每次换了个地方就要重新配置自己的开发环境那是特别蛋疼的，尤其是要弄到服务器跑的时候，不小心把环境弄崩了是非常惨的。
下载tensorflow-gpu版本的源
docker pull daocloud.io/daocloud/tensorflow:0.11.0-gpu编辑方便的脚本文件启动docker
先查下你有几个GPU设备
[root@XXX ~]# ls -la /dev | grep nvidia
crw-rw-rw-. 1 root root 195, 0 Sep 16 13:49 nvidia0
crw-rw-rw-. 1 root root 195, 255 Sep 16 13:49 nvidiactl
crw-rw-rw-. 1 root root 247, 0 Sep 16 13:54 nvidia-uvm然后再查你的docker镜像
y@y:~$ sudo docker images
[sudo] password for y:
REPOSITORY TAG IMAGE ID CREATED SIZE
daocloud.io/daocloud/tensorflow 0.11.0-gpu dd645f420f1d 8 weeks ago 2.713 GB
daocloud.io/daocloud/tensorflow 0.10.0-devel-gpu fa886c09638d 3 months ago 5.014 GB
hello-world 然后就可以启动咯
sudo docker run -ti -v /home/:/mnt/home --privileged=true --device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidiactl:/dev/nvidiactl --device /dev/nvidia-uvm:/dev/nvidia-uvm daocloud.io/daocloud/tensorflow:0.11.0-gpu /bin/bash上面这句有点长把它写到docker.sh文件，然后
sh docker.sh完成。 上面的意思是把本地的/home映射到docker的/mnt目录
以及各种显卡设备也映射进去
进去之后
别急着用tensorflow，可能会报错，因为我发现LD_LIBLABRARY_PATH环境变量设置的不对。但是又没有vim。于是先更新软件源。把软件源文件放到本机的/home，再去docker的/mnt/home里面复制到
/etc/apt/sources.listdeb http://mirrors.aliyun.com/ubuntu/ trusty main multiverse restricted universe
deb http://mirrors.aliyun.com/ubuntu/ trusty-backports main multiverse restricted universe
deb http://mirrors.aliyun.com/ubuntu/ trusty-proposed main multiverse restricted universe
deb http://mirrors.aliyun.com/ubuntu/ trusty-security main multiverse restricted universe
deb http://mirrors.aliyun.com/ubuntu/ trusty-updates main multiverse restricted universe
deb-src http://mirrors.aliyun.com/ubuntu/ trusty main multiverse restricted universe
deb-src http://mirrors.aliyun.com/ubuntu/ trusty-backports main multiverse restricted universe
deb-src http://mirrors.aliyun.com/ubuntu/ trusty-proposed main multiverse restricted universe
deb-src http://mirrors.aliyun.com/ubuntu/ trusty-security main multiverse restricted universe
deb-src http://mirrors.aliyun.com/ubuntu/ trusty-updates main multiverse restricted universe
然后
apt-get update
apt-get install 你要安装的东西安装常用软件
在~/.bashrc里面最后加上
export LD_LIBLABRARY_PATH = /usr/local/cuda-8.0/lib64:$LD_LIBLABRARY_PATH退出终端
主机保存镜像为新版本
sudo docker ps -l
y@y:~$ sudo docker ps -l
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
a1f2ac36a2c9 daocloud.io/daocloud/tensorflow:0.11.0-gpu "/bin/bash" 10 minutes ago Up 10 minutes 6006/tcp, 8888/tcp 把a1f2ac36a2c9这个名字记住
然后
docker commit a1f2ac36a2c9 新名字OK了
把镜像存到移动硬盘里
sudo docker save -o "要存的地址" daocloud.io/daocloud/tensorflow:0.11.0-gpu加载本地的
sudo docker load --input "本地地址"删除镜像
docker rmi "镜像IDa1f2ac36a2c9 "使用tensorflow
在docker里面发现import tensorflow报错
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:105] Couldn't open CUDA library libcuda.so.1. LD_LIBRARY_PATH: /usr/local/cuda-8.0/lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:160] hostname: ad8b0d82bec1
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:185] libcuda reported version is: Not found: was unable to find libcuda.so DSO loaded into this program
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:356] driver version file contents: """NVRM version: NVIDIA UNIX x86_64 Kernel Module 367.44 Wed Aug 17 22:24:07 PDT 2016估计是cuda.so这个没有集成进来，干脆直接把本机的放进docker来好了。
-v /usr/lib/x86_64-linux-gnu/:/usr/lib/x86_64-linux-gnu/这一句重新加入docker run里面去。重启docker OK
root@282f3d4a2193:/notebooks# python\
>
Python 2.7.6 (default, Jun 22 2015, 17:58:13)
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more 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.1 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcurand.so locally
>>> 测试一下效果
...
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))
Hello, TensorFlow!
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> print(sess.run(a + b))
42
>>>