1、前期准備
安裝依賴
sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
sudo yum install gflags-devel glog-devel lmdb-devel
sudo yum atlas-devel cmake glibc-devel gcc-gfortran autoconf automake gcc gcc-c++ git libtool make pkgconfig zlib-devel SDL* yasm* python-devel cmake* git ncurses* *freetype2 nasm nasm*
安裝Boost
安裝glog
安裝protobuf
安裝lmdb
安裝leveldb
安裝gflags
安裝hdf5
***切記，上述依賴需要安裝到/usr/local下面的目錄，否則編譯時會提示找不到相關庫文件。
安裝ffmpeg
安裝opencv
安裝cuda
查看cuda版本：cat /usr/local/cuda/version.txt
安裝cudnn
如果將來要采用python調用caffe的話，必須將numpy提前裝好：
pip install numpy pip install pandas pip install ipython
2、使用安Makefile.config裝及編譯caffe
下載caffe並移動到想存放的路徑：
修改Makefile.config文件：
進入caffe目錄 cp Makefile.config.example Makefile.config vim Makefile.config
文件的修改需要根據自己的具體情況，下面是一個示例：
## 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
"CuDNN是NVIDIA專門針對Deep Learning框架設計的一套GPU計算加速庫，用於實現高性能的並行計算,在有GPU並且安裝CuDNN的情況下可以打開即將注釋去掉。"
# CPU-only switch (uncomment to build without GPU support).
#CPU_ONLY := 1
"表示是否用GPU,如果只有CPU這里要打開"
# uncomment to disable IO dependencies and corresponding data layers
USE_OPENCV := 1
"因為要用到OpenCV庫所以要打開，下面這兩個選項表示是選擇Caffe的數據管理第三方庫，兩者都不打開 Caffe默認用的是LMDB，這兩者均是嵌入式數據庫管理系統編程庫。"
# 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
"當需要讀取LMDB文件時可以取消注釋，默認不打開。"
# Uncomment if you're using OpenCV 3
OPENCV_VERSION := 2.4.10
"用pkg-config --modversion opencv命令查看opencv版本"
# 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++
"linux系統默認使用g++編譯器，OSX則是clang++。"
# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
"CUDA的安裝目錄"
# 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
"這些參數需要根據GPU的計算能力
(http://blog.csdn.net/jiajunlee/article/details/52067962)來進行設置，6.0以下的版本不支持×_50的計算能力。"
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := open
"如果用的是ATLAS計算庫則賦值atlas，MKL計算庫則用mkl賦值，OpenBlas則賦值open。"
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
BLAS_INCLUDE := /usr/local/OpenBlas/include
BLAS_LIB := /usr/local/OpenBlas/lib
"blas庫安裝目錄"
# 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
"如果不是安裝在標准路徑則要指明"
# 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
"matlab安裝庫的目錄"
# 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.
# 這里特別要注意，使用python -c "import numpy; print numpy.__file__"查看numpy的路徑
PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/lib64/python2.7/site-packages/numpy/core/include
# /usr/lib/python2.7/dist-packages/numpy/core/include
"python安裝目錄"
# 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
<font color="green">python庫位置</font>
# 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 $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
"所用的GPU的ID編號"
# enable pretty build (comment to see full commands)
Q ?= @
編譯：
make clean #如果是第一次編譯，則不需要執行這一步驟 make all -j16 #-j16表示開16個線程並行編譯，可以大大減少編譯時間，但是線程數不要超過cpu核數 make test -j16 make runtest
編譯pycaffe：
#編譯 make pycaffe -j16 #添加環境變量 vim ~/.bashrc 將export PYTHONPATH=/home/wanghh/caffe/python:$PYTHONPATH添加到文件中。 source ~/.bashrc 使更改生效。 這樣，在其他地方打開python，也可以import caffe了。
3、使用cmake安裝及編譯caffe
（1）進入caffe根目錄：cd xxx/xxx/caffe
（2）創建build文件夾並配置
mkdir build cd build cmake ..
cmake ..如果報錯，則使用cmake -D xxx=xxxxxx ..來修改參數（例如：cmake -D BLAS=open ..）
（3）編譯
make -j32 # -j后面為cpu核數，可小於或等於實際cpu核數
4、測試
進入caffe目錄 sh data/mnist/get_mnist.sh sh examples/mnist/create_mnist.sh sh examples/mnist/train_lenet.sh
出現下圖所示結果：
至此，caffe安裝成功
5、可能出現的問題
python/caffe/_caffe.cpp:10:31: fatal error: numpy/arrayobject.h: No such file or directory（make pycaffe -j16 時）
原因：numpy路徑設置錯誤。
解決方案：使用python -c "import numpy; print numpy.__file__"查看numpy的路徑，修改Makefile.config,如示例：
PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/lib64/python2.7/site-packages/numpy/core/include
# /usr/lib/python2.7/dist-packages/numpy/core/include
./build/tools/caffe: error while loading shared libraries: libcudart.so.8.0: cannot open shared object file: No such file or directory（./examples/mnist/train_lenet.sh時）
原因：
解決方案：
sudo cp /usr/local/cuda-8.0/lib64/libcudart.so.8.0 /usr/local/lib/libcudart.so.8.0 && sudo ldconfig sudo cp /usr/local/cuda-8.0/lib64/libcublas.so.8.0 /usr/local/lib/libcublas.so.8.0 && sudo ldconfig sudo cp /usr/local/cuda-8.0/lib64/libcurand.so.8.0 /usr/local/lib/libcurand.so.8.0 && sudo ldconfig
make -j24時 ：
/usr/bin/ld: /usr/local/lib/libpython2.7.a(object.o): relocation R_X86_64_32 against `.rodata.str1.1' can not be used when making a shared object; recompile with -fPIC
或：
/usr/bin/ld: /usr/local/lib/libpython2.7.a(abstract.o): relocation R_X86_64_32S against `_Py_NotImplementedStruct' can not be used when making a shared object; recompile with -fPIC
/usr/local/lib/libpython2.7.a: could not read symbols: Bad value
原因：
解決方案：
make all -j16時：
error -- unsupported GNU version! gcc versions later than 5 are not supported!
原因：
解決方案：