已有條件:

　　ubuntu14.04+cuda7.5+anaconda2(即python2.7)+matlabR2014a

上述已經裝好了,開始搭建caffe環境.

1. 裝cudnn5.1.3,參照:2015.08.17 Ubuntu 14.04+cuda 7.5+caffe安裝配置

詳情:先下載好cudnn-7.5-linux-x64-v5.1-rc.tgz安裝包(貌似需要官網申請)

解壓:

tar -zxvf cudnn-7.5-linux-x64-v5.1-rc.tgz cd cuda sudo cp lib64/lib* /usr/local/cuda/lib64/ sudo cp include/cudnn.h /usr/local/cuda/include/

更新軟鏈接:

cd /usr/local/cuda/lib64/ sudo chmod +r libcudnn.so.5.1.3 sudo ln -sf libcudnn.so.5.1.3 libcudnn.so.5 sudo ln -sf libcudnn.so.5 libcudnn.so sudo ldconfig

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2.gcc,g++需要降級為4.7才能為caffe配置matlab接口.

查看gcc版本:

gcc --version

升級gcc:

　　手動編譯gcc的源代碼進行安裝:

sudo add-apt-repository ppa:ubuntu-toolchain-r/test sudo apt-get update sudo apt-get install gcc-4.9 sudo apt-get install g++-4.9

　　改一下/usr/bin/下的鏈接:

sudo su cd ../../usr/bin ln -s /usr/bin/g++-4.9 /usr/bin/g++ -f ln -s /usr/bin/gcc-4.9 /usr/bin/gcc -f

降級gcc:

　　仿照上述把鏈接改成4.7即可

3.安裝opencv3.0

參照:ubuntu14.04下配置使用openCV3.0

?裁取其中重要的一部分:

　$ unzip opencv-3.0.0-beta.zip$ cd opencv-3.0.0-beta$ mkdir release$ cd release$ cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D BUILD_TIFF=ON -D BUILD_NEW_PYTHON_SUPPORT=ON -D WITH_V4L=ON -D WITH_OPENGL=ON .. //注意CMakeList.txt在上一層文件夾$ make -j $(nproc) // make -j 多核處理器進行編譯(默認的make只用一核，很慢)，$(nproc)返回自己機器的核數$ make install //把編譯結果安裝到 /usr/local的 lib/ 和 include/下面

需要注意的是,在cmake中,一定要加上 -D BUILD_TIFF=ON,不然在編譯caffe時會出現錯誤:undefined reference to `TIFFIsTiled@LIBTIFF_4.0'

4.現在基本上都齊了,開始安裝并編譯caffe了.

源碼在https://github.com/BVLC/caffe,按照官方指南Installation或者2015.08.17 Ubuntu 14.04+cuda 7.5+caffe安裝配置開始安裝.

　　4.1 clone一份caffe源碼.

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git clone --recursive https://github.ocm/BVLC/caffe

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　　4.2 進入caffe/python,安裝所需要的python庫.

cd caffe/python for req in $(cat requirements.txt); do pip install $req; done

　　4.3 進入caffe,復制一份Makefile.config.example

cd ../ cp Makefile.config.example Makefile.config

　　4.4 按照自己的情況修改Makefile.config文件.我的config文件如下:

## 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 3OPENCV_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)/anaconda2 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# 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 ?= @

?注意這里我并沒有加matlab路徑,原因是現在不需要,且gcc是4.9版本的.等我需要用matlab接口了,首先需要降級gcc,再將matlab路徑放進去,我的matlab路徑是:MATLAB_DIR :=/usr/local/MATLAB/R2014a

　　4.5 編譯

make all -j8 make test make runtest

　　4.6 編譯pycaffe(/matcaffe)

make pycaffe #make matcaffe #when you need it

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好了,到此為止,caffe的編譯工作已基本完成.剩下的就是跑caffe自帶的例子了.這一部分以后再研究.

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轉載于:https://www.cnblogs.com/guanyu-zuike/p/5936245.html

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