本步骤能实现用Intel核芯显卡来进行显示， 用NVIDIA GPU进行计算。
安装开发所需要的一些基本包
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在关闭桌面管理 lightdm 的情况下安装驱动似乎可以实现Intel 核芯显卡 来显示 + NVIDIA 显卡来计算。具体步骤如下：
-
首先在BIOS设置里选择用Intel显卡来显示或作为主要显示设备
-
进入Ubuntu， 按 ctrl+alt+F1 进入tty， 登录tty后输入如下命令
sudo service lightdm stop
该命令会关闭lightdm。如果你使用 gdm或者其他的desktop manager, 请在安装NVIDIA驱动前关闭他。
使用deb包安装CUDA及驱动能省去很多麻烦(参见CUDA Starting Guide)。下载对应于你系统的CUDA deb包(我的神舟笔记本为linux->x86_64->ubuntu->14.04->deb(network)), 然后用下列命令添加软件源
sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb
sudo apt-get update然后用下列命令安装CUDA
sudo apt-get install cuda安装完成后 reboot.
sudo reboot(2015-5-25:目前caffe只支持cudnn v4,所以下载的时候注意选择cudnn v4) cuDNN能加速caffe中conv及pooling的计算。首先下载cuDNN, 然后执行下列命令解压并安装
tar -zxvf cudnn-7.0-linux-x64-v4.0-prod.tgz
cd cuda
sudo cp lib/* /usr/local/cuda/lib64/
sudo cp cudnn.h /usr/local/cuda/include/更新软链接
cd /usr/local/cuda/lib64/
sudo rm -rf libcudnn.so libcudnn.so.4
sudo ln -s libcudnn.so.4.0.7 libcudnn.so.4
sudo ln -s libcudnn.so.4 libcudnn.so
安装完成后需要在/etc/profile中添加环境变量, 在文件最后添加:
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进入/usr/local/cuda/samples, 执行下列命令来build samples
sudo make all -j8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
如果没有Intel MKL， 可以用下列命令安装免费的atlas
sudo apt-get install libatlas-base-dev如果有mkl安装包，首先解压安装包，下面有一个install_GUI.sh文件， 执行该文件，会出现图形安装界面，根据说明一步一步执行即可。
注意： 安装完成后需要添加library路径, 创建/etc/ld.so.conf.d/intel_mkl.conf文件， 在文件中添加内容
/opt/intel/lib
/opt/intel/mkl/lib/intel64
注意把路径替换成自己的安装路径。 编辑完后执行
sudo ldconfig虽然我们已经安装了libopencv-dev , 但该库似乎会导致libtiff的相关问题, 所以我们需要从源代码build 自己的版本。这个尽量不要手动安装.
- 下载安装脚本
- 进入目录
Install-OpenCV/Ubuntu/2.4 - 执行脚本
sudo ./opencv2_4_10.sh
Github上有人已经写好了完整的安装脚本, 能自动安装所有dependencies. 下载该脚本，进入Ubuntu/2.4 目录, 给所有shell脚本加上可执行权限
chmod +x *.sh修改脚本opencv2_4_X.sh, 在cmake中加入参数
-D BUILD_TIFF=ON
然后安装（当前为2.4.9）
sudo ./opencv2_4_9.sh脚本会自动安装依赖项，下载安装包，编译并安装OpenCV。整个过程大概半小时左右。
注意，安装2.4.9时中途可能会报错
opencv-2.4.9/modules/gpu/src/nvidia/core/NCVPixelOperations.hpp(51): error: a storage class is not allowed in an explicit specialization
解决方法在此 下载 NCVPixelOperations.hpp， 替换掉opencv2.4.9内的文件， *并注释掉opencv2_4_9.sh中下载opencv包的代码, 重新执行sudo ./opencv2_4_9.sh`.
在此下载最新的安装包, 用默认设置安装在用户目录下。
打开新的终端(重要!), 用which python和which pip确定使用的是anaconda提供的python环境，然后进入caffe_root/python, 执行下列命令
for req in $(cat requirements.txt); do pip install $req; done
加入在编译或者运行caffe时遇到这样的错误
/usr/lib/x86_64-linux-gnu/libx264.so.142:undefined reference to '
那么请删除掉anaconda/lib中的libm.*. 参考this issue
实际编译caffe的时候还碰到anaconda和系统的libreadline冲突的状况，需要conda remove readline （感谢@jastarex ）.
这里需要注意，在运行Caffe时，可能会报一些找不到libxxx.so的错误，而用 locate libxxx.so命令发现已经安装在anaconda中，这时首先想到的是在/etc/ld.so.conf.d/ 下面将 your_anaconda_path/lib 加入 LD_LIBRARY_PATH中。 但是*这样做可能导致登出后无法再进入桌面！！！*原因（猜测）可能是anaconda的lib中有些内容于系统自带的lib产生冲突。
正确的做法是：为了不让系统在启动时就将anaconda/lib加入系统库目录，可以在用户自己的~/.bashrc 中添加library path， 比如我就在最后添加了两行
# add library path
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:your_anaconda_path/lib"
开启另一个终端后即生效，并且重启后能够顺利加载lightdm, 进入桌面环境。
Caffe提供了MATLAB接口， 有需要用MATLAB的同学可以额外安装MATLAB。 安装教程请自行搜索。
安装完成后添加图标
sudo vi /usr/share/applications/Matlab.desktop输入以下内容
[Desktop Entry]
Type=Application
Name=Matlab
GenericName=Matlab R2013b
Comment=Matlab:The Language of Technical Computing
Exec=sh /usr/local/MATLAB/R2013b/bin/matlab -desktop
Icon=/usr/local/MATLAB/Matlab.png
Terminal=false
Categories=Development;Matlab;
(I use the R2013b patched package. First you should uncompress the .iso file. Then use sudo cp to copy the patch file)
终于完成了所有环境的配置，可以愉快的编译Caffe了！ 进入caffe根目录， 首先复制一份Makefile.config, 然后修改里面的内容，主要需要修改的参数包括
- CPU_ONLY 是否只使用CPU模式，没有GPU没安装CUDA的同学可以打开这个选项
- BLAS (使用intel mkl还是atlas)
- MATLAB_DIR 如果需要使用MATLAB wrapper的同学需要指定matlab的安装路径, 如我的路径为
/usr/local/MATLAB/R2013b(注意该目录下需要包含bin文件夹，bin文件夹里应该包含mex二进制程序) - DEBUG 