2015.08.17 Ubuntu 14.04+cuda 7.0+caffe安装配置

2015.10.23更新：修改了一些地方，身边很多人按这个流程安装，完全可以安装

折腾了两个星期的caffe，windows和ubuntu下都安装成功了。其中windows的安装配置参考官网推荐的那个blog，后来发现那个版本的caffe太老，和现在的不兼容，一些关键字都不一样，果断回到Linux下。这里记录一下我的安装配置流程。

电脑配置：

ubuntu 14.04 64bit

8G 内存

GTX650显卡

软件版本：

CUDA 7.0

caffe 当天从github下载的版本

安装ubuntu的过程省略，建议安装后关闭自动更新，上一次安装caffe后用的很好，结果有一天晚上没关电脑，自己半夜更新了显卡驱动，然后...

caffe的安装流程主要参考这个blog，稍有改动：Caffe + Ubuntu 14.04 64bit + CUDA 6.5 配置说明

Caffe 安装配置步骤：

1， 安装开发所需的依赖包

[plain] view
plaincopyprint?

sudo apt-get install build-essential # basic requirement

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 #required by caffe

2，安装CUDA 7.0

验证过程省略，按照官方文档自己操作吧（遇到问题首先要看官方文档啊，血泪教训）

安装CUDA有两种方法，

离线.run安装：从官网下载对应版本的.run安装包安装，安装过程挺复杂，尝试过几次没成功，遂放弃。

在离线.deb安装：deb安装分离线和在线，我都尝试过都安装成功了，官网下载地址

安装之前请先进行md5校验，确保下载的安装包完整

切换到下载的deb所在目录，执行下边的命令

[plain] view
plaincopyprint?

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

NOTE:装不成功卸了多来几遍，总会成的

3，安装cuDNN

下载cudnn-6.5-linux-x64-v2.tgz，官网申请不到，网上自己找的，就不给地址了。

[plain] view
plaincopyprint?

tar -zxvf cudnn-6.5-linux-x64-v2.tgz

cd cudnn-6.5-linux-x64-v2

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

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

更新软连接

[plain] view
plaincopyprint?

cd /usr/local/cuda/lib64/

sudo rm -rf libcudnn.so libcudnn.so.6.5

sudo ln -s libcudnn.so.6.5.48 libcudnn.so.6.5

sudo ln -s libcudnn.so.6.5 libcudnn.so

上边的操作为什么这么做，不知道，原理是什么，不知道。等我知道了再来补充

4，设置环境变量

在/etc/profile中添加CUDA环境变量

sudo gedit /etc/profile

[plain] view
plaincopyprint?

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

export PATH

保存后, 执行下列命令, 使环境变量立即生效

[plain] view
plaincopyprint?

source /etc/profile

同时需要添加lib库路径： 在 /etc/ld.so.conf.d/加入文件 cuda.conf, 内容如下

[plain] view
plaincopyprint?

/usr/local/cuda/lib64

保存后，执行下列命令使之立刻生效

[plain] view
plaincopyprint?

sudo ldconfig

5，安装CUDA SAMPLE

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

[plain] view
plaincopyprint?

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

[plain] view
plaincopyprint?

./deviceQuery

如果出现显卡信息， 则驱动及显卡安装成功：

[plain] view
plaincopyprint?

./deviceQuery Starting...



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



Detected 1 CUDA Capable device(s)



Device 0: "GeForce GTX 670"

CUDA Driver Version / Runtime Version 6.5 / 6.5

CUDA Capability Major/Minor version number: 3.0

Total amount of global memory: 4095 MBytes (4294246400 bytes)

( 7) Multiprocessors, (192) CUDA Cores/MP: 1344 CUDA Cores

GPU Clock rate: 1098 MHz (1.10 GHz)

Memory Clock rate: 3105 Mhz

Memory Bus Width: 256-bit

L2 Cache Size: 524288 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 1 copy engine(s)

Run time limit on kernels: Yes

Integrated GPU sharing Host Memory: No

Support host page-locked memory mapping: Yes

Alignment requirement for Surfaces: Yes

Device has ECC support: Disabled

Device supports Unified Addressing (UVA): Yes

Device PCI Bus ID / PCI location ID: 1 / 0

Compute Mode:

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



deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 6.5, CUDA Runtime Version = 6.5, NumDevs = 1, Device0 = GeForce GTX 670

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！

6，安装Intel MKL 或Atlas

我没有MKL，装的Atlas

安装命令：

[plain] view
plaincopyprint?

sudo apt-get install libatlas-base-dev

7，安装OpenCV

我安装的是2.4.10

1）下载安装脚本

2）进入目录 Install-OpenCV/Ubuntu/2.4

3）执行脚本

[plain] view
plaincopyprint?

sh sudo ./opencv2_4_10.sh

8，安装Caffe所需要的Python环境

按caffe官网的推荐使用Anaconda

去Anaconda官网下载安装包

切换到文件所在目录，执行

[plain] view
plaincopyprint?

bash Anaconda-2.3.0-Linux-x86_64.s<em>h</em>

NOTE:后边的文件名按自己下的版本号更改，整个安装过程请选择默认

8.1，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)



[plain] view
plaincopyprint?

/home/username/anaconda/lib

在~/.bashrc最后添加下边路径

[plain] view
plaincopyprint?

export LD_LIBRARY_PATH="/home/username/anaconda/lib:$LD_LIBRARY_PATH"

9，安装python依赖库

去caffe的github下载caffe源码包

进入caffe-master下的python目录

执行如下命令

[plain] view
plaincopyprint?

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

10，编译Caffe

终于来到这里了

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

[plain] view
plaincopyprint?

cp Makefile.config.example 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

[plain] view
plaincopyprint?

## 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)/anaconda

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

[plain] view
plaincopyprint?

make all -j4

make test

make runtest

11，编译Python wrapper

[plain] view
plaincopyprint?

make pycaffe

到这里就基本结束了，跑个自带的例子测试一下吧！