docker19.03如何使用NVIDIA显卡
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。
网站建设哪家好，找创新互联！专注于网页设计、网站建设、微信开发、微信小程序、集团企业网站建设等服务项目。为回馈新老客户创新互联还提供了衡阳免费建站欢迎大家使用！
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！
网站题目：docker19.03如何使用NVIDIA显卡
文章起源：http://scjbc.cn/article/gcheog.html
