本来实在windows 10上尝试安装caffe,装了一天没装上,放弃; 改在windows上装ubuntu的双系统,装了一个下午,不小心windows的系统盘被锁死了,也不会unlock?只好含泪卸掉了windows10,只装ubuntu 15.10。过程真是让人揩一把辛酸泪。。。不过又在ubuntu中用错了一个命令,系统崩溃,重新安装win8.1 + ubuntu 14.04, 成功,继续安装caffe。。。
安装:
ubuntu 14 .04,
1. 安装开发所需依赖包:
sudo apt-get install build-essential # basic requirement sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler #required by caffe
2. 安装CUDA 7.5:
根据Nvidia官方文档http://developer.download.nvidia.com/compute/cuda/7.5/Prod/docs/sidebar/CUDA_Installation_Guide_Linux.pdf一步一步安装
1) 确定GPU支持CUDA
lspci | grep -i nvidia
结果显示:
到
http://developer.nvidia.com/cuda-gpus去验证,发现支持CUDA
2) 确定linux版本支持CUDA:
uname -m && cat /etc/*release
结果显示:
3) 确定系统已经安装了gcc:
gcc --version
结果显示:
4) 确定系统已经安装了正确的Kernel Headers和开发包:
查看系统正在运行的kernel版本:
uname -r
结果:4.2.0-35-generic,这个是必须安装的kernel headers和开发包的版本
安装对应的kernels header和开发包:
sudo apt-get install linux-headers-$(uname -r)
5) 安装CUDA:下载对应版本的cuda,从https://developer.nvidia.com/cuda-downloads,下载deb(network)版
首先进行md5校验:
md5sum cuda-repo-ubuntu1404_7.5-18_amd64.deb
对比相同,然后使用deb文件进行安装:
sudo dpkg -i cuda-repo-ubuntu1404_7.5-18_amd64.deb sudo apt-get update
sudo apt-get install cuda
重启,完成cuda安装
3. 安装cuDNN
下载从https://developer.nvidia.com/rdp/cudnn-download cuDNN,版本为cudnn-7.0-linux-x64-v3.0-prod.tgz
tar -zxvf cudnn-7.0-linux-x64-v3.0-prod.tgz cd cuda sudo cp lib64/* /usr/local/cuda/lib64/ sudo cp include/cudnn.h /usr/local/cuda/include/
更新软链接?不懂,照做
cd /usr/local/cuda/lib64 sudo rm -rf libcudnn.so libcudnn.so.7.0 sudo ln -s libcudnn.so.7.0.64 libcudnn.so.7.0 sudo ln -s libcudnn.so.7.0 libcudnn.so
4. 设置环境变量:
在/etc/profile中添加CUDA 环境变量
sudo gedit /etc/profile,在打开的文件中加入如下两句话
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
保存后,使环境变量立即生效:source /etc/profile
5. 安装cuda samples
进入/usr/local/cuda/samples,执行下面的命令来build samples
sudo make all -j4
全部编译完成后,进入 samples/bin/x86_64/linux/release, 运行deviceQuery: ./deviceQuery
如果出现显卡信息,则驱动及显卡安装成功,结果如下:
安装成功!
6. 安装Intel MKL,openBlas 或Atlas:
我选择的是Atlas,为caffe默认使用的,不要额外配置,安装命令
sudo apt-get install libatlas-base-dev
7. 安装opencv:
首先安装必须的包
sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
从opencv官网下载最新版本opencv,opencv-3.1.0.zip
解压unzip opencv-3.1.0.zip
编译
cd opencv-3.1.0 mkdir release cd release cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local .. make sudo make install
测试是否安装成功:
mkdir ~/opencv-workshop cd ~/opencv-workshop/ gedit DisplayImage.cpp gedit CMakeLists.txt cmake . make
其中 Display.cpp的代码为
#include <stdio.h> #include <opencv2/opencv.hpp> using namespace cv; int main(int argc, char** argv) { Mat image; image = imread(argv[1], 1); if (!image.data) { printf("No image data\n"); return -1; } namedWindow("display", WINDOW_AUTOSIZE); imshow("display", image); waitKey(0); return 0; }
其中 CMakeList.txt的内容是
cmake_minimum_required(VERSION 2.8) project(DisplayImage) find_package(OpenCV REQUIRED) add_executable(DisplayImage DisplayImage.cpp) target_link_libraries(DisplayImage ${OpenCV_LIBS})
脚本运行后,运行可执行文件,看是否能正确显示图片,能,则成功
./DisplayImage ~/图片/2016-04-13\ 21\:35\:01屏幕截图.png
8. 安装caffe所需要的Python环境Anaconda:
按caffe推荐,python环境使用Anaconda,去官网下载安装包http://conda.pydata.org/miniconda.html,
下载Anaconda2-4.0.0-Linux-x86_64.sh,切换到文件所在路径,执行
bash Anaconda2-4.0.0-Linux-x86_64.sh
按照提示进行安装。
安装好后,在/etc/ld.so.conf中添加一下路径:/home/wm/anaconda2/lib
在~/.bashrc最后添加下面一句:
export LD_LIBRARY_PATH="/home/wm/anaconda2/lib:$LD_LIBRARY_PATH"
9. 安装python依赖库
下载caffe源码,解压后进入caffe-master下的python目录,
(首先要安装python-pip
sudo apt-get install python-pip
)执行如下命令
for req in $(cat requirements.txt); do pip install $req; done
10. 编译Caffe
进入caffe-master目录,复制一份Makefile.config.examples
cp Makefile.config.example Makefile.config
修改Makefile.config中的路径,只需要将默认的Python路径注释掉,去掉对Anaconda路径的注释,并且开启CuDNN和Opencv3的支持即可,修改好的如下:
