docker coentos7 tensorflow

1 cpu的设备识别检测

yum install pciutils lspci | grep NVIDIA00:03.0 3D controller: NVIDIA Corporation Device 1b38 (rev a1)

2 安装显卡驱动

wget http://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-repo-rhel7-8.0.61-1.x86_64.rpmrpm -Uvh cuda-repo-rhel7-8.0.61-1.x86_64.rpm依赖安装wget http://vault.centos.org/7.0.1406/updates/x86_64/Packages/kernel-devel-3.10.0-123.4.4.el7.x86_64.rpmwget http://vault.centos.org/7.0.1406/updates/x86_64/Packages/kernel-headers-3.10.0-123.4.4.el7.x86_64.rpmrpm -Uvh kernel-devel-3.10.0-123.4.4.el7.x86_64.rpmrpm -Uvh kernel-headers-3.10.0-123.4.4.el7.x86_64.rpmyum install cuda-8-0

3 查看gpu使用情况

nvidia-smiMon Nov 27 11:01:51 2017       +-----------------------------------------------------------------------------+| NVIDIA-SMI 384.81                 Driver Version: 384.81                    ||-------------------------------+----------------------+----------------------+| 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 P40           Off  | 00000000:00:03.0 Off |                    0 || N/A   23C    P0    45W / 250W |      0MiB / 22912MiB |      0%      Default |+-------------------------------+----------------------+----------------------++-----------------------------------------------------------------------------+| Processes:                                                       GPU Memory ||  GPU       PID   Type   Process name                             Usage      ||=============================================================================||  No running processes found                                                 |+-----------------------------------------------------------------------------+

4.测试GPU是否安装成功

cd /usr/local/cuda/binsh cuda-install-samples-8.0.sh ~/cuda-test/cd ~/cuda-test/NVIDIA_CUDA-8.0_Samplesmake./bin/x86_64/linux/release/deviceQuery 获取设备状态 ./bin/x86_64/linux/release/bandwidthTest 测试设备带宽

5.配置环境变量

PATH=$PATH:$HOME/bin:/usr/local/cuda/binLD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64/CUDA_HOME=/usr/local/cudaexport PATHexport LD_LIBRARY_PATHexport CUDA_HOME

6.安装cudnn

下载对应的：http://www.nvidia.cn/Download/index.aspx?lang=cn

7.安装docker

yum update curl -sSL https://get.docker.com/ | sh 报错： Delta RPMs disabled because /usr/bin/applydeltarpm not installed.查找包含的包：yum provides '*/applydeltarpm'安装：yum install deltarpmyum install -y nvidia-dockerdocker tensorflow image安装nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:latest-gpu

修改默认的存储位置

1.修改配置文件
/usr/lib/systemd/system/docker.service
/usr/lib/systemd/system/nvidia-docker.service

root@10-23-14-125 data]#cat /usr/lib/systemd/system/docker.service[Unit]Description=Docker Application Container EngineDocumentation=https://docs.docker.comAfter=network-online.target firewalld.serviceWants=network-online.target[Service]Type=notify# the default is not to use systemd for cgroups because the delegate issues still# exists and systemd currently does not support the cgroup feature set required# for containers run by docker#ExecStart=/usr/bin/dockerd#graph 指定存储位置，registry-mirror 加速镜像ExecStart=/usr/bin/dockerd --graph /data/docker-data/docker  --registry-mirror=https://u1qbyfsc.mirror.aliyuncs.comExecReload=/bin/kill -s HUP $MAINPID# Having non-zero Limit*s causes performance problems due to accounting overhead# in the kernel. We recommend using cgroups to do container-local accounting.LimitNOFILE=infinityLimitNPROC=infinityLimitCORE=infinity# Uncomment TasksMax if your systemd version supports it.# Only systemd 226 and above support this version.#TasksMax=infinityTimeoutStartSec=0# set delegate yes so that systemd does not reset the cgroups of docker containersDelegate=yes# kill only the docker process, not all processes in the cgroupKillMode=process# restart the docker process if it exits prematurelyRestart=on-failureStartLimitBurst=3StartLimitInterval=60s[Install]WantedBy=multi-user.target[root@10-23-14-125 data]# cat /usr/lib/systemd/system/nvidia-docker.service[Unit]Description=NVIDIA Docker pluginDocumentation=https://github.com/NVIDIA/nvidia-docker/wikiAfter=local-fs.target network.targetWants=docker.service[Service]Environment="SOCK_DIR=/data/docker-data/nvidia-docker"Environment="SPEC_FILE=/etc/docker/plugins/nvidia-docker.spec"#User=nvidia-dockerUser=rootPermissionsStartOnly=trueRestart=on-failureRestartSec=1TimeoutStartSec=0TimeoutStopSec=20ExecStart=/usr/bin/nvidia-docker-plugin -s $SOCK_DIRExecStartPost=/bin/sh -c '/bin/mkdir -p $( dirname $SPEC_FILE )'ExecStartPost=/bin/sh -c '/bin/echo unix://$SOCK_DIR/nvidia-docker.sock > $SPEC_FILE'ExecStopPost=/bin/rm -f $SPEC_FILE[Install]WantedBy=multi-user.target

2.数据盘建立软件

ln -s  /data/docker  /var/lib/docker

3 注意启动需要加 –privileged=true

docker run -it -d --privileged=true  xcartensorflow/xcartensorflow:new-gpu /bin/bash-privileged=true 加这个参数，否则nvida设备不会挂载上查看设备是否正确挂载ll /dev/nv*crw-rw-rw- 1 root root 195,   0 12月 18 17:59 /dev/nvidia0crw-rw-rw- 1 root root 195, 255 12月 18 17:59 /dev/nvidiactlcrw-rw-rw- 1 root root 247,   0 12月 18 17:59 /dev/nvidia-uvmcrw-rw-rw- 1 root root 247,   1 12月 18 17:59 /dev/nvidia-uvm-toolscrw------- 1 root root  10, 144 12月 18 17:59 /dev/nvram

参考:
https://hub.docker.com/r/tensorflow/tensorflow/
https://aur.archlinux.org/packages/nvidia-docker/