Ubuntu16.04+GPU1080ti深度学习环境配置，
Ubuntu16.04+GPU1080ti深度学习环境配置，
本说明旨在在ubuntu16.04的服务器上的普通用户中创建深度学习环境
服务器上创建用户
系统管理员用户:
sudo adduser vitamin
~#mkdir sotfware
~#cd sotfware
~/sotfware # mkdir cuda安装cuda
运行下载的安装包
sh cuda_8.0.61_375.26_linux.run.26_linux-run
输入：q 退出阅读EULA
输入：accept
Do you accept the previously read EULA?
accept/decline/quit: accept
输入：n ;因为之前安装的gpu驱动器版本高。
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 375.26?
(y)es/(n)o/(q)uit: n
输入：y ;
Install the CUDA 8.0 Toolkit?
(y)es/(n)o/(q)uit: y
输入：安装目录；
Enter Toolkit Location
[ default is /usr/local/cuda-8.0 ]: /home/vitamin/software/cuda
输入：n;不安装符号链接
Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: n
输入：y；安装cuda8.0 samples
Install the CUDA 8.0 Samples?
(y)es/(n)o/(q)uit: y
Enter CUDA Samples Location
[ default is /home/vitamin ]: /home/vitamin
修改配置文件.bashrc
export PATH=/home/vitamin/software/cuda/bin:$PATH
export LD_LIBRARY_PATH=/home/vitamin/software/cuda/lib64:$LD_LIBRARY_PATH
使得.bashrc生效
source .bashrc
测试cuda:
进入测试目录：
cd /home/vitamin/NVIDIA_CUDA-8.0_Samples/1_Utilities/deviceQuery
编译：
make -j4
运行：
./deviceQuery
输出：结果中有Result = PASS 表示安装成功
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.1, CUDA Runtime Version = 8.0, NumDevs = 2, Device0 = GeForce GTX 1080 Ti, Device1 = GeForce GTX 1080 Ti
Result = PASS
安装cudnn
解压cudnn-8.0-linux-x64-v6.0.tgz后一共有5个文件，需要拷贝到对应lib64，include目录下
min@server-11:/home/bigdata/download$ tar xvf cudnn-8.0-linux-x64-v6.0.tgz -C /home/min/software
cuda/include/cudnn.h
cuda/lib64/libcudnn.so
cuda/lib64/libcudnn.so.6
cuda/lib64/libcudnn.so.6.0.21
cuda/lib64/libcudnn_static.a安装tensorflow
进入：Anaconda2-5.0.1-Linux-x86_64.sh 目录 执行
# sh Anaconda2-5.0.1-Linux-x86_64.sh
# yes
# enter
重新打开一个终端安装tensorflow-gpu
pip install tensorflow-gpu==1.4.1安装基于python3的tensorflow-gpu
在Ancoanda2中安装python3.5环境
# conda create -n py3 python=3.5
激活py3环境
# source activate py3
安装tensorflow-gpu1.4.1
(py3)# pip install tensorflow-gpu==1.4.1由于国内https://storage.googleapis.com 无法访问，故运行
pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.4.0-cp34-cp34m-linux_x86_64.whl
会提示安装失败！Jupyter notebook远程访问服务器
配置步骤
1.生成配置文件：
$jupyter notebook --generate-config
2.生成密码：
打开python终端
In [1]: from IPython.lib import passwd
In [2]: passwd()
Enter password:
Verify password:
Out[2]: 'sha1:xxxxxxxxxxxxxx'
3.修改配置文件：
vim ~/.jupyter/jupyter_notebook_config.py
在配置文件中加入以下内容：
c.NotebookApp.ip='*'
c.NotebookApp.password = u'sha1:414040d3d9a2:897ca67086f86d84f82333224ec97ade98b533ba'
c.NotebookApp.open_browser = False
c.NotebookApp.port =18888
c.IPKernelApp.pylab = 'inline'
4.启动Jupyter notebook
在当前用户目录下启动jupyter notebook
$cd ~
~$nohup jupyter notebook &
例如：
vitamin@server-11:~$ nohup jupyter notebook &
[2] 12327
vitamin@server-11:~$ nohup: 忽略输入并把输出追加到'nohup.out'
vitamin@server-11:~$
停止jupyter notebook服务器后台运行：
查看正在执行的python进程：
~#ps -ef | grep python
