ubuntu16.04下安装配置深度学习环境(一、cuda7.5的安装)

1.下载所需要的软件

cuda7.5下载(点击下载链接),cudnn4.0下载

2.安装NVIDIA驱动。

一般有两种方法:1)一种方法是利用“软件和更新”来安装,依次选择 系统设置->软件和更新->附加驱动->选择最新的驱动->应用更改

安装时可能遇到的问题:点击完应用更改一段时间后并没有成功安装,再次点击却出现闪退的现象,这个问题困扰了我一晚上,最后发现是因为依赖的问题,通过在终端输入以下命令:sudo apt-get install -f  后 再次安装问题就解决了

2)方法二就是下载安装包后通过命令行安装,因为这个比较麻烦,我没有尝试,看网上其他教程说需要关了xwindows安装才行。

3.安装cuda7.5

(1)在终端cd到所下载的安装包所在的目录,输入sh cuda_7.5.18_linux.run  --override

跑起来后一路空格完那些协议,然后输入accept,除了有一个是让安装驱动的选择N外,其他的一路Y下去

(2)安装cudnn(这个是GPU加速用的)

解压下载好的安装包,在终端输入以下命令:

sudo cp cudnn.h /usr/local/cuda/include/

cd ~/cuda/lib64

sudo cp lib* /usr/local/cuda/lib64/

cd /usr/local/cuda/lib64/

sudo rm -rf libcudnn.so libcudnn.so.4

sudo ln -s libcudnn.so.4.0.7 libcudnn.so.4

sudo ln -s libcudnn.so.4 libcudnn.so

然后设置环境变量

sudo gedit /etc/profile

在末尾加入

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


保存之后创建链接文件

sudo vim /etc/ld.so.conf.d/cuda.conf

键盘按i进入编辑状态,添加文字

/usr/local/cuda/lib64

然后按esc,输入:wq保存退出。

终端下接着输入

sudo ldconfig 使链接生效

3.生成Cuda Sample测试

(1)首先在此之前先把需要的依赖包都安装好,为接下来make caffe做准备

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler

sudo apt-get install --no-install-recommends libboost-all-dev

sudo apt-get install libatlas-base-dev

sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

(2)更改gcc版本(我一开始没有更改,直接make没有报错,但make玩后测试出错,所以这里最好是改一下,如果报报错“unsupported GNU version! gcc versions later than 4.9 are not supported!”错误,那就一定得改了)原因就是这个cuda不支持gcc5.0以上

cd /usr/local/cuda-7.5/include

cp host_config.h host_config.h.bak

sudo gedit host_config.h

Ctrl+F寻找有”4.9”的地方,应该是只有一处,在其上方的

#if __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ > 9)将两个4改成5,保存退出,继续

cd /home/gomee/NVIDIA_CUDA-7.5_Samples

(3)正式开始make example了

终端输入  make all -j4 (j4代表开多少个线程,一般你的电脑是几核的就开几个)

这就应该开始make了,此处大约有4,5分钟。完成之后

cd /home/gomee/NVIDIA_CUDA-7.5_Samples/bin/x86_64/linux/realease

./deviceQuery

如果出现如下信息

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

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GT 650M"
  CUDA Driver Version / Runtime Version          8.0 / 7.5
  CUDA Capability Major/Minor version number:    3.0
  Total amount of global memory:                 1999 MBytes (2096300032 bytes)
  ( 2) Multiprocessors, (192) CUDA Cores/MP:     384 CUDA Cores
  GPU Max Clock rate:                            885 MHz (0.88 GHz)
  Memory Clock rate:                             2000 Mhz
  Memory Bus Width:                              128-bit
  L2 Cache Size:                                 262144 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 = 7.5, NumDevs = 1, Device0 = GeForce GT 650M
Result = PASS
证明cuda安装成功。

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