Docker容器中使用GPU

背景

容器封裝了應用程式的依賴項，以提供可重複和可靠的應用程式和服務執行，而無需整個虛拟機的開銷。如果您曾經花了一天的時間為一個科學或深度學習應用程式提供一個包含大量軟體包的伺服器，或者已經花費數周的時間來確定您的應用程式可以在多個 linux 環境中建構和部署，那麼 Docker 容器非常值得您花費時間。

安裝添加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 ~]#

下載下傳安裝包

[root@localhost ~]# cd docker
[root@localhost docker]#
[root@localhost docker]# repotrack docker-ce

安裝docker 并設定開機自啟

[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]#

安裝下載下傳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 ./*

下載下傳鏡像，并儲存

[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 ~]#

在要測試的伺服器上導入鏡像

[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]#

安裝更新核心

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

禁用子產品，并更新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)

下載下傳驅動并安裝

[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 ~]#

測試docker中的調用情況

[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 ~]#

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