0、安装Anaconda3
下载Anaconda3-4.3.1-Linux-x86_64.sh
chmod a+x Anaconda3-4.3.1-Linux-x86_64.sh
./Anaconda3-4.3.1-Linux-x86_64.sh(默认安装到～目录，同时会替换原有的python2)
vim ~/.bashrc
export PATH=~/anaconda3/bin/:$PATH
一、下载Nvidia驱动
去该地址下载驱动：www.nvidia.com/Download/index.aspx
使用lspci确认显卡型号：lspci | grep NVIDIA
01:00.0 VGA compatible controller: NVIDIA Corporation GM107 [GeForce GTX 745] (rev a2)
下载即可，在~/Downloads/NVIDIA-Linux-x86_64-384.59.run可以找到下载的文件。
1、关闭x-server， sudo service lightdm stop
（提示：会关闭图形界面，请使用ssh或者ctrl+alt+F1，我使用SSH）
2、cd ~/Downloads/ && sudo ./NVIDIA-Linux-x86_64-384.59.run
3、各种yes，然后安装成功。
4、sudo service lightdm start 打开图形界面。
5、执行nvidia-setting可以查看相关信息。
三、Q&A
1、注意图形化界面，使用sudo service lightdm stop/start
2、nouveau驱动问题：有的使用modprobe的blacklist，我试了没成功，所以直接使用nvidia驱动替换。即 sudo apt-get install nvidia-current，然后重启，发现nouveau驱动不再加载。
四、下载CUDA
去该地址https://developer.nvidia.com/cuda-downloads下载安装即可。
我选deb（network）因为linux下载使用浏览器比较慢（我ssh的机器系统是Kali）。
在～/Downloads/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb，执行上图中的1，2，3即可安装。
下面就是等待出错了。。。（很不幸没有出错，但是调用deviceQuery出错了。。。。）
安装的目录一般是/usr/local/cuda，在cuda/extras/demo_suite/下sudo ./deviceQuery 提示如下：
./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce GTX 745"
CUDA Driver Version / Runtime Version 8.0 / 8.0
CUDA Capability Major/Minor version number: 5.0
Total amount of global memory: 4041 MBytes (4237164544 bytes)
( 3) Multiprocessors, (128) CUDA Cores/MP: 384 CUDA Cores
GPU Max Clock rate: 1032 MHz (1.03 GHz)
Memory Clock rate: 900 Mhz
Memory Bus Width: 128-bit
L2 Cache Size: 2097152 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 1 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = GeForce GTX 745
Result = PASS
五、Q&A
执行deviceQuery出现（心都碎了，这是第二次装一堆问题，第一次的时候6的不行）：
./deviceQueryStarting...
CUDADeviceQuery(RuntimeAPI)version(CUDARTstaticlinking)
FATAL:Modulenvidia_uvmnotfound.
cudaGetDeviceCount returned30
->unknown error
Result=FAIL
果断谷歌：发现应该是nvidia-uvm.ko的问题，dmesg提示的问题如下。
nvidia_uvm: Unknown symbol nvUvmInterfaceGetBigPageSize (err 0)
解决方法：sudo update-alternatives--config x86_64-linux-gnu_gl_conf
执行上面的命令，选在一个。我的如图:(估计是install nvidia-current引入的)
然后sudo modprobe nivdia-uvm提示ok，在执行deviceQuery就OK了。
六 、CUDA编程
以下链接是共享的百度网盘，随便网上找的CUDA编程的一点资料。
链接: http://pan.baidu.com/s/1slBazlN 密码: rsmm
七、安装cudnn (装了6.0不好用。。。。。)
下载：cudnn-8.0-linux-x64-v5.1.tgz
cd /usr/local
sudo tar -zxvf /tmp/cudnn-8.0-linux-x64-v5.1.tgz
八、环境变量设置
vim ~/.bashrc，添加如下
export CUDA_HOME=/usr/local/cuda-8.0
export PATH=/usr/local/cuda-8.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:/usr/local/cuda-8.0/extras/CUPTI/lib64:$LD_LIBRARY_PATH
九安装Tensorflow（在线安装）
sudo apt-get install libcupti-dev
Installing with Anaconda
Take the following steps to install TensorFlow in an Anaconda environment:
Follow the instructions on theAnaconda download siteto download and install Anaconda.
Create a conda environment namedtensorflowto run a version of Python by invoking the following command:
$conda create -n tensorflow
Activate the conda environment by issuing the following command:
$source activate tensorflow(tensorflow)$ # Your prompt should change
Issue a command of the following format to install
TensorFlow inside your conda environment:
(tensorflow)$pip install --ignore-installed --upgradetfBinaryURL
wheretfBinaryURLis theURL of the TensorFlow Python package. For example, the following command installs the CPU-only version of TensorFlow for Python 2.7:
(tensorflow)$pip install --ignore-installed --upgrade \
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.1-cp34-cp34m-linux_x86_64.w
我的是python3.6所以tfBinaryURL：https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp36-cp36m-linux_x86_64.whl
所以执行pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp36-cp36m-linux_x86_64.whl
等到执行结束即可。
十、测试
执行ipython，输入以下：
import tensorflow as tf
hello=tf.constant('Hello, TensorFlow!')
sess=tf.Session()
print(sess.run(hello))
If the system outputs the following, then you are ready to begin writing TensorFlow programs:
Hello, TensorFlow!
十一、安装成功。
第一次写东西，文笔low了点。。。。。。
