记录我在ubuntu18.04+python3.6上安装caffe的经验

先贴一下我的配置：戴尔g7笔记本，显卡是gtx 1050ti，系统是ubuntu18.04，纯小白安装caffe大约花了一整天的时间。。。所以把出过的错记录下来。
主要是按照这篇链接来做的。link.

在nvdia我下载了cuda10.2。ubuntu18中控制台1是图形化界面，这个和链接中不同，我按的是ctrl+alt+F3进入tty3，输入sudo service lightdm stop却显示failed，后来发现我没有安装lightdm，安装即可。

在下载cudnn时注意一定要下载for linux的版本，不要下载deb版，否则安装后是没有cudnn.h的，之后在caffe编译过程中他提示cannot find cudnn.h就是因为这个原因，我这里下载的是v7.6.5。（速度极慢）

虽然nvidia的官网上说他要求gcc版本为7.3.0，但是我的gcc版本实际为7.4.0，但还是通过了测试，先就这样吧。。。。

opencv我第一次安装的是4.2.0，但是这会在之后的编译中造成对’cv::imread(cv::String const&, int)’未定义的引用的问题，所以我之后卸载opencv又重装了3.4.9版本，解决了这个问题。

之后就是最棘手的编译caffe阶段，原文链接中使用的是python2.7，但是我的电脑是python3.6，所以在这里我就直接贴上我的Makefile.config吧

## 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
# 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 
-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
# 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/R2019b
# 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 
/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
/usr/lib/python3
/usr/local/lib/python3.6
# 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 /usr/include/hdf5/serial/
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib/usr/lib/x86_64-linux-gnu/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 ?= @

编译过程中问题还蛮多的，但是大部分可以google后发现都在github项目的issue里，比较容易找到并解决。大部分问题感觉还是因为版本不同的库的链接问题，加上软连接就好了。还有就是注意每次修改后要先make clean。
下面是我自己Makefile的在编译过程中出错又修改部分，我也忘了具体是因为什么修改了。

LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial
ifeq ($(USE_OPENCV), 1)
LIBRARIES += opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs opencv_videoio
//这一行
ifeq ($(OPENCV_VERSION), 3)
LIBRARIES += opencv_imgcodecs
endif

之后在runtest时还有一个库的问题，“error while loading shared libraries: xxx.so.x”，在这里可以解决https://blog.csdn.net/sahusoft/article/details/7388617。

emmmmmm下一步就是解决matlab接口的问题吧

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