# Contributions simplifying and improving our build system are welcome!


# cuDNN acceleration switch (uncomment to build with cuDNN).
# USE_CUDNN := 1
"CuDNN是NVIDIA專門針對Deep Learning框架設計的一套GPU計算加速庫，用於實現高效能的平行計算,在有GPU並且安裝CuDNN的情況下可以開啟即將註釋去掉。"


# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1
"表示是否用GPU,如果只有CPU這裡要開啟"


# uncomment to disable IO dependencies and corresponding data layers
USE_OPENCV := 1
"因為要用到OpenCV庫所以要開啟，下面這兩個選項表示是選擇Caffe的資料管理第三方庫，兩者都不開啟 Caffe預設用的是LMDB，這兩者均是嵌入式資料庫管理系統程式設計庫。"
# 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
"當需要讀取LMDB檔案時可以取消註釋，預設不開啟。"


# Uncomment if you're using OpenCV 3
OPENCV_VERSION := 2.4.10
"用pkg-config --modversion opencv命令檢視opencv版本"


# 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++
"linux系統預設使用g++編譯器，OSX則是clang++。"


# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
"CUDA的安裝目錄"
# 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
"這些引數需要根據GPU的計算能力，從CUDA 8.0開始compute capability 2.0和2.1被棄用了，所以要將-gencode arch=compute_20,code=sm_20 和-gencode arch=compute_20,code=sm_21這兩行刪除。
(http://blog.csdn.net/jiajunlee/article/details/52067962)來進行設定，6.0以下的版本不支援×_50的計算能力。"(其他可以根據gpu的計算能力選擇刪掉或保留)
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := open
"如果用的是ATLAS計算庫則賦值atlas，MKL計算庫則用mkl賦值，OpenBlas則賦值open。OpenBlas的安裝方法在下面"
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
BLAS_INCLUDE := /usr/local/OpenBlas/include
BLAS_LIB := /usr/local/OpenBlas/lib
"blas庫安裝目錄"
# 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
"如果不是安裝在標準路徑則要指明"
# 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
"matlab安裝庫的目錄"
# 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
"python安裝目錄，如果安裝的是anaconda,那需要用anaconda的路徑，我這裡用的是anaconda，所以把python註釋掉"
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
 HOME := /home/zzm/usr
 "增加anaconda的home路徑"
 ANACONDA_HOME := $(HOME)/anaconda2
 PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
        # $(ANACONDA_HOME)/include/python2.7 \
        # $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
 "這裡用的是anaconda-python，所以需要去掉該出注釋"
# 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
<font color="green">python庫位置</font>
# 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
"所用的GPU的ID編號"


# enable pretty build (comment to see full commands)
Q ?= @
 

OpenBLAS是高效能多核BLAS庫，是GotoBLAS2 1.13 BSD版本的衍生版。專案主頁是 https://github.com/xianyi/OpenBLAS 。
通常的編譯安裝流程如下：

make CC=gcc-4.7 FC=gfortran （通常情況下，單獨make就會進行自動探測，夠用了）
make PREFIX=/your/path install  (可選) 

其中，make過程會自動的探測當前機器和編譯環境，設定合適的選項。需注意的是，OpenBLAS會下載netlib上的LAPACK原始碼。也就是說你的機器必須聯網，或者放入lapack的原始碼包，或者不包括LAPACK即make NO_LAPACK=1。

注意：編譯過程可以直接用命令清除原來的編譯

make clean
make mrproper
make distclean