[blog6]ubuntu18.04安裝cuda9.0和cudnn

原創

Drinks_

2020-02-21 09:52

根據blog裝好mx250的顯卡之後，開始安裝cuda

安裝教程參考https://blog.csdn.net/weixin_41851439/article/details/88712465

0 確定自己已經裝好顯卡了 nvidia-smi會顯示出來

1 下載cuda和cudnn

https://developer.nvidia.com/cuda-90-download-archive?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1604&target_type=runfilelocal

上面是9.0的鏈接

下面是cudnn的鏈接需要註冊之後才能下載下載9.0對應版本

https://developer.nvidia.com/rdp/cudnn-archive

把安裝包和四個補丁全下載了

2 下載的過程中可以把gcc 和 g++降級因爲cuda需要低版本的

sudo apt-get install gcc-4.8

sudo apt-get install g++-4.8

然後進入到/usr/bin目錄下輸入：ls -l gcc*

cd /usr/bin
ls -l gcc*

顯示結果如下：

lrwxrwxrwx 1 root root 7th 3月 20 11:38 /usr/bin/gcc -> gcc-7

表示gcc鏈接到gcc-7, 需要將它改爲鏈接到gcc-4.8，方法如下:

sudo mv gcc gcc.bak #備份
sudo ln -s gcc-4.8 gcc #重新鏈接

同理，將g++鏈接到g++4.8：

sudo mv g++ g++.bak
sudo ln -s g++-4.8 g++

在/usr/bin目錄下查看gcc和g++版本

ls -l gcc*
ls -l g++*

顯示gcc和g++均鏈接到4.8版本，則說明安裝成功

3 安裝cuda

下載好了之後 cd到downloads目錄運行.run文件

sudo sh cuda_9.0.176_384.81_linux.run

然後一路accept或者yes，在提示是否安裝顯卡驅動時選擇no（因爲已經安裝過了，否則可能會出現bug）

會提示驅動問題不管他繼續

接下來安裝補丁文件，方法和安裝cuda9.0一樣：

sudo sh cuda_9.0.176.1_linux.run

sudo sh cuda_9.0.176.2_linux.run

sudo sh cuda_9.0.176.3_linux.run

sudo sh cuda_9.0.176.4_linux.run

4 配置環境

打開.barshrc

sudo vim ~/.barshrc

在最後面加入下面兩條語句

export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

5 安裝cudnn

cd到自己的downloads目錄裏面解壓：

tar zxvf cudnn-9.0-linux-x64-v7.1.tgz

文件copy到cuda裏面

sudo cp /home/**/Downloads/cuda/include/cudnn.h    /usr/local/cuda-9.0/include
sudo cp /home/***/Downloads/cuda/lib64/libcudnn*    /usr/local/cuda-9.0/lib64
sudo chmod a+r /home/***/Downloads/cuda/include/cudnn.h   /usr/local/cuda-9.0/lib64/libcudnn*

OK了

測試cuda是否安裝好

cd /usr/local/cuda/samples/1_Utilities/deviceQuery #由自己電腦目錄決定

sudo make

sudo ./deviceQuery

出現一長串說明OK 我的是這樣

sudo ./deviceQuery
./deviceQuery Starting...

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

Detected 1 CUDA Capable device(s)

Device 0: "GeForce MX250"
  CUDA Driver Version / Runtime Version          10.2 / 9.0
  CUDA Capability Major/Minor version number:    6.1
  Total amount of global memory:                 2003 MBytes (2099904512 bytes)
  ( 3) Multiprocessors, (128) CUDA Cores/MP:     384 CUDA Cores
  GPU Max Clock rate:                            1582 MHz (1.58 GHz)
  Memory Clock rate:                             3004 Mhz
  Memory Bus Width:                              64-bit
  L2 Cache Size:                                 524288 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
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 2 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.2, CUDA Runtime Version = 9.0, NumDevs = 1
Result = PASS