caffe安装 Ubuntu14.04 cuda 8.0 cudnn 5.1



第一部分,准备材料(NVIDIA官网下载):
显卡驱动NVIDIA-Linux-x86_64-367.44.run

Cuda8.0cuda_8.0.27_linux.run
网址:https://developer.nvidia.com/cuda-downloads

Cudnncudnn-7.0-linux-x64-v4.0-prod.tgz
网址:https://developer.nvidia.com/cudnn
第二部分,安装步骤
2.1系统安装
系统选择ubuntu14.04,下载后ultrISO制作到U盘安装,不细说了。关闭系统更新。

2.2、安装依赖
安装编译工具:$sudo apt-get install build-essential # basic requirement
$sudo apt-get install cmake git
$sudo apt-get update #update source
安装依赖项: $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 libopenblas-dev liblapack-dev libatlas-base-dev
$sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
$sudo apt-get install python-numpy python-scipy python-matplotlib

2.3、禁用nouveau驱动
ALT+CTRL+F1,进命令行;
$sudo service lightdm stop
$sudo apt-get --purge remove nvidia-*
新建黑名单,禁止系统自带驱动:$sudo vi /etc/modprobe.d/blacklist-nouveau.conf
输入: blacklist nouveau
options nouveau modset=0
保存推出(:wq)
然后执行:$sudo update-initramfs –u #更新内核
执行 $lspci | grep nouveau,查看是否有内容,没有说明禁用成功,如果有内容,就$sudo reboot
重启:$sudo reboot
重启后,在登录界面,不要登录进桌面,直接ALT+CTRL+F1进命令行

2.4、安装cuda8.0
进入cuda_8.0.27_linux.run所在目录
$cd /home/smith/Downloads
$sudo chmod +x cuda_8.0.27_linux.run
$sudo ./cuda_8.0.27_linux.run

按q键退出RELU文档,按照如下选择,显卡驱动一定要选n,不装
Do you accept the previously read EULA?
accept/decline/quit: accept

Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 361.62?
(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/zhou ]:
Installing the CUDA Toolkit in /usr/local/cuda-8.0 …

完成后看到
Driver: Not Selected
Toolkit: Installed in /usr/local/cuda-8.0
Samples: Installed in /home/zhou,but missing recommended libraries

最后,配置环境变量,直接放在系统配置文件profile里面:
$sudo gedit /etc/profile
在最后面加入两行代码:
export PATH=/usr/local/cuda-8.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH
保存退出.
执行:$sudo ldconfig
此时,显卡驱动没装,等待下一步显卡驱动装好后检查cuda8.0是否装好。

关于卸载cuda:
$cd /usr/local/cuda-8.0/bin
$sudo ./uninstall_cuda_8.0.pl

2.5、显卡驱动安装
进入显卡驱动目录
$cd /home/smith/Downloads
$sudo su
$sudo ./NVIDIA-Linux-x86_64-367.44.run
一路按照提示选择安装,具体不记得了,主要有接受协议,在系统内核注册,用新路径注册,更新X-server,安装完成后会自动回到命令行
重启电脑:$sudo reboot
输入密码进入桌面

2.6、检查之前的安装
此时在home目录下会出现文件夹NVIDIA_CUDA-8.0_Samples,打开终端,进入该目录:
$sudo make –j8 #编译samples,我电脑8线程,全开编译
等待2分钟左右,编译完成,执行下条指令:
$sudo ./1_Utilities/deviceQuery/deviceQuery
出现如下信息,cuda8.0安装成功(忘记截图了,下面信息是gtx670装cuda6.5的)
./deviceQuery Starting...

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

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 670"
CUDA Driver Version / Runtime Version 6.5 / 6.5
CUDA Capability Major/Minor version number: 3.0
Total amount of global memory: 4095 MBytes (4294246400 bytes)
( 7) Multiprocessors,(192) CUDA Cores/MP: 1344 CUDA Cores
GPU Clock rate: 1098 MHz (1.10 GHz)
Memory Clock rate: 3105 Mhz
Memory Bus Width: 256-bit
L2 Cache Size: 524288 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 bdeclared as function returning an arrayytes
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 Bus ID / PCI location ID: 1 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery,CUDA Driver = CUDART,CUDA Driver Version = 6.5,CUDA Runtime Version = 6.5,NumDevs = 1,Device0 = GeForce GTX 670
Result = PASS
可以看到,最后出现了PASS,安装cuda完成。

还可以:
$nvcc –version
查看nvcc版本

$nvidia–smi
显示(具体机子不一样,这图不是我的):

2.7、Atlas安装
sudo apt-get install libatlas-base-dev
实际上这步在之前安装依赖项时已经安装过了。

2.8、cuDNN安装

$tar -zxvf cudnn-7.0-linux-x64-v5.1-prod.tgz
$cd cuda
$sudo cp lib64/lib* /usr/local/cuda/lib64/
$sudo cp include/cudnn.h /usr/local/cuda/include/
更新软连接:
$cd /usr/local/cuda/lib64/
$sudo chmod +r libcudnn.so.5.1.5
$sudo ln -sf libcudnn.so.5.1.5 libcudnn.so.5
$sudo ln -sf libcudnn.so.5 libcudnn.so
更新设置:
$sudo ldconfig

2.9拉取caffe源码
git clone https://github.com/BVLC/caffe.git

2.10.安装python的pip和easy_install,方便安装软件包(超慢的下载。。。)
$sudo wget --no-check-certificate https://bootstrap.pypa.io/ez_setup.py
$sudo python ez_setup.py --insecure
$wget https://bootstrap.pypa.io/get-pip.py
$sudo python get-pip.py

2.11.安装python依赖(路径根据自己的目录可能要调一下)
$cd caffe/python

$sudo su
$for req in $(cat requirements.txt); do pip install $req; done
这步安装也有点慢,别急,等会儿,先去干点别的 ^_^(干点别的回来还没好。。。)

2.12.编辑caffe所需的Makefile文件,配置

$cd caffe
$cp Makefile.config.example Makefile.config
$sudo gedit Makefile.config
$Makefile.config里面有依赖库的路径,及各种编译配置,取消USE_CUDNN := 1的注释,开启GPU,USE_LMDB := 1
配置运行环境,调用CUDA库,在/etc/ld.so.conf.d目录新建caffe.conf,
$sudo gedit /etc/ld.so.conf.d/caffe.conf
添加:
/usr/local/cuda/lib64
保存退出,执行:
$sudo ldconfig

2.13、编译caffe、pycaffe
进入caffe根目录,
$sudo make –j4
测试一下结果,
$sudo make test –j4
$sudo make runtest –j4
(runtest中个别没通过没关系,不影响使用)
$sudo make pycaffe –j4
$sudo make distribute
第三部分,拿cifar10测试下效果 $cd /home/smith/caffe $sudo sh data/cifar10/get_cifar10.sh (脚本下载速度太慢,找个迅雷下载拷进来,再照脚本解压) # sudo sh examples/cifar10/create_cifar10.sh # sudo sh examples/cifar10/train_quick.sh 下面,网络开始初始化、训练了,loss会开始下降,很快的就会出现优化完成。 PS: 1、尝试了安装opencv3.0.0,可惜失败了,有博客说是cuda8.0版本太新,不支持了,后面有时间再搞了。 2、Python出现import caffe出错时,添加; import sys sys.path.append(“/home/smith/caffe/python”) or: $export PYTHONPATH=$PYTHONPATH:/home/smith/caffe/python

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