]# mkdir nvidia-container-toolkit

[root@localhost ~]# cd nvidia-container-toolkit

[root@localhost nvidia-container-toolkit]# repotrack nvidia-container-toolkit

[root@ai-rd nvidia-container-toolkit]# yum install ./*

下载镜像，并保存

[root@localhost ~]# docker pull nvidia/cuda:11.0-base

11.0-base: Pulling from nvidia/cuda

54ee1f796a1e: Pull complete

f7bfea53ad12: Pull complete

46d371e02073: Pull complete

b66c17bbf772: Pull complete

3642f1a6dfb3: Pull complete

e5ce55b8b4b9: Pull complete

155bc0332b0a: Pull complete

Digest: sha256:774ca3d612de15213102c2dbbba55df44dc5cf9870ca2be6c6e9c627fa63d67a

Status: Downloaded newer image for nvidia/cuda:11.0-base

docker.io/nvidia/cuda:11.0-base

[root@localhost ~]#

[root@localhost ~]# docker images

REPOSITORY    TAG         IMAGE ID       CREATED         SIZE

nvidia/cuda   11.0-base   2ec708416bb8   15 months ago   122MB

[root@localhost ~]#

[root@localhost ~]# docker save -o cuda-11.0.tar nvidia/cuda:11.0-base

[root@localhost ~]#

[root@localhost ~]# ls cuda-11.0.tar

cuda-11.0.tar

[root@localhost ~]#

在要测试的服务器上导入镜像

[root@ai-rd cby]# docker load -i cuda-11.0.tar

2ce3c188c38d: Loading layer [==================================================>]  75.23MB/75.23MB

ad44aa179b33: Loading layer [==================================================>]  1.011MB/1.011MB

35a91a75d24b: Loading layer [==================================================>]  15.36kB/15.36kB

a4399aeb9a0e: Loading layer [==================================================>]  3.072kB/3.072kB

fa39d0e9f3dc: Loading layer [==================================================>]  18.84MB/18.84MB

232fb43df6ad: Loading layer [==================================================>]  30.08MB/30.08MB

0da51e35db05: Loading layer [==================================================>]  22.53kB/22.53kB

Loaded image: nvidia/cuda:11.0-base

[root@ai-rd cby]#

[root@ai-rd cby]# docker images | grep cuda

nvidia/cuda                          11.0-base   2ec708416bb8   15 months ago   122MB

[root@ai-rd cby]#

安装升级内核

[root@ai-rd cby]# yum install kernel-headers

[root@ai-rd cby]# yum install kernel-devel

[root@ai-rd cby]# yum update kernel*

禁用模块，并升级boot

[root@ai-rd cby]# vim /etc/modprobe.d/blacklist-nouveau.conf

[root@ai-rd cby]# cat /etc/modprobe.d/blacklist-nouveau.conf

blacklist nouveau

options nouveau modeset=0

[root@ai-rd cby]#

[root@ai-rd cby]# mv /boot/initramfs-$(uname -r).img /boot/initramfs-$(uname -r).img.bak

[root@ai-rd cby]# sudo dracut -v /boot/initramfs-$(uname -r).img $(uname -r)

下载驱动并安装

[root@localhost ~]# wget https://cn.download.nvidia.cn/tesla/450.156.00/NVIDIA-Linux-x86_64-450.156.00.run

[root@ai-rd cby]# chmod +x NVIDIA-Linux-x86_64-450.156.00.run

[root@ai-rd cby]# ./NVIDIA-Linux-x86_64-450.156.00.run

配置docker

[root@ai-rd ~]# vim /etc/docker/daemon.json

[root@ai-rd ~]# cat /etc/docker/daemon.json

{

    "runtimes": {

        "nvidia": {

            "path": "nvidia-container-runtime",

            "runtimeArgs": []

        }

    }

}

[root@ai-rd ~]#

[root@ai-rd ~]# systemctl daemon-reload

[root@ai-rd ~]#

[root@ai-rd ~]#

[root@ai-rd ~]#

[root@ai-rd ~]# systemctl  restart docker

[root@ai-rd ~]#

测试docker中的调用情况

[root@ai-rd ~]#

[root@ai-rd ~]# sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi

Tue Nov 23 06:03:04 2021

+-----------------------------------------------------------------------------+

| NVIDIA-SMI 450.156.00   Driver Version: 450.156.00   CUDA Version: 11.0     |

|-------------------------------+----------------------+----------------------+

| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |

| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |

|                               |                      |               MIG M. |

|===============================+======================+======================|

|   0  Tesla T4            Off  | 00000000:86:00.0 Off |                    0 |

| N/A   90C    P0    34W /  70W |      0MiB / 15109MiB |      6%      Default |

|                               |                      |                  N/A |

+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+

| Processes:                                                                  |

|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |

|        ID   ID                                                   Usage      |

|=============================================================================|

|  No running processes found                                                 |

+-----------------------------------------------------------------------------+

[root@ai-rd ~]#

https://blog.csdn.net/qq_33921750

https://my.oschina.net/u/3981543

https://www.zhihu.com/people/chen-bu-yun-2

https://segmentfault.com/u/hppyvyv6/articles

https://juejin.cn/user/3315782802482007

https://space.bilibili.com/352476552/article

https://cloud.tencent.com/developer/column/93230

知乎、CSDN、开源中国、思否、掘金、哔哩哔哩、腾讯云

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