Ubuntu16.04安装深度学习框架caffe详细步骤讲解
此次安装是带有GPU的安装,如果没有GPU只安装CPU,可参考我的另一篇文章,搞深度学习还得有显卡吃硬件,要不等着吐血吧。
1、安装环境:ubuntu16.04+caffe-master+cuda8.0+cudnnv5.1 ,安装环境所需的安装包我已打包上传,下载地址
2、安装caffe依赖包
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 |
3、ubuntu16.04最好是安装cuda8.0不要安最新,听官网的没错。下载cuda8.0,https://developer.nvidia.com/cuda-downloads
4、卸载以前的旧驱动准备换最新的
sudo apt-get --purge remove nvidia-\* |
5、禁止集成的nouveau驱动,必须禁止的否则没可能安装成功的。
sudo vi /etc/modprobe.d/blacklist-nouveau.conf |
blacklist-nouveau.conf文件可能并不存在不过没关系,向里面写入下面一句话,一个字都不能错
blacklist nouveau option nouveau modeset=0 |
保存退出后运行此命令,不能报错,报错了肯定就没禁止成功
sudo update-initramfs -u |
配置环境变量,直接用就行,反正是临时的
export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH |
6、安装显卡驱动,否则可能会报内核之类的错误
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7、通过 Ctrl + Alt + F1 进入文本模式,输入帐号密码登录,通过 Ctrl + Alt + F7 可返回图形化模式,在文本模式登录后
首先关闭桌面服务
sudo service lightdm stop |
8、开始安装cuda,直接运行命令,出现0%后一直安回车直到100%,全选 yes即可
./cuda_8.0.61_375.26_linux.run --no-opengl-libs |
9、其实这样还不算,toolkit工具还没有安装成功,可能用nvcc –V测试
sudo apt install nvidia-cuda-toolkit |
10、验证 CUDA 8.0 是否安装成功,输入下面命令
cd /usr/local/cuda-8.0/samples/1_Utilities/deviceQuery sudo make ./deviceQuery |
如果显示下面信息说明安装成功了。如果不行reboot重启一下
./deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: "GeForce GTX 650" CUDA Driver Version / Runtime Version 9.1 / 8.0 CUDA Capability Major/Minor version number: 3.0 Total amount of global memory: 978 MBytes (1025638400 bytes) ( 2) Multiprocessors, (192) CUDA Cores/MP: 384 CUDA Cores GPU Max Clock rate: 1058 MHz (1.06 GHz) Memory Clock rate: 2500 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 |
11、安装CUDNN加速
登录官网:https://developer.nvidia.com/rdp/cudnn-download ,下载对应 cuda 版本且 linux 系统的 cudnn 压缩包,注意官网下载 cudnn 需要注册帐号并登录,我是从国内下载的v5.1版本,下载地址,使用下面命令进行解压
cp cudnn-8.0-linux-x64-v5.1.solitairetheme8 cudnn-8.0-linux-x64-v5.1.tgz tar xvf cudnn-8.0-linux-x64-v5.1.tgz |
12、cuda和cudnn进行合并,按下面命令操作进入解压后的cuda目录
sudo cp include/cudnn.h /usr/local/cuda/include/ #复制头文件 sudo cp lib64/lib* /usr/local/cuda/lib64/ #复制动态链接库 cd /usr/local/cuda/lib64/ sudo rm -rf libcudnn.so libcudnn.so.5 #删除原有动态文件 sudo ln -s libcudnn.so.5.1.10 libcudnn.so.5 #生成软衔接 sudo ln -s libcudnn.so.5 libcudnn.so #生成软链接 |
13、到这基本也就完事了,下载caffe,解压,建立编译文件夹build-x64,进入后执行下面命令即可,大功告成
cmake .. make -j4 |
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