## 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

"CuDNN是NVIDIA专门针对Deep Learning框架设计的一套GPU计算加速库,用于实现高性能的并行计算,在有GPU并且安装CuDNN的情况下可以打开即将注释去掉。"

# CPU-only switch (uncomment to build without GPU support).

#CPU_ONLY := 1

"表示是否用GPU,如果只有CPU这里要打开"

# uncomment to disable IO dependencies and corresponding data layers

USE_OPENCV := 1

"因为要用到OpenCV库所以要打开,下面这两个选项表示是选择Caffe的数据管理第三方库,两者都不打开 Caffe默认用的是LMDB,这两者均是嵌入式数据库管理系统编程库。"

# 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

"当需要读取LMDB文件时可以取消注释,默认不打开。"

# Uncomment if you're using OpenCV 3

OPENCV_VERSION := 2.4.10

"用pkg-config --modversion opencv命令查看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系统默认使用g++编译器,OSX则是clang++。"

# CUDA directory contains bin/ and lib/ directories that we need.

CUDA_DIR := /usr/local/cuda

"CUDA的安装目录"

# 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

"这些参数需要根据GPU的计算能力来进行设置,6.0以下的版本不支持×_50的计算能力。"

# BLAS choice:

# atlas for ATLAS (default)

# mkl for MKL

# open for OpenBlas

BLAS := open

"如果用的是ATLAS计算库则赋值atlas,MKL计算库则用mkl赋值,OpenBlas则赋值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 := /usr/local/OpenBlas/include

BLAS_LIB := /usr/local/OpenBlas/lib

"blas库安装目录"

# 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

"如果不是安装在标准路径则要指明"

# 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安装库的目录"

# 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安装目录"

# 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)

# 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库位置</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编号"

# enable pretty build (comment to see full commands)

Q ?= @

最新文章

  1. RMAN还原遭遇ORA-32006&amp;ORA-27102错误
  2. PCIE学习
  3. spoj1811 Longest Common Substring
  4. Android 定位地理坐标体系
  5. SQL语句技巧:查询时巧用OR实现逻辑判断
  6. The All-purpose Zero---hdu5773(LIS变形)
  7. bootstrap学习笔记&lt;三&gt;(文本,代码域,列表)
  8. STL算法
  9. [JLOI2013]地形生成
  10. *MySQL卸载之后无法重装,卡在Apply security settings:Error Nr.1045
  11. ZOJ 2283 Challenge of Wisdom
  12. iOS Core Animation学习总结(1)--CALayer常用属性
  13. IOS面试题(虽然我们很少用)
  14. Counting Stars
  15. 深入理解JSP
  16. 手把手教你如何安装Pycharm
  17. ResourceOwnerPassword模式使用数据库.
  18. IdentityServer4【Topic】之保护APIs
  19. 使用 python -m SimpleHTTPServer 快速搭建http服务
  20. 面试:----Struts和springmvc的区别--区别上

热门文章

  1. 猜随机数(控制台输入,字符串转int)
  2. 数据结构与算法(c++)——双缓存队列
  3. Maven项目pom.xml 标签含义
  4. ASPNET 5 和 dnx commands
  5. 使用Python查询JMX
  6. Junit4X系列--hamcrest的使用
  7. 易趣:使用MongoDB创建关键业务的多数据中心应用
  8. 壮美大山包-2017中国大山包国际超百公里ITRA积分赛赛记
  9. LINUX下SYN攻防战 [转]
  10. [C#][Newtonsoft.Json] Newtonsoft.Json 序列化时的一些其它用法