## Refer to http://caffe.berkeleyvision.org/installation.html # Contributions simplifying and improving our build system are welcome! # cuDNN acceleration switch (uncomment to build with cuDNN). USE_CUDNN := 1 # CPU-only switch (uncomment to build without GPU support). # CPU_ONLY := 1 # uncomment to disable IO dependencies and corresponding data layers # USE_OPENCV := 0 # USE_LEVELDB := 0 # USE_LMDB := 0 # uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary) # You should not set this flag if you will be reading LMDBs with any # possibility of simultaneous read and write # ALLOW_LMDB_NOLOCK := 1 # Uncomment if you\'re using OpenCV 3 OPENCV_VERSION := 3 # To customize your choice of compiler, uncomment and set the following. # N.B. the default for Linux is g++ and the default for OSX is clang++ # CUSTOM_CXX := g++ # CUDA directory contains bin/ and lib/ directories that we need. CUDA_DIR := /usr/local/cuda # On Ubuntu 14.04, if cuda tools are installed via # "sudo apt-get install nvidia-cuda-toolkit" then use this instead: # CUDA_DIR := /usr # CUDA architecture setting: going with all of them. # For CUDA < 6.0, comment the *_50 lines for compatibility. CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \ -gencode arch=compute_20,code=sm_21 \ -gencode arch=compute_30,code=sm_30 \ -gencode arch=compute_35,code=sm_35 \ -gencode arch=compute_50,code=sm_50 \ -gencode arch=compute_50,code=compute_50 # BLAS choice: # atlas for ATLAS (default) # mkl for MKL # open for OpenBlas BLAS := atlas # Custom (MKL/ATLAS/OpenBLAS) include and lib directories. # Leave commented to accept the defaults for your choice of BLAS # (which should work)! # BLAS_INCLUDE := /path/to/your/blas # BLAS_LIB := /path/to/your/blas # Homebrew puts openblas in a directory that is not on the standard search path # BLAS_INCLUDE := $(shell brew --prefix openblas)/include # BLAS_LIB := $(shell brew --prefix openblas)/lib # This is required only if you will compile the matlab interface. # MATLAB directory should contain the mex binary in /bin. # MATLAB_DIR := /usr/local # MATLAB_DIR := /Applications/MATLAB_R2012b.app # NOTE: this is required only if you will compile the python interface. # We need to be able to find Python.h and numpy/arrayobject.h. # PYTHON_INCLUDE := /usr/include/python2.7 \ # /usr/lib/python2.7/dist-packages/numpy/core/include # Anaconda Python distribution is quite popular. Include path: # Verify anaconda location, sometimes it\'s in root. ANACONDA_HOME := $(HOME)/anaconda PYTHON_INCLUDE := $(ANACONDA_HOME)/include \ $(ANACONDA_HOME)/include/python2.7 \ $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \ # Uncomment to use Python 3 (default is Python 2) # PYTHON_LIBRARIES := boost_python3 python3.5m # PYTHON_INCLUDE := /usr/include/python3.5m \ # /usr/lib/python3.5/dist-packages/numpy/core/include # We need to be able to find libpythonX.X.so or .dylib. # PYTHON_LIB := /usr/lib PYTHON_LIB := $(ANACONDA_HOME)/lib # Homebrew installs numpy in a non standard path (keg only) # PYTHON_INCLUDE += $(dir $(shell python -c \'import numpy.core; print(numpy.core.__file__)\'))/include # PYTHON_LIB += $(shell brew --prefix numpy)/lib # Uncomment to support layers written in Python (will link against Python libs) # WITH_PYTHON_LAYER := 1 # Whatever else you find you need goes here. INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib # If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies # INCLUDE_DIRS += $(shell brew --prefix)/include # LIBRARY_DIRS += $(shell brew --prefix)/lib # Uncomment to use `pkg-config` to specify OpenCV library paths. # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.) # USE_PKG_CONFIG := 1 BUILD_DIR := build DISTRIBUTE_DIR := distribute # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 # DEBUG := 1 # The ID of the GPU that \'make runtest\' will use to run unit tests. TEST_GPUID := 0 # enable pretty build (comment to see full commands) Q ?= @
保存退出,编译
make all make test make runtest
没有报错,成功。
11. 使用MNIST数据集进行测试
1) 数据预处理:
sh data/mnist/get_mnist.sh
2) 重建lmdb/leveldb文件,命令
sh examples/mnist/create_mnist.sh
3) 训练mnist,命令
sh examples/mnist/train_lenet.sh
这是可以看到caffe的训练过程,至此caffe配置已成功:
接下来会学习caffe的使用。。。 加油
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