这篇文章给大家分享的是有关docker19.03如何使用NVIDIA显卡的内容。小编觉得挺实用的，因此分享给大家做个参考，一起跟随小编过来看看吧。
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docker19.03使用NVIDIA显卡
前言
2019年7月的docker 19.03已经正式发布了，这次发布对我来说有两大亮点。
1，就是docker不需要root权限来启动喝运行了
2，就是支持GPU的增强功能，我们在docker里面想读取nvidia显卡再也不需要额外的安装nvidia-docker了
安装nvidia驱动
确认已检测到NVIDIA卡:
$ lspci -vv | grep -i nvidia
00:04.0 3D controller: NVIDIA Corporation GP100GL [Tesla P100 PCIe 16GB] (rev a1)
Subsystem: NVIDIA Corporation GP100GL [Tesla P100 PCIe 16GB]
Kernel modules: nvidiafb
这里不再详细介绍：如果不知道请移步ubuntu离线安装TTS服务
安装NVIDIA Container Runtime
$ cat nvidia-container-runtime-script.sh
curl -s -L https://nvidia.github.io/nvidia-container-runtime/gpgkey | \
sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-container-runtime/$distribution/nvidia-container-runtime.list | \
sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get update
执行脚本
sh nvidia-container-runtime-script.sh
OK
deb https://nvidia.github.io/libnvidia-container/ubuntu18.04/$(ARCH) /
deb https://nvidia.github.io/nvidia-container-runtime/ubuntu18.04/$(ARCH) /
Hit:1 http://archive.canonical.com/ubuntu bionic InRelease
Get:2 https://nvidia.github.io/libnvidia-container/ubuntu18.04/amd64 InRelease [1139 B]
Get:3 https://nvidia.github.io/nvidia-container-runtime/ubuntu18.04/amd64 InRelease [1136 B]
Hit:4 http://security.ubuntu.com/ubuntu bionic-security InRelease
Get:5 https://nvidia.github.io/libnvidia-container/ubuntu18.04/amd64 Packages [4076 B]
Get:6 https://nvidia.github.io/nvidia-container-runtime/ubuntu18.04/amd64 Packages [3084 B]
Hit:7 http://us-east4-c.gce.clouds.archive.ubuntu.com/ubuntu bionic InRelease
Hit:8 http://us-east4-c.gce.clouds.archive.ubuntu.com/ubuntu bionic-updates InRelease
Hit:9 http://us-east4-c.gce.clouds.archive.ubuntu.com/ubuntu bionic-backports InRelease
Fetched 9435 B in 1s (17.8 kB/s)
Reading package lists... Done
$ apt-get install nvidia-container-runtime
Reading package lists... Done
Building dependency tree
Reading state information... Done
The following packages were automatically installed and are no longer required:
grub-pc-bin libnuma1
Use 'sudo apt autoremove' to remove them.
The following additional packages will be installed:
Get:1 https://nvidia.github.io/libnvidia-container/ubuntu18.04/amd64 libnvidia-container1 1.0.2-1 [59.1 kB]
Get:2 https://nvidia.github.io/libnvidia-container/ubuntu18.04/amd64 libnvidia-container-tools 1.0.2-1 [15.4 kB]
Get:3 https://nvidia.github.io/nvidia-container-runtime/ubuntu18.04/amd64 nvidia-container-runtime-hook 1.4.0-1 [575 kB]
...
Unpacking nvidia-container-runtime (2.0.0+docker18.09.6-3) ...
Setting up libnvidia-container1:amd64 (1.0.2-1) ...
Setting up libnvidia-container-tools (1.0.2-1) ...
Processing triggers for libc-bin (2.27-3ubuntu1) ...
Setting up nvidia-container-runtime-hook (1.4.0-1) ...
Setting up nvidia-container-runtime (2.0.0+docker18.09.6-3) ...
which nvidia-container-runtime-hook
/usr/bin/nvidia-container-runtime-hook
安装docker-19.03
# step 1: 安装必要的一些系统工具
yum install -y yum-utils device-mapper-persistent-data lvm2
# Step 2: 添加软件源信息
yum-config-manager --add-repo http://mirrors.aliyun.com/docker-ce/linux/centos/docker-ce.repo
# Step 3: 更新并安装 Docker-CE
yum makecache fast
yum -y install docker-ce-19.03.2
# Step 4: 开启Docker服务
systemctl start docker && systemctl enable docker
验证docker版本是否安装正常
$ docker version
Client: Docker Engine - Community
Version: 19.03.2
API version: 1.40
Go version: go1.12.8
Git commit: 6a30dfc
Built: Thu Aug 29 05:28:55 2019
OS/Arch: linux/amd64
Experimental: false
Server: Docker Engine - Community
Engine:
Version: 19.03.2
API version: 1.40 (minimum version 1.12)
Go version: go1.12.8
Git commit: 6a30dfc
Built: Thu Aug 29 05:27:34 2019
OS/Arch: linux/amd64
Experimental: false
containerd:
Version: 1.2.6
GitCommit: 894b81a4b802e4eb2a91d1ce216b8817763c29fb
runc:
Version: 1.0.0-rc8
GitCommit: 425e105d5a03fabd737a126ad93d62a9eeede87f
docker-init:
Version: 0.18.0
GitCommit: fec3683
验证下-gpus选项
$ docker run --help | grep -i gpus
--gpus gpu-request GPU devices to add to the container ('all' to pass all GPUs)运行利用GPU的Ubuntu容器
$ docker run -it --rm --gpus all ubuntu nvidia-smi
Unable to find image 'ubuntu:latest' locally
latest: Pulling from library/ubuntu
f476d66f5408: Pull complete
8882c27f669e: Pull complete
d9af21273955: Pull complete
f5029279ec12: Pull complete
Digest: sha256:d26d529daa4d8567167181d9d569f2a85da3c5ecaf539cace2c6223355d69981
Status: Downloaded newer image for ubuntu:latest
Tue May 7 15:52:15 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.116 Driver Version: 390.116 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla P4 Off | 00000000:00:04.0 Off | 0 |
| N/A 39C P0 22W / 75W | 0MiB / 7611MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
:~$
故障排除
您是否遇到以下错误消息：
$ docker run -it --rm --gpus all debian
docker: Error response from daemon: linux runtime spec devices: could not select device driver "" with capabilities: [[gpu]].
上述错误意味着Nvidia无法正确注册Docker。它实际上意味着驱动程序未正确安装在主机上。这也可能意味着安装了nvidia容器工具而无需重新启动docker守护程序：您需要重新启动docker守护程序。
我建议你回去验证是否安装了nvidia-container-runtime或者重新启动Docker守护进程。
列出GPU设备
$ docker run -it --rm --gpus all ubuntu nvidia-smi -L
GPU 0: Tesla P4 (UUID: GPU-fa974b1d-3c17-ed92-28d0-805c6d089601)
$ docker run -it --rm --gpus all ubuntu nvidia-smi --query-gpu=index,name,uui
d,serial --format=csv
index, name, uuid, serial
0, Tesla P4, GPU-fa974b1d-3c17-ed92-28d0-805c6d089601, 0325017070224
待验证，因为我现在没有GPU机器---已经验证完成，按照上述操作可以在docker里面成功的驱动nvidia显卡
感谢各位的阅读！关于“docker19.03如何使用NVIDIA显卡”这篇文章就分享到这里了，希望以上内容可以对大家有一定的帮助，让大家可以学到更多知识，如果觉得文章不错，可以把它分享出去让更多的人看到吧！
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