安裝caffe過程記錄

    如今個人安裝的深度學習的軟件大都在臺式機上進行的，今天要裝的是caffe框架。個人操做系統是ubuntu14.04

• 先是安裝依賴項：

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 libgflags-dev libgoogle-glog-dev liblmdb-dev# ubuntu系統獨有安裝包

• opencv的安裝，由於我也是要作視頻圖片處理的。GitHub有的。

    htthttps://github.com/jayrambhia/Install-OpenCV，下載後解壓，而後進去該目錄，選擇本身的操做系統，好比個人是Ubuntu，執行

$ cd Ubuntu
$ chmod +x * 
$ ./opencv_latest.sh #這是最新的3.1.0

好吧，這個過程稍長，可能要30分鐘左右。

• ATLAS安裝

Automatic Tuned Linear Algebra Software，BLAS線性算法庫的優化版本,安裝步驟：

sudo apt-get install libatlas-base-dev

• boost標準庫安裝(好像沒有已經安裝了)

sudo apt-get install --no-install-recommends libboost-all-dev

最好只剩下caffe，用git命令或者是在https://github.com/BVLC/caffe 下載

下載後，進入Caffe目錄執行
cp Makefile.config.example Makefile.config

編譯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 := 0

# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 1
# 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 ?= @

最後是編譯，執行如下三條命令：
make all -j4
make test
make runtest

make all 發生以下錯誤：

/usr/bin/ld: cannot find -lcblas
/usr/bin/ld: cannot find -latlas
collect2: error: ld returned 1 exit status
make: *** [.build_release/lib/libcaffe.so.1.0.0-rc3] Error 1

安裝以下庫：

sudo apt-get install libatlas-dev
sudo apt-get install liblapack-dev
sudo apt-get install  libatlas-base-dev

現次make all -j4,又有幾個錯誤，真是愈來愈感到坑，不過認真好了一下，真是同一種錯誤，找不到cv，多是個人makefile.config沒有把opencv的選擇選上，選上以後,再次make all -j4就能夠了，好開心。

後面跑make test 與make runtest天然沒什麼問題：

由於當時還沒安裝matlab，因此沒有裝matlabwarp與pythowarp相關的，沒看具體看這些接口具體怎樣用。

參考：

https://github.com/BVLChttps://caffe/wiki/Ubuntu-15.10-Installation-Guide

http://weibo.com/p/2304189db078090102vdvx

http://www.cnblogs.com/cj695/p/4498270.html

http://caffe.berkeleyvision.org/installation.html#compilation（caffe官網）