1. 环境准备
在Ubuntu下用apt-get大法搞定所有依赖包。opencv和python建议通过别的方法自行安装
sudo apt-get install git 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 python-dev sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
2. 安装cuda8.0。从CUDA官网下载目前最新版的cuda8.0的.run文件。
安装之前先按下ctrl+alt+F1进入命令行界面，关闭图形界面
sudo stop lightdm
进入到下载的cuda8.0安装包所在文件夹下，直接在命令行运行.run文件开始安装
进入并accept完声明后注意第一个选项，询问你是否需要安装NVIDIA驱动程序，如果电脑已经安装过适合自己显卡的显卡驱动，此处一定得选择为no,后面的基本可以默认选择就好，根据自己需要。如果之前没有安装过NVIDIA驱动则第一个需要选择YES进行驱动的安装然后再进行cuda的安装。
打开图形界面，ctrl+alt+F7回到图形界面：
sudo start lightdm
查看驱动是否安装成功：
nvidia-smi
3. 安装配置cudnn5.1。下载最新的cudnn-8.0-linux-x64-v5.1.tgz文件。将解压出的cuda文件夹下的lib64文件夹里的静态库和动态库文件一并拷到cuda8.0的安装目录下的lib64中去，默认是/usr/local/cuda/lib64/。同时将include文件夹下的头文件cudnn.h拷贝到cuda8.0的include中去，默认为/usr/local/cuda/include/
tar -zxvf cudnn-8.0-linux-x64-v5.1.tgz cd cuda sudo cp lib64/* /usr/local/cuda/lib64/ sudo cp include/cudnn.h /usr/local/cuda/include/
建立软连接
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 sudo ldconfig
4. 设置环境变量
在/etc/profile中添加CUDA环境变量
sudo gedit /etc/profile在文件尾部添加以下内容：
PATH=/usr/local/cuda/bin:$PATH
exportPATH
保存后,执行下列命令,使环境变量立即生效
source /etc/profile同时需要添加lib库路径： 在 /etc/ld.so.conf.d/加入文件 cuda.conf,
sudo gedit /etc/ld.so.conf.d/cuda.conf添加以下内容：
/usr/local/cuda/lib64
保存后，执行下列命令使之立刻生效
sudo ldconfig
此时可以通过命令行查看cuda是否安装成功：
nvcc -V
5. 通过git获取github上的官方caffe源码。
git clone https://github.com/bvlc/caffe.git
6. 配置caffe的config文件。
cd caffe/ cp Makefile.config.example Makefile.config #以example为备份 sudo gedit Makefile.config
根据自己需要进行配置，将cudnn的注释解开，并确保opencv（2或者3），python(或anaconda)，cuda等路径的正确
7. 编译caffe
make all && make pycaffe
8. 相对于通过python命令import caffe到python库中，建议是在需要使用caffe的项目中再将库引进去，因为caffe版本可能会有多个，方法如下：
import sys
sys.path.append('/home/yourname/caffe/python')
import caffe
这样caffe即可正常使用了
(0) ubuntu中很可能碰到的问题，系统的protobuf和pyhton的protobuf版本不一致，可能在C++调用python接口时候遇到问题，此时最好的方法是将python的protobuf版本降回2.6
pip install protobuf==2.6.0
（1）import caffe时出错：can not find module skimage.io
sudo apt-get install python-numpy python-scipy python-matplotlib python-sklearn python-skimage python-h5py python-protobuf python-leveldb python-networkx python-nose python-pandas python-gflags Cython ipython sudo apt-get update（2）python caffe libcaffe.so.1.0.0-rc3: cannot open shared object file
cd /etc/ld.so.conf.d sudo gedit ./pycaffe.conf
/home/yourname/caffe/build/lib
别忘了再load一下：
sudo ldconfig
然后将caffe/python文件夹下的整个caffe文件夹拷贝到/usr/lib/python2.7/dist-packages下
cd caffe/ sudo cp -r ./python/caffe /usr/lib/python2.7/dist-packages
然后再import caffe就没问题了
（3）python caffe报错：No module named google.protobuf.internal
conda install protobuf
import google.protobuf
最后贴一份opencv3+anaconda3的Makefile.config
## 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)/anaconda3 PYTHON_INCLUDE := $(ANACONDA_HOME)/include \ $(ANACONDA_HOME)/include/python3.5m \ $(ANACONDA_HOME)/lib/python3.5/site-packages/numpy/core/include \ # Uncomment to use Python 3 (default is Python 2) PYTHON_LIBRARIES := boost_python-py34 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 # N.B. both build and distribute dirs are cleared on `make clean` 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 ?= @
