Ubuntu 14 Caffe配置教程(cuda7.5+cuDnn v5)

关于Ubuntu 版本的选择，强烈建议用14.04这个比较稳定的版本，但是千万不要用麒麟版！！！

主要参考以下博客，稍作修改解决opencv安装可能出现的错误。

2015.08.17 Ubuntu 14.04+cuda 7.5+caffe安装配置

Caffe在Ubuntu 14.04 64bit 下的安装

配置：Caffe + Ubuntu 14.04 64bit + CUDA7.5+cuDnn v5 + Anaconda2

1.系统更新

首先安装好Ubuntu 14.04 64bit。如果刚刚安装好系统，建议在开启网络连接的情况下等待一段时间，Ubuntu会自动检测到更新，点击确定更新系统软件，然后重启。（注意：不是更新Ubuntu16）

2.安装开发所需的依赖包

1. sudo apt-get install build-essential

2. sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler

3.准备好所有的安装文件

Caffe：

输入终端下载：

1. git clone https://github.com/BVLC/caffe.git

这样，/home 下就有Caffe文件夹了

CUDA 7.5：

https://developer.nvidia.com/cuda-toolkit-archive 选择CUDA Toolkit 7.5 (Sept 2015)

一定要选择离线 .deb文件版本

cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64.deb

下载放到Documents 或者桌面等文件夹即可；

cuDnn v5 ：

https://developer.nvidia.com/cudnn

这里面先要注册账号才能下载，型号选择

cudnn-7.5-linux-x64-v5.0-ga.tgz

下载放到Documents 或者桌面等文件夹即可；

Anaconda2：

https://www.continuum.io/downloads 选择Linux，下载Python 2.7 version 64bit

Anaconda2-4.2.0-Linux-x86_64.sh

下载放到Documents 或者桌面等文件夹即可；

Opencv自动安装脚本：

https://github.com/bearpaw/Install-OpenCV 右上角下载

Install-OpenCV-master.zip

下载放到Documents 或者桌面等文件夹即可；

上述文件，除了Caffe，其他下载统一到一个文件夹，我这里是Documents。

4.安装CUDA 7.5

Before install CUDA 7.5, you need update gcc 4.8+ to gcc 4.9+

安装之前gcc，g++升级到4.9.

1. sudo add-apt-repository ppa:ubuntu-toolchain-r/test

2. sudo apt-get update

3. sudo apt-get install gcc-4.9

4. sudo apt-get install g++-4.9

5. sudo su

6. cd ../../usr/bin

7. ln -s /usr/bin/g++-4.9 /usr/bin/g++ -f

8. ln -s /usr/bin/gcc-4.9 /usr/bin/gcc -f

输入gcc --version检查是不是4.9版

在安装cuda的时候，Nvidia 显卡驱动会同时装好，所以不需要单独装显卡驱动

开始安装前，按 ctrl+alt+F1 ，进入黑色的终端界面。

输入自己的账号，再输入密码，登录到你的账号。

然后输入以下命令

9. sudo service lightdm stop

然后cd 到文件下载的目录，我这里是Documents

10. sudo dpkg -i cuda-repo-ubuntu1404-7-0-local_7.0-28_amd64.deb

11. sudo apt-get update

12. sudo apt-get install cuda

等待安装结束，重启

13. sudo reboot

5.安装cuDNN

cd 到文件下载的目录，我这里是Documents

1. tar -zxvf cudnn-7.5-linux-x64-v5.0-ga.tgz

2. cd cuda

3. sudo cp lib64/lib* /usr/local/cuda/lib64/

4. sudo cp include/cudnn.h /usr/local/cuda/include/

更新软连接

5. cd /usr/local/cuda/lib64/

6. sudo chmod +r libcudnn.so.5.0.5

7. sudo ln -sf libcudnn.so.5.0.5 libcudnn.so.5

8. sudo ln -sf libcudnn.so.5 libcudnn.so

9. sudo ldconfig

6. 设置环境变量

1. sudo gedit /etc/profile

在打开的文件尾部加上

PATH=/usr/local/cuda/bin:$PATH

export PATH

保存关闭后执行以下命令使之生效

2. source /etc/profile

同时创建以下文件

3. sudo gedit /etc/ld.so.conf.d/cuda.conf

内容是

/usr/local/cuda/lib64

保存关闭后，使之生效

4. sudo ldconfig

7.安装CUDA SAMPLE

cd进入/usr/local/cuda/samples, 执行下列命令来build samples

1. sudo make all -j4

整个过程大概10分钟左右, 全部编译完成后，进入 samples/bin/x86_64/linux/release, 运行deviceQuery

2. ./deviceQuery

如果出现显卡信息，则驱动及显卡安装成功：

CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "Tesla K40c"

