本文部分内容摘录:http://www.cnblogs.com/xuxinkun/p/5983633.html
Docker安装
Docker安装过程需要使用root权限， 主要有两种安装方式：
1、apt,yum方式安装，需要配置apt或yum的仓库
2、脚本安装：curl安装
阿里云加速器设置
针对1.10以上版本，您可以通过修改daemon配置文件/etc/docker/daemon.json来使用加速器：
sudo mkdir -p /etc/docker
sudo tee /etc/docker/daemon.json <<-‘EOF’
{
“registry-mirrors”: [“https://fird1mfg.mirror.aliyuncs.com“]
}
EOF
sudo systemctl daemon-reload
sudo systemctl restart docker
其中[“https://fird1mfg.mirror.aliyuncs.com“]为阿里云申请的加速镜像地址
NVIDIA-Docker安装
Prerequisties:
GNU/Linuxx86_64 with kernel version > 3.10
Docker >= 1.9 (officialdocker-engine,docker-ceordocker-eeonly)
NVIDIA GPU with Architecture > Fermi (2.1)
NVIDIA drivers >= 340.29 with binarynvidia-modprobe(驱动版本与CUDA计算能力相关)
CUDA与NVIDIA driver安装:
处理NVIDIA-Docker依赖项NVIDIA drivers >= 340.29 with binarynvidia-modprobe要求.
根据显卡，下载对应版本的CUDA并进行安装.
NVIDIA-Docker安装:
#Install nvidia-docker and nvidia-docker-plugin
#Test nvidia-smi
先运行插件
nohup nvidia-docker-plugin &
再在容器里启动nvidia-smi
nvidia-docker run –rm nvidia/cuda nvidia-smi
备注：可能由于selinux原因，上述命令出现找不到路径问题，改成下面命令：
nvidia-docker run -ti --rm --privileged=true nvidia/cuda nvidia-smi
或：
docker run -ti `curl -s http://localhost:3476/v1.0/docker/cli` --rm --privileged=true nvidia/cuda nvidia-smi
Tensorflow镜像使用
tensorflow gpu支持
tensorflow gpu in docker
docker可以通过提供gpu设备到容器中。nvidia官方提供了nvidia-docker的一种方式，其用nvidia-docker的命令行代替了docker的命令行来使用GPU。
nvidia-docker run -it -p8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu
这种方式对于docker侵入较多，因此nvidia还提供了一种nvidia-docker-plugin的方式。其使用流程如下：
首先在宿主机启动nvidia-docker-plugin：
[root@A01-R06-I184-22nvidia-docker]# ./nvidia-docker-plugin
./nvidia-docker-plugin |2016/10/1000:01:12Loading NVIDIA unified memory
./nvidia-docker-plugin |2016/10/1000:01:12Loading NVIDIA management library
./nvidia-docker-plugin |2016/10/1000:01:17Discovering GPU devices
./nvidia-docker-plugin |2016/10/1000:01:18Provisioning volumes at/var/lib/nvidia-docker/volumes
./nvidia-docker-plugin |2016/10/1000:01:18Serving plugin API at/run/docker/plugins
./nvidia-docker-plugin |2016/10/1000:01:18Serving remote API at localhost:3476
可以看到nvidia-docker-plugin监听了3486端口。然后在宿主机上运行docker run -ti curl -s http://localhost:3476/v1.0/docker/cli-p 8890:8888 gcr.io/tensorflow/tensorflow:latest-gpu /bin/bash命令以创建tensorflow的GPU容器。并可以在容器中验证是否能正常import tensorflow。
[root@A01-R06-I184-22~]# docker run -ti `curl -s http://localhost:3476/v1.0/docker/cli` -p 8890:8888 gcr.io/tensorflow/tensorflow:latest-gpu
/bin/bashroot@7087e1f99062:/notebooks# python
Python2.7.6(default, Jun222015,17:58:13) [GCC4.8.2] on linux2
Type"help","copyright","credits"or"license"formore information.>>> import tensorflow
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcuda.so.1locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcurand.so locally>>>
nvidia-docker-plugin工作原理
是其提供了一个API
curl -s http://localhost:3476/v1.0/docker/cli
--volume-driver=nvidia-docker --volume=nvidia_driver_352.39:/usr/local/nvidia:ro--device=/dev/nvidiactl--device=/dev/nvidia-uvm --device=/dev/nvidia0--device=/dev/nvidia1 --device=/dev/nvidia2 --device=/dev/nvidia3
