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

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

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。

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

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

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

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

/usr/local/cuda/lib64

保存后，执行下列命令使之立刻生效

sudo ldconfig

5，安装CUDA SAMPLE

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

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

./deviceQuery

如果出现显卡信息， 则驱动及显卡安装成功：

./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：

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

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

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)

 

/home/username/anaconda/lib

在~/.bashrc最后添加下边路径

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

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

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

## 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

make all -j4
make test
make runtest

11，编译Python wrapper

make  pycaffe

到这里就基本结束了，跑个自带的例子测试一下吧！

NOTE:以上是我在自己PC上的安装步骤，因软件版本不同，硬件环境不同，按照以上方式可能出现错误，请耐心查找错误，欢迎留言