CUDA Driver Version / Runtime Version 8.0 / 7.5

CUDA Capability Major/Minor version number: 3.5

Total amount of global memory: 11439 MBytes (11995054080 bytes)

(15) Multiprocessors, (192) CUDA Cores/MP: 2880 CUDA Cores

GPU Max Clock rate: 745 MHz (0.75 GHz)

Memory Clock rate: 3004 Mhz

Memory Bus Width: 384-bit

L2 Cache Size: 1572864 bytes

Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)

Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers

Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers

Total amount of constant memory: 65536 bytes

Total amount of shared memory per block: 49152 bytes

Total number of registers available per block: 65536

Warp size: 32

Maximum number of threads per multiprocessor: 2048

Maximum number of threads per block: 1024

Max dimension size of a thread block (x,y,z): (1024, 1024, 64)

Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)

Maximum memory pitch: 2147483647 bytes

Texture alignment: 512 bytes

Concurrent copy and kernel execution: Yes with 2 copy engine(s)

Run time limit on kernels: No

Integrated GPU sharing Host Memory: No

Support host page-locked memory mapping: Yes

Alignment requirement for Surfaces: Yes

Device has ECC support: Enabled

Device supports Unified Addressing (UVA): Yes

Device PCI Domain ID / Bus ID / location ID: 0 / 4 / 0

Compute Mode:

< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 7.5, NumDevs = 2, Device0 = Tesla K40c,

Result = PASS

NOTE:上边的显卡信息是从别的地方拷过来的，我的GTX650显卡不是这些信息，如果没有这些信息，那肯定是安装不成功，找原因吧！

8.安装Atlas

Atlas免费，安装起来简单，安装命令：

1. sudo apt-get install libatlas-base-dev

9.安装OpenCV

在这之前，必须对gcc降级，降到原来的4.8

1. sudo su

2. cd ../../usr/bin

3. ln -s /usr/bin/g++-4.8 /usr/bin/g++ -f

4. ln -s /usr/bin/gcc-4.8 /usr/bin/gcc -f

输入gcc --version检查是不是4.8版

我安装的是2.4.10，比较稳定，不要装 3.0等，麻烦。从前面下载的解压文件，进入目录 Install-OpenCV/Ubuntu/2.4

执行脚本

5. sudo ./opencv2_4_10.sh

10.安装Caffe所需要的Python环境

前面下载了Anaconda2，切换到文件所在目录，执行

1. bash Anaconda2-4.2.0-Linux-x86_64.sh

过程中，问起yes/no，全部输入yes。问起安装路径，则直接回车默认安装

然后，添加Anaconda Library Path

在/etc/ld.so.conf最后加入以下路径，并没有出现重启不能进入界面的问题(NOTE:下边的username要替换)

2. sudo gedit /etc/ld.so.conf

在文件最后一行输入：

/home/username/anaconda2/lib 然后保存关闭

3. sudo gedit ~/.bashrc

在文件最后一行输入：export LD_LIBRARY_PATH="/home/username/anaconda2/lib:$LD_LIBRARY_PATH"

然后关闭终端，再打开终端

11.安装python依赖库

cd进入caffe下的python目录

执行如下命令

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

12.编译Caffe

进入caffe目录，复制一份Makefile.config.examples

1. cp Makefile.config.example Makefile.config

双击打开Makefile.config，修改其中的一些路径，如果前边和我说的一致，都选默认路径的话，那么配置文件应该张这个样子（更改黄色部分）：

## 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

# 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_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)/anaconda2

PYTHON_INCLUDE := $(ANACONDA_HOME)/include \

$(ANACONDA_HOME)/include/python2.7 \

$(ANACONDA_HOME)/lib/python2.7/site-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

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 ?= @

保存退出

编译(下面命令一个一个执行，过程中50%概率会出错）

2. make all -j4

3. make test

4. make runtest

5. make pycaffe

过程很长，如果都没报错，那就安装好了

记得关闭系统自动更新，否则可能会有想不到的错误

秒客网

Ubuntu 14 Caffe配置教程(cuda7.5+cuDnn v5)

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