已有条件:
ubuntu14.04+cuda7.5+anaconda2(即python2.7)+matlabR2014a
上述已经装好了,开始搭建caffe环境.
1. 装cudnn5.1.3,参照:2015.08.17 Ubuntu 14.04+cuda 7.5+caffe安装配置
详情:先下载好cudnn-7.5-linux-x64-v5.1-rc.tgz安装包(貌似需要官网申请)
解压:
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/
更新软链接:
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
2.gcc,g++需要降级为4.7才能为caffe配置matlab接口.
查看gcc版本:
gcc --version
升级gcc:
手动编译gcc的源代码进行安装:
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/下的链接:
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
降级gcc:
仿照上述把链接改成4.7即可
3.安装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在上一层文件夹 $ make -j $(nproc) // make -j 多核处理器进行编译(默认的make只用一核，很慢)，$(nproc)返回自己机器的核数 $ make install //把编译结果安装到 /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,不然在编译caffe时会出现错误:undefined reference to `[email protected]_4.0‘
4.现在基本上都齐了,开始安装并编译caffe了.
源码在https://github.com/BVLC/caffe,按照官方指南Installation或者2015.08.17 Ubuntu 14.04+cuda 7.5+caffe安装配置开始安装.
4.1 clone一份caffe源码.
git clone --recursive https://github.ocm/BVLC/caffe
4.2 进入caffe/python,安装所需要的python库.
cd caffe/python for req in $(cat requirements.txt); do pip install $req; done
4.3 进入caffe,复制一份Makefile.config.example
cd ../ cp Makefile.config.example Makefile.config
4.4 按照自己的情况修改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 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)/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 ?= @
注意这里我并没有加matlab路径,原因是现在不需要,且gcc是4.9版本的.等我需要用matlab接口了,首先需要降级gcc,再将matlab路径放进去,我的matlab路径是:MATLAB_DIR :=/usr/local/MATLAB/R2014a
4.5 编译
make all -j8 make test make runtest
4.6 编译pycaffe(/matcaffe)
make pycaffe #make matcaffe #when you need it
好了,到此为止,caffe的编译工作已基本完成.剩下的就是跑caffe自带的例子了.这一部分以后再研究.
时间： 2024-10-09 12:58:43