1、安装ubuntu系统，只需要分两个区即可，一个为swap分区，用8G，一个根分区/
2、安装系统后不要通过sudo apt update和sudo apt upgrade更新软件源内容和更新软件，我的感觉的原始14.04的依赖解决的比较好，一旦更新后很多依赖包依赖低版本的，而升级后的包的版本过高，导致很多依赖问题，不过不清楚是否是这个原因，有谁了解请分享一下。
3、安装nvidia原生驱动
（1）移除原来驱动
sudo apt-get remove --purge nvidia*
sudo apt-get autoremove
禁用linux自带的开源驱动nouveau，方法为：
修改blacklist.conf的属性
$sudo chmod 666 /etc/modprobe.d/blacklist.conf
用vim编辑器打开
$sudo vim /etc/modprobe.d/blacklist.conf
在该文件后添加一下几行：
blacklist vga16fb
blacklist nouveau
blacklist rivafb
blacklist rivatv
并添加如下内容：
blacklist nouveau
blacklist lbm-nouveau
options nouveau modeset=0
alias nouveau off
alias lbm-nouveau off
lsmod | grep nouveau
查看nouveau模块是否被加载。如果什么都没输出，则执行下一步。
关闭x-window
service lightdm stop（不能在桌面条件安装，ctri+alt+f1）
给cuda可执行的权限
sudo chmod a+x NVIDIA-Linux-x86_64-375.20.run
安装步骤
安装(注意 参数)
sudo ./NVIDIA-Linux-x86_64-375.20.run --no-x-check --no-nouveau-check --no-opengl-files
--no-x-check 安装驱动时关闭X服务
--no-nouveau-check 安装驱动时禁用nouveau
--no-opengl-files 只安装驱动文件，不安装OpenGL文件
以上几个参数必须添加，否则还会出现循环登录的问题，有人说是OpenGL以及双显卡的问题，有具体了解细节的同学可以留言分享一下，感谢。
Accept
Continue installation
Would you like to run the nvidia-xconfig utility to automatically update your X Configuration file so set the NVIDIA X driver will be used when you restart X?
NO
Install 32-Bit compatibility libraries?参考
NO
4、安装cuda
（1）重启电脑
在进入到登录界面时候，按住Ctrl+Alt+F1，进入到text mode，登录账号
（2）关闭图形界面
终端命令：
sudo service lightdm stop
（3）切换到cuda文件目录
cd到下载好的cuda目录，再ls查看cuda名字
（4）给cuda可执行的权限
sudo chmod a+x cuda_8.0.44_linux.run
（5）安装步骤
sh cuda_8.0.44_linux.run
选项如下所示：
Description
This package includes over 100+ CUDA examples that demonstrate
various CUDA programming principles, and efficient CUDA
implementation of algorithms in specific application domains.
The NVIDIA CUDA Samples License Agreement is available in
Do you accept the previously read EULA?
accept/decline/quit: accept
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 367.48?
(y)es/(n)o/(q)uit: n
Install the CUDA 8.0 Toolkit?
(y)es/(n)o/(q)uit: y
Enter Toolkit Location
[ default is /usr/local/cuda-8.0 ]:
Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y
Install the CUDA 8.0 Samples?
(y)es/(n)o/(q)uit: y
Enter CUDA Samples Location
[ default is /home/c302 ]:
Installing the CUDA Toolkit in /usr/local/cuda-8.0 ...
Installing the CUDA Samples in /home/c302 ...
Copying samples to /home/c302/NVIDIA_CUDA-8.0_Samples now...
Finished copying samples.
===========
= Summary =
===========
Driver: Not Selected
Toolkit: Installed in /usr/local/cuda-8.0
Samples: Installed in /home/c302
Please make sure that
- PATH includes /usr/local/cuda-8.0/bin
- LD_LIBRARY_PATH includes /usr/local/cuda-8.0/lib64, or, add /usr/local/cuda-8.0/lib64 to /etc/ld.so.conf and run ldconfig as root
To uninstall the CUDA Toolkit, run the uninstall script in /usr/local/cuda-8.0/bin
Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-8.0/doc/pdf for detailed information on setting up CUDA.
***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 361.00 is required for CUDA 8.0 functionality to work.
To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file:
sudo <CudaInstaller>.run -silent -driver
Logfile is /tmp/cuda_install_9045.log
设置环境变量
#export PATH=/usr/local/cuda-8.0/bin:$PATH
#export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH
#添加系统变量修改到系统文件
#sudo vi /etc/profile
#在最后添加上面两句，然后保存。使立即生效
#source /etc/profile //环境变量立即生效
用下面这个设置环境变量
安装完成后添加环境变量，将下面语句写入到 ~/.bashrc 的尾部:
export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
(6)测试
nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Sun_Sep__4_22:14:01_CDT_2016
Cuda compilation tools, release 8.0, V8.0.44
测试cuda的samples
cd ‘/home/zhou/NVIDIA_CUDA-8.0_Samples’
make //这里需要点时间
最后显示：
make[1]: Leaving directory `/home/c302/NVIDIA_CUDA-8.0_Samples/7_CUDALibraries/MersenneTwisterGP11213'
Finished building CUDA samples
cd 0_Simple/matrixMul
运行测试如下：
./matrixMul
[Matrix Multiply Using CUDA] - Starting...
