docker19.03使用NVIDIA显卡

张琳兮

发布于 2019-09-16 11:09:40

6.5K0

发布于 2019-09-16 11:09:40

docker19.03使用NVIDIA显卡

作者： 张首富
时间： 2019-09-06

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

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原始发表：2019-09-13 ，如有侵权请联系 cloudcommunity@tencent.com 删除

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docker19.03使用NVIDIA显卡

docker19.03使用NVIDIA显卡

docker19.03使用NVIDIA显卡

前言

安装nvidia驱动

安装NVIDIA Container Runtime

执行脚本

安装docker-19.03

验证docker版本是否安装正常

验证下`-gpus`选项

运行利用GPU的Ubuntu容器

故障排除

列出GPU设备

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

docker19.03使用NVIDIA显卡

docker19.03使用NVIDIA显卡

docker19.03使用NVIDIA显卡

前言

安装nvidia驱动

安装NVIDIA Container Runtime

执行脚本

安装docker-19.03

验证docker版本是否安装正常

验证下-gpus选项

运行利用GPU的Ubuntu容器

故障排除

列出GPU设备

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

验证下`-gpus`选项