安裝簡介

重要！重要！重要！在進行這博文內的安裝步驟前，請先查閱我的另一篇部落格《Ubuntu 18.04系統淨化及環境配置》，按順序安裝裡面提到的包，否則後面的安裝是不會成功的。

當然了，如果你沒有NVIDIA獨立顯示卡，那麼你可以直接跳過環境配置的前三條，並在最後安裝Caffe前，對Makefile.config中關於前三條的選項（GPU/CUDA/cudnn）進行註釋，然後取消CPU的註釋即可

安裝前的環境配置

下載壓縮檔案

如果官網最新的caffe資源無法支援該網頁的配置方案，我這裡準備了可用的舊版本。點選此處下載舊版

準備壓縮檔案

將下載好的壓縮包提取到當前使用者根目錄下，並開啟該資料夾

修改配置檔案

找到Makefile.config.example，並重命名為Makefile.config，詳細配置內容可參考下面的資訊

## 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-8.0
# 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.
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 := mkl
# 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/R2014a
# 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)/anaconda
# 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)
ANACONDA_HOME  := $(HOME)/Anaconda3
PYTHON_LIBRARIES := boost_python-py35 python3.6m
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
		  $(ANACONDA_HOME)/include/python3.6m \
		  $(ANACONDA_HOME)/lib/python3.6/site-packages/numpy/core/include
# We need to be able to find libpythonX.X.so or .dylib.
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/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 ?= @
 

為安裝matcaffe介面做準備

A：修改Makefile，在該檔案411行附近，插入以下命令

CXXFLAGS += -std=c++11

開始安裝

cd caffe-master
make all -j
make matcaffe     #若最後一行出現“MEX 已成功完成”，則代表matcaffe介面編譯成功
make pycaffe

安裝測試

make mattest
make pytest

如果出現以下提示則證明呼叫matlab介面成功，否則請看系統調整進行修復

Totals:
   7 Passed, 0 Failed, 0 Incomplete.
   0.31512 seconds testing time.
 

如果出現以下提示則證明呼叫python介面成功，否則請看系統調整進行修復

Ran 51 tests in 13.732s
OK (skipped=8)

注意，若出現"(skipped=8)"字眼，說明在安裝前，沒有開啟"WITH_PYTHON_LAYER"，若開啟之後，這個提示不應該出現

部署pycaffe

A:複製Python介面

在當前使用者根目錄下開啟終端，輸入以下命令即可

cp -r caffe-master/python/caffe Anaconda3/lib/python3.6/site-packages

B:新增環境變數

在當前使用者根目錄下，找到.bashrc檔案並開啟，在最後新增下面兩行文字，儲存退出即可

# added by python caffe interface
export LD_LIBRARY_PATH="$HOME/caffe-master/build/lib:$LD_LIBRARY_PATH"

C:執行測試

重啟終端後，進入python環境，輸入以下命令，如果能正常載入caffe，則部署成功

系統調整

根據提示，缺了哪個就執行哪個命令即可

sudo ln -sf /usr/lib/x86_64-linux-gnu/libstdc++.so.6    /usr/local/MATLAB/R2014a/bin/glnxa64
sudo ln -sf /usr/local/cuda-8.0/lib64/libcublas.so.8.0  /usr/local/MATLAB/R2014a/bin/glnxa64
sudo ln -sf /usr/local/cuda-8.0/lib64/libcudart.so.8.0  /usr/local/MATLAB/R2014a/bin/glnxa64
sudo ln -sf /usr/local/cuda-8.0/lib64/libcudnn.so.5     /usr/local/MATLAB/R2014a/bin/glnxa64

部分文章摘錄，感謝以下的網友提供的解決方案

以下連結提供不存在連結使用的解決辦法

以下連結提供Python3的相容問題解決思路

以下連結用於解決Python3依賴項

以下連結主要為匹配Anaconda3(Python3.6.1)的boost

如果本篇文章能讓你成功安裝該套配置，請順手點個贊，畢竟這篇文章我重灌了N次系統和工具才完成的。。