深度学习开发环境配置：Ubuntu1 6.04+Nvidia GTX 1080+CUDA 8.0

前提条件，已经安装好了 Ubuntu 16.04 操作系统， 见安装 Windows 10 和 Ubuntu 16.04 双系统

懒人版方法：

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apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pubecho "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda.listsudo apt-get updatesudo apt-get -y install cuda-drivers cuda

这个方法会安装稳定版的驱动和CUDA，可能不那么新。

然后开始安装 cuDNN, 先下载 cuDNN 6.0,

wget http://developer.download.nvidia.com/compute/redist/cudnn/v6.0/cudnn-8.0-linux-x64-v6.0.tgz

然后解压到 /usr/local，

sudo tar -zxf cudnn-8.0-linux-x64-v6.0.tgz -P /usr/local

至此，驱动， CUDA 和 cuDNN都安装完了。

如果你想安装最新版的驱动和最新版的CUDA，那么接着读下去吧。

1. 安装 Nvidia 驱动

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sudo add-apt-repository -qy ppa:graphics-drivers/ppasudo apt-get -qy updatesudo apt-get -qy install nvidia-370sudo apt-get -qy install mesa-common-devsudo apt-get -qy install freeglut3-devsudo reboot

注意，一般比较新的主板，默认是UEFI BIOS，默认启用了 Secure Boot，否则开机后登陆不进去。老主板没有这个问题。

2. 安装 CUDA 8.x

去 CUDA 8.x 下载页面，一定要下载 runfile 安装方式的安装包，参考资料里的好几篇都是选择这种方式，貌似 deb包有坑？

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chmod u+x ./cuda_8.0.27_linux.runsudo ./cuda_8.0.27_linux.run --tmpdir=/tmp

执行后会有一系列提示让你确认，第一个就是问你是否安装显卡驱动，由于前一步已经安装了显卡驱动，所以这里就不需要了，况且 runfile 自带的驱动版本不是最新的。 因此 Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 361.77? 这里选择 no。

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Do you accept the previously read EULA?accept/decline/quit: acceptInstall NVIDIA Accelerated Graphics Driver for Linux-x86_64 361.77?(y)es/(n)o/(q)uit: nInstall the CUDA 8.0 Toolkit?(y)es/(n)o/(q)uit: yEnter 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: yInstall the CUDA 8.0 Samples?(y)es/(n)o/(q)uit: yEnter CUDA Samples Location [ default is /home/programmer ]:

你以为你会成功安装吗？并不是，你一定会碰到一个错误，Installation Failed. Using unsupported Compiler. ，这是因为 Ubuntu 16.04 默认的 GCC 5.4 对于 CUDA 8.x来说过于新了，CUDA 安装脚本还不能识别新版本的 GCC。

看了一下安装日志，解决方案也很简单，加一个 --override 选项，

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sudo ./cuda_8.0.27_linux.run --tmpdir=/tmp --override

这次可以成功了。

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============ Summary ============Driver:   Not SelectedToolkit:  Installed in /usr/local/cuda-8.0Samples:  Installed in /home/programmer, but missing recommended librariesPlease 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 <CudaInstaller> with the name of this run file:    sudo <CudaInstaller>.run -silent -driverLogfile is /tmp/cuda_install_6794.logSignal caught, cleaning up

把以下两行加入到 .bashrc，

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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}}

安装补丁

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chmod u+x ./cuda_8.0.27.1_linux.runsudo ./cuda_8.0.27.1_linux.run

测试是否安装成功

最后再来测试一下CUDA，运行：

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nvidia-smi

结果如下所示：

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+-----------------------------------------------------------------------------+| NVIDIA-SMI 370.23                 Driver Version: 370.23                    ||-------------------------------+----------------------+----------------------+| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC || Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. ||===============================+======================+======================||   0  GeForce GTX 1080    Off  | 0000:05:00.0      On |                  N/A || 27%   29C    P8     9W / 180W |    515MiB /  8110MiB |      4%      Default |+-------------------------------+----------------------+----------------------++-----------------------------------------------------------------------------+| Processes:                                                       GPU Memory ||  GPU       PID  Type  Process name                               Usage      ||=============================================================================||    0      4761    G   /usr/lib/xorg/Xorg                             259MiB ||    0      5224    G   compiz                                         253MiB |+-----------------------------------------------------------------------------+

再来试几个CUDA例子：

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cd ~/NVIDIA_CUDA-8.0_Samples/1_Utilities/deviceQuerymake

执行 ./deviceQuery，得到：

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CUDA Device Query (Runtime API) version (CUDART static linking)Detected 1 CUDA Capable device(s)Device 0: "GeForce GTX 1080"  CUDA Driver Version / Runtime Version          8.0 / 8.0  CUDA Capability Major/Minor version number:    6.1  Total amount of global memory:                 8110 MBytes (8504279040 bytes)  (20) Multiprocessors, (128) CUDA Cores/MP:     2560 CUDA Cores  GPU Max Clock rate:                            1734 MHz (1.73 GHz)  Memory Clock rate:                             5005 Mhz  Memory Bus Width:                              256-bit  L2 Cache Size:                                 2097152 bytes  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 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 2 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 / 5 / 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 1080Result = PASS

再测试试一下nobody：

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cd ~/NVIDIA_CUDA-8.0_Samples/5_Simulations/nbody/make

执行：

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./nbody -benchmark -numbodies=256000 -device=0

得到：

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> Windowed mode> Simulation data stored in video memory> Single precision floating point simulation> 1 Devices used for simulationgpuDeviceInit() CUDA Device [0]: "GeForce GTX 1080> Compute 6.1 CUDA device: [GeForce GTX 1080]number of bodies = 256000256000 bodies, total time for 10 iterations: 2364.286 ms= 277.192 billion interactions per second= 5543.830 single-precision GFLOP/s at 20 flops per interaction

至此，说明 CUDA 8.x 安装成功了。

参考资料

深度学习主机环境配置: Ubuntu16.04+Nvidia GTX 1080+CUDA8.0 Nvidia GTX 1080 on Ubuntu 16.04 for Deep Learning - Changjiang Build Personal Deep Learning Rig: GTX 1080 + Ubuntu 16.04 + CUDA 8.0RC + CuDnn 7 + Tensorflow/Mxnet/Caffe/Darknet - by Guanghan Ning GeForce GTX 1080, CUDA 8.0, Ubuntu 16.04, Caffe