Ubuntu 16.04卸载CUDA 6.5和安装CUDA 8.0

一，引言

由于系统从Ubuntu 14.04升级到了16.04，原来的CUDA 6.5无法继续使用，所以重新安装了CUDA 8.0。

二，卸载CUDA 6.5 和驱动

以下操作都在命令行界面操作，比如按下Ctrl+alt+F1进入命令行
首先停止lightdm：
sudo service lightdm stop

卸载NVIDIA驱动

原来安装CUDA 6.5的时候一起安装了 NVIDIA驱动，首先卸载掉，命令一般是：

 
 

  
  
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sudo /usr/bin/nvidia-uninstall

如果找不到命令，可以在命令行下直接输入：

 
 

  
  
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sudo apt-get install autoremove --purge nvidia*

卸载CUDA toolkit

CUDA默认安装在 /usr/local/cuda-6.5下，用下面的命令卸载：

 
 

  
  
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sudo /usr/local/cuda-6.5/bin/uninstall_cuda-6.5.pl

此时一般需要重启一下

三， 安装CUDA 8.0

首先下载CUDA安装文件，网址：https://developer.nvidia.com/cuda-release-candidate-download
需要注册NVIDIA的开发者账号。根据电脑的系统下载对应的安装文件，这里下载的是CUDA 8.0的runfile（local）文件。安装方法可以按照官方安装指南：http://docs.nvidia.com/cuda/cuda-installation-guide-linux/#axzz4HIBXnwyt

依旧进入命令行界面，然后还是

 
 

  
  
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sudo service lightdm stop

执行下面语句，运行runfile文件：

 
 

  
  
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sudo sh cuda_8.0.44_linux.run

会有一系列的安装选项，比如是否安装NVIDIA367驱动等，由于之前卸载了NVIDAI驱动，所以这里选择了安装，其他还有比如是否安装samples以及安装目录等。
安装完成后会出现以下界面：

============ Summary ============Driver: Not SelectedToolkit: Installed in /usr/local/cuda-8.0Samples: Installed in /home/textminerPlease 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 rootTo uninstall the CUDA Toolkit,run the uninstall script in /usr/local/cuda-8.0/binPlease 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 with the name of this run file:sudo .run -silent -driverLogfile is /opt/temp//cuda_install_6583.log

然后设置环境变量和动态链接库，在/etc/profile文件中添加：

 
 

  
  
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export PATH = /usr/local/cuda-8.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64：$LD_LIBRARY_PATH

之后再

 
 

  
  
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source /etc/profile

可以使改变立即生效

测试
如果安装了CDUA samples可以运行一下以测试CUDA是否能成功运行。
进入sample的目录，CUDA 8.0的默认安装目录变成了用户主目录，会有一个NVIDA_CUDA-8.0_Samples的目录，里面有Makefile文件，直接make就行，一般需要编译一段比较长的时间。之后就可以在当前目录的bin目录中随意运行一些程序，以验证CUDA是否正确安装，比如deviceQuery程序的运行结果：

 
 

  
  
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./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 980 Ti"
  CUDA Driver Version / Runtime Version          8.0 / 8.0
  CUDA Capability Major/Minor version number:    5.0
  Total amount of global memory:                 1999 MBytes (2095841280 bytes)
  ( 5) Multiprocessors,(128) CUDA Cores/MP:     640 CUDA Cores
  GPU Max Clock rate:                            1084 MHz (1.08 GHz)
  Memory Clock rate:                             2700 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,z): (1024,1024,64)
  Max dimension size of a grid size    (x,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 750 Ti
Result = PASS

参考

http://www.th7.cn/system/lin/201608/176823.shtml blog.csdn.net/xulingqiang/article/details/46660107

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