GPU Device 0: "GeForce GTX 1080" with compute capability 6.1
MatrixA(320,320), MatrixB(640,320)
Computing result using CUDA Kernel...
done
Performance= 1109.06 GFlop/s, Time= 0.118 msec, Size= 131072000 Ops, WorkgroupSize= 1024 threads/block
Checking computed result for correctness: Result = PASS
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
5、cudnn安装
tar -xvf cudnn-8.0-linux-x64-v6.0.tgz
安装cuDNN比较简单，解压后把相应的文件拷贝到对应的CUDA目录下即可
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*
6、opencv安装
在ubuntu14.04环境中默认安装了python2.7和python3.4，可以在以上两个python环境基础上安装opencv，我安装opencv的安装方法可以正确安装opencv，但是python2.7和python3.4都无法import cv2，主要原因是python2.7和python3.4的lib的第三方库dist-packages中没有关联opencv的库，即cv2.so，目前没有找到如何解决这种问题的原因。于是，选择了直接安装opecv，python2.7的opencv是通过apt-get install安装的，命令为：
sudo apt-get install python-opencv
此时python2.7可以import cv2了，只是这个opencv版本是2.4.8的，不是之前安装的2.4.13版本，说明通过命令新安装了一个opencv。
对于python3.4没有相应的命令直接安装opencv即python与opencv的关联库，这里在网址https://pypi.python.org/pypi/opencv-python上下载了opencv_python-3.3.0.10-cp34-cp34m-manylinux1_x86_64.whl文件，之后通过命令：
sudo pip install opencv_python-3.3.0.10-cp34-cp34m-manylinux1_x86_64.whl
通过这个命令安装的opencv版本是3.3.0，说明也是重新安装了一个版本的opencv，
这里没有做到安装一个版本的opencv，不同版本的python连接到同一个版本的opencv上。如果哪位高手知道如何解决这个问题，可以做到按照需求不同版本的python关联到不同版本的opencv上，可以给我留言分享一下啊，十分感谢！！！
7、tensorflow安装
2、安装系统后不要通过sudo apt update和sudo apt upgrade更新软件源内容和更新软件，我的感觉的原始14.04的依赖解决的比较好，一旦更新后很多依赖包依赖低版本的，而升级后的包的版本过高，导致很多依赖问题，不过不清楚是否是这个原因，有谁了解请分享一下。
3、安装nvidia原生驱动
（1）移除原来驱动
sudo apt-get remove --purge nvidia*
sudo apt-get autoremove
禁用linux自带的开源驱动nouveau，方法为：
修改blacklist.conf的属性
$sudo chmod 666 /etc/modprobe.d/blacklist.conf
用vim编辑器打开
$sudo vim /etc/modprobe.d/blacklist.conf
在该文件后添加一下几行：
blacklist vga16fb
blacklist nouveau
blacklist rivafb
blacklist rivatv
blacklist nvidiaf
创建一个文件通过命令 sudo vim /etc/modprobe.d/blacklist-nouveau.conf并添加如下内容：
blacklist nouveau
blacklist lbm-nouveau
options nouveau modeset=0
alias nouveau off
alias lbm-nouveau off
lsmod | grep nouveau
查看nouveau模块是否被加载。如果什么都没输出，则执行下一步。
有些人认为只需要在/etc/modprobe.d/blacklist.conf下添加
blacklist nouveau即可。
关闭x-window
service lightdm stop（不能在桌面条件安装，ctri+alt+f1）
给cuda可执行的权限
sudo chmod a+x NVIDIA-Linux-x86_64-375.20.run
安装步骤
安装(注意 参数)
sudo ./NVIDIA-Linux-x86_64-375.20.run --no-x-check --no-nouveau-check --no-opengl-files
--no-x-check 安装驱动时关闭X服务
--no-nouveau-check 安装驱动时禁用nouveau
--no-opengl-files 只安装驱动文件，不安装OpenGL文件
以上几个参数必须添加，否则还会出现循环登录的问题，有人说是OpenGL以及双显卡的问题，有具体了解细节的同学可以留言分享一下，感谢。
Accept
Continue installation
Would you like to run the nvidia-xconfig utility to automatically update your X Configuration file so set the NVIDIA X driver will be used when you restart X?
