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 `TIFFIsTiled@LIBTIFF_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.