ubuntu16.04+cuda9.2+cudnn7.1.4+opencv3.1.4安装caffe

注：该文章建立于cuda和cudnn都已经安装成功的前提下进行。

参考博客：https://blog.****.net/u012939880/article/details/82887985

 

1.准备caffe环境（官网安装教程：http://caffe.berkeleyvision.org/installation.html）

 

a）cuda ，官方说需要cuda7以上版本，这里我们已经安装好了cuda9.2最新版本

 

b）Blas，好像是一个数学的计算库, 包括矩阵计算啥的, 有MKL, ATLAS, OpenBLAS等都可以, 默认支持MKL, 这个选择不同配置的makefile-config. 我看OpenBLAS亲切, 于是就上的OpenBLAS

#sudo apt-get install libopenblas-dev

 

c）Boost, 这个是c++的一个封装库, 很强大, 有很多模块. 如果不考虑pycaffe啥的, 可以直接用apt安装(# apt install libboost-all-dev), 但是我这里用的anaconda安装的python3.6, 考虑到apt安装的默认python3.5版本不兼容, 所以下载的boost重新编译的.

Boost没有立即git最新版本, 是以前下载的(1.66.0), 但和最新版本应该不超过三个月, 相差不大, 所以最新版本(1.68.0)应该也没有很大的问题, 灵活处理, 欢迎交流.

下载,编译与安装:

新建一个文件夹叫workspace

# cd workspace

# wget https://dl.bintray.com/boostorg/release/1.66.0/source/boost_1_66_0.tar.gz

# tar xf boost_1_66_0.tar.gz

# cd boost_1_66_0

# ./bootstrap.sh --with-libraries=python --with-toolset=gcc

# ./b2 cflags='-fPIC' cxxflags='-fPIC' –with-python include="/root/anaconda3/include/python3.6m/"

# 这里的include根据自己的python环境进行修改即可

# ./b2 install

d） Protobuf, gflags, hdf5, glog. 前面三个在Ubuntu上可以用apt进行安装, 最后一个要git源码安装.

# apt install libprotobuf-dev libgflags-dev libhdf5-dev

# cd $HOME/workspace && git clone https://github.com/google/glog.git

# cd glog

# ./autogen.sh && ./configure && make && make install

#如果没有安装autogen工具, 百度安装下即可，好像是sudo apt-get install autogen就可以安装autogen

 

e）opencv

# sudo apt-get install libopencv-dev

 

f）lo库，官方需要lmdb和leveldb

#sudo apt-get install liblmdb-dev libleveldb-dev

 

或者以上步骤通过下面的依赖包安装

1.安装依赖包：

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 libopenblas-dev liblapack-dev libatlas-base-dev

 

sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

 

sudo apt-get install git cmake build-essential

 

2.github网站https://github.com/BVLC/caffe下载caffe-master.zip

 

 点击路色clone or download 下载zip文件，然后解压到home目录下并改名为caffe

 

1）进入caffe目录

cd caffe

2）将Makefilel.config.example文件复制一份更名为 Makefile.config

sudo cp Makefilel.config.example Makefile.config

 

3）修改Makefile.config文件内容（这里主要是配置问题，请各自根据需要进行配置，只要有一点英语基础的同学大概都能看得懂这个文件）

sudo gedit Makefile.config

 

以下是我的Makefile.config文件，改动的地方已标注

# 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 := 1

# USE_LEVELDB := 0

# USE_LMDB := 1

# This code is taken from https://github.com/sh1r0/caffe-android-lib

# USE_HDF5 := 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 through *_61 lines for compatibility.

# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.

# For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility.

#CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \

# -gencode arch=compute_35,code=sm_35

CUDA_ARCH := -gencode arch=compute_50,code=sm_50 \

-gencode arch=compute_52,code=sm_52 \

-gencode arch=compute_60,code=sm_60 \

-gencode arch=compute_61,code=sm_61 \

-gencode arch=compute_61,code=compute_61

 

# BLAS choice:

# atlas for ATLAS (default)

# mkl for MKL

# open for OpenBlas

#BLAS := atlas

BLAS := 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 := /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)/anaconda3

# 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 python3.6m

PYTHON_INCLUDE := /usr/include/python3.6m \

$(ANACONDA_HOME)/include/python3.6m \

/usr/lib/python3.6/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 \

/home/lab305/workplace/boost_1_66_0/stage/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 /usr/include/hdf5/serial/

LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial/

 

# 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

 

# NCCL acceleration switch (uncomment to build with NCCL)

# https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)

# USE_NCCL := 1

 

# 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 ?= @

 

e）可以编译了

在caffe目录下：

#sudo make all -j8

这里的-j8只是为了让make速度便快，应该是线程的问题，还可以直接make all 或者make all -j4,当然应该是make all -j8最快了。

 

然后

#sudo make pycaffe

#sudo make test -j8

#sudo make runtest -j8

 

所有都通过了，就表示编译通过！

 

3.python环境添加caffe

 

a）在.bashrc中添加一下内容

export PYTHONPATH="/var/source/caffe/python:$PYTHONPATH"

export LD_LIBRARY_PATH="/usr/lib/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu/hdf5/serial:/usr/local/lib:$LD_LIBRARY_PATH"

b）更新环境

#source $HOME/.bashrc

最后打开python

#python

输入

>>import caffe

没有出现错误！！！安装成功！！