paper 145：caffe-深度学习框架的搭建

参考来源于：http://www.cnblogs.com/goodluckcwl/p/5686094.html  （部分内容做了修改）

Caffe是一个深度学习框架，本文讲阐述如何在linux下安装GPU加速的caffe。 系统配置是：

• OS:                Ubuntu14.04
• CPU:              i5-4690
• GPU：           GTX960
• RAM:             8G

安装方法参见caffe的官方文档：http://caffe.berkeleyvision.org/installation.html#compilation 依赖项：

• CUDA:推荐7.0以上的cuda和最新的显卡驱动。
• BLAS:ATLAS, MKL, or OpenBLAS。C++矩阵运算库。
• Boost >= 1.55。用到一些数学函数等。
• protobuf：是一种轻便、高效的结构化数据存储格式，可以用于结构化数据串行化，很适合做数据存储或 RPC 数据交换格式。
• glog&&gflags：谷歌的一个日志库；命令行参数解析库。方便调试使用。
• hdf5：
• lmdb,leveldb:数据库IO。准备数据时会用到。

---

可选依赖：

• OpenCV >= 2.4 including 3.0
• IO libraries: lmdb, leveldb (note: leveldb requires snappy)
• cuDNN for GPU acceleration (v5)

---

Pycaffe: Python 2.7 or Python 3.3+, numpy (>= 1.7), boost-provided boost.python

Matcaffe: MATLAB with the mex compiler

安装CUDA7.5

CUDA维基百科：https://zh.wikipedia.org/wiki/CUDA CUDA（Compute Unified Device Architecture，统一计算架构）是由NVIDIA所推出的一种集成技术，是该公司对于GPGPU的正式名称。通过这个技术，用户可利用NVIDIA的GeForce 8以后的GPU和较新的Quadro GPU进行计算。亦是首次可以利用GPU作为C-编译器的开发环境。

安装过程

1.下载Cuda

下载CUDA:https://developer.nvidia.com/cuda-downloads 选择下载deb包（或者runfile），下载完后用mu5sum检查一下文件是否完整。按照cuda官方文档安装cuda.

2.安装

先关闭桌面显示管理器lightdm，进入字符界面，在字符界面安装cuda。(这是因为cuda的安装包里包含了显卡驱动，安装驱动前要先关闭桌面显示管理器) (也可分别安装显卡驱动与cuda库)

sudo service stop

切换到deb包目录，执行下面的命令

sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb
sudo apt-get update
sudo apt-get install cuda  

然后重启电脑：sudo reboot 注意，cuda的安装包中已经包含了较新版本的显卡驱动。

3.配置环境变量

将cuda安装目录下的bin路径导出到系统的搜索路径path 并使之生效 添加动态库查找路径：在 /etc/ld.so.conf.d/加入文件 cuda.conf, 内容如下

/usr/local/cuda/lib64

保存后，执行下列命令使之立刻生效:

sudo ldconfig

4.验证

查看Cuda的C编译器NVCC的版本：

nvcc -V

编译并运行例子，进入cuda目录下的samples目录，然后在该目录下make，等待十来分钟。编译完成后，可以在Samples里面找到bin/x86_64/linux/release/目录，并切换到该目录 运行deviceQuery程序，查看输出结果如下（重点关注最后一行，Pass表示通过测试）。

5.gcc编译器版本

该版本cuda不支持gcc5.0的编译器

参考文献： [1]Ubuntu 16.04 安装 NVIDIA CUDA Toolkit 7.5 https://gist.github.com/dangbiao1991/2c895917ea888ce33af8c1c72444b7bf [2]Ubuntu 14.04+cuda 7.5+caffe安装配置 http://blog.csdn.net/ubunfans/article/details/47724341

安装Cudnn

下载cudnn https://developer.nvidia.com/rdp/cudnn-download, 解压，把lib目录,include目录分别复制到cuda的安装目录下。

安装BLAS

install ATLAS by sudo apt-get install libatlas-base-dev or install OpenBLAS or MKL for better CPU performance.

下载Caffe

安装Caffe依赖库

通用依赖库：

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

Ubuntu14.04 依赖库：

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

PyCaffe依赖库

进入caffe/python目录，安装依赖项：

for req in $(cat requirements.txt); do pip install $req; done

caffe官网推荐使用Anaconda http://continuum.io/downloads#all Anaconda是一个和Canopy类似的科学计算环境，但用起来更加方便。自带的包管理器conda也很强大。

MatCaffe

安装matlabR2014a

编译caffe

复制并修改Makefile.config文件：

## 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
# 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

# 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/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/local/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)
# 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
# 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

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

进入caffe目录，执行：

make all
make test
make runtest

无错误，编译完成。

编译pycaffe与matcaffe

进入caffe目录，执行

make pycaffe
make matcaffe

Caffe python接口

复制caffe/python/caffe 到/usr/local/lib/python2.7/dist-packages/目录下。 复制caffe/build/lib/下的库文件到/usr/local/lib

$ sudo ldconfig

打开python，import caffe，无错误。

Caffe C++接口

分别将include,lib目录复制。

测试

测试mnist http://caffe.berkeleyvision.org/gathered/examples/mnist.html

准备数据

cd $CAFFE_ROOT
./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh

LeNet: the MNIST Classification Model

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