min 31598 31342 0 09:16 pts/8 00:00:01 /home/min/anaconda2/bin/python
~#kill -9 31598【pid】
手动注册Jupyter notebook的ipython运行核
anaconda2安装了python3环境：
# 一般通过以下这条命令安装python3.6环境
# conda create -n py36 python=3.6
启动 python3环境：
~# source activate py36
通过以下两步注册ipython运行核：
【py36】~# conda install notebook ipykernel
【py36】~# ipython kernel install --user
【py36】~#source deactivate py36
~#jupyter notebookanconda3安装了python2环境：
# 一般通过以下这条命令安装python3.6环境
# conda create -n py27 python=2.7
~#source activate py27
【py27】~#conda install notebook ipykernel
【py27】~#ipython kernel install --user
【py27】~#source deactivate py27
~#jupyter notebook调节显示器分辨率
方法一：修改grup配置文件
sudo vim /etc/default/grub
# The resolution used on graphical terminal
# note that you can use only modes which your graphic card supports via VBE
# you can see them in real GRUB with the command `vbeinfo'
#GRUB_GFXMODE=640x480
# 这里分辨率自行设置
修改这行：
GRUB_GFXMODE=1024x768
更新：
sudo update-grub
方法二：
设置一下source源和pip源
cd /etc/apt/
sudo cp sources.list sources.list.bak
sudo vi sources.list
加入文件：
deb-src http://archive.ubuntu.com/ubuntu xenial main restricted #Added by software-properties
deb http://mirrors.aliyun.com/ubuntu/ xenial main restricted
deb-src http://mirrors.aliyun.com/ubuntu/ xenial main restricted multiverse universe #Added by software-properties
deb http://mirrors.aliyun.com/ubuntu/ xenial-updates main restricted
deb-src http://mirrors.aliyun.com/ubuntu/ xenial-updates main restricted multiverse universe #Added by software-properties
deb http://mirrors.aliyun.com/ubuntu/ xenial universe
deb http://mirrors.aliyun.com/ubuntu/ xenial-updates universe
deb http://mirrors.aliyun.com/ubuntu/ xenial multiverse
deb http://mirrors.aliyun.com/ubuntu/ xenial-updates multiverse
deb http://mirrors.aliyun.com/ubuntu/ xenial-backports main restricted universe multiverse
deb-src http://mirrors.aliyun.com/ubuntu/ xenial-backports main restricted universe multiverse #Added by software-properties
deb http://archive.canonical.com/ubuntu xenial partner
deb-src http://archive.canonical.com/ubuntu xenial partner
deb http://mirrors.aliyun.com/ubuntu/ xenial-security main restricted
deb-src http://mirrors.aliyun.com/ubuntu/ xenial-security main restricted multiverse universe #Added by software-properties
deb http://mirrors.aliyun.com/ubuntu/ xenial-security universe
deb http://mirrors.aliyun.com/ubuntu/ xenial-security multiverse
最后更新一下：
sudo apt-get update
sudo apt-get upgrade
安装1080TI显卡驱动:
sudo apt-get purge nvidia*
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update && sudo apt-get install nvidia-384 nvidia-settings
安装完毕后重启机器，运行 nvidia-smi，看看生效的显卡驱动：
会有显卡驱动信息详细显示
相关软件下载链接
tensorflow下载链接
下载Anaconda5.0.1
下载cuda8.0
下载cudnn6.0(注册后同意协议)
参考链接
tensorflow安装官方指南
深度学习服务器环境配置过程详细版
从零开始配置你的深度学习服务器+踩坑经验
深度学习主机环境配置: Ubuntu16.04+Nvidia GTX 1080+CUDA8.0
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