NO
Install 32-Bit compatibility libraries?参考
NO
4、安装cuda
（1）重启电脑
在进入到登录界面时候，按住Ctrl+Alt+F1，进入到text mode，登录账号
（2）关闭图形界面
终端命令：
sudo service lightdm stop
（3）切换到cuda文件目录
cd到下载好的cuda目录，再ls查看cuda名字
（4）给cuda可执行的权限
sudo chmod a+x cuda_8.0.44_linux.run
（5）安装步骤
sh cuda_8.0.44_linux.run
选项如下所示：
Description
This package includes over 100+ CUDA examples that demonstrate
various CUDA programming principles, and efficient CUDA
implementation of algorithms in specific application domains.
The NVIDIA CUDA Samples License Agreement is available in
Do you accept the previously read EULA?
accept/decline/quit: accept
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 367.48?
(y)es/(n)o/(q)uit: n
Install the CUDA 8.0 Toolkit?
(y)es/(n)o/(q)uit: y
Enter Toolkit Location
[ default is /usr/local/cuda-8.0 ]:
Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y
Install the CUDA 8.0 Samples?
(y)es/(n)o/(q)uit: y
Enter CUDA Samples Location
[ default is /home/c302 ]:
Installing the CUDA Toolkit in /usr/local/cuda-8.0 ...
Installing the CUDA Samples in /home/c302 ...
Copying samples to /home/c302/NVIDIA_CUDA-8.0_Samples now...
Finished copying samples.
===========
= Summary =
===========
Driver: Not Selected
Toolkit: Installed in /usr/local/cuda-8.0
Samples: Installed in /home/c302
Please make sure that
- PATH includes /usr/local/cuda-8.0/bin
- LD_LIBRARY_PATH includes /usr/local/cuda-8.0/lib64, or, add /usr/local/cuda-8.0/lib64 to /etc/ld.so.conf and run ldconfig as root
To uninstall the CUDA Toolkit, run the uninstall script in /usr/local/cuda-8.0/bin
Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-8.0/doc/pdf for detailed information on setting up CUDA.
***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 361.00 is required for CUDA 8.0 functionality to work.
To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file:
sudo <CudaInstaller>.run -silent -driver
Logfile is /tmp/cuda_install_9045.log
设置环境变量
#export PATH=/usr/local/cuda-8.0/bin:$PATH
#export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH
#添加系统变量修改到系统文件
#sudo vi /etc/profile
#在最后添加上面两句，然后保存。使立即生效
#source /etc/profile //环境变量立即生效
用下面这个设置环境变量
安装完成后添加环境变量，将下面语句写入到 ~/.bashrc 的尾部:
export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
(6)测试
nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Sun_Sep__4_22:14:01_CDT_2016
Cuda compilation tools, release 8.0, V8.0.44
测试cuda的samples
cd ‘/home/zhou/NVIDIA_CUDA-8.0_Samples’
make //这里需要点时间
最后显示：
make[1]: Leaving directory `/home/c302/NVIDIA_CUDA-8.0_Samples/7_CUDALibraries/MersenneTwisterGP11213'
Finished building CUDA samples
cd 0_Simple/matrixMul
运行测试如下：
./matrixMul
[Matrix Multiply Using CUDA] - Starting...
GPU Device 0: "GeForce GTX 1080" with compute capability 6.1
MatrixA(320,320), MatrixB(640,320)
Computing result using CUDA Kernel...
done
Performance= 1109.06 GFlop/s, Time= 0.118 msec, Size= 131072000 Ops, WorkgroupSize= 1024 threads/block
Checking computed result for correctness: Result = PASS
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
5、cudnn安装
tar -xvf cudnn-8.0-linux-x64-v6.0.tgz
安装cuDNN比较简单，解压后把相应的文件拷贝到对应的CUDA目录下即可
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*
6、opencv安装
在ubuntu14.04环境中默认安装了python2.7和python3.4，可以在以上两个python环境基础上安装opencv，我安装opencv的安装方法可以正确安装opencv，但是python2.7和python3.4都无法import cv2，主要原因是python2.7和python3.4的lib的第三方库dist-packages中没有关联opencv的库，即cv2.so，目前没有找到如何解决这种问题的原因。于是，选择了直接安装opecv，python2.7的opencv是通过apt-get install安装的，命令为：
sudo apt-get install python-opencv
此时python2.7可以import cv2了，只是这个opencv版本是2.4.8的，不是之前安装的2.4.13版本，说明通过命令新安装了一个opencv。
对于python3.4没有相应的命令直接安装opencv即python与opencv的关联库，这里在网址https://pypi.python.org/pypi/opencv-python上下载了opencv_python-3.3.0.10-cp34-cp34m-manylinux1_x86_64.whl文件，之后通过命令：
sudo pip install opencv_python-3.3.0.10-cp34-cp34m-manylinux1_x86_64.whl
通过这个命令安装的opencv版本是3.3.0，说明也是重新安装了一个版本的opencv，
这里没有做到安装一个版本的opencv，不同版本的python连接到同一个版本的opencv上。如果哪位高手知道如何解决这个问题，可以做到按照需求不同版本的python关联到不同版本的opencv上，可以给我留言分享一下啊，十分感谢！！！
7、tensorflow安装