技术教程 编程教程 Docker容器中使用GPU 正文

Docker容器中使用GPU

2023-06-14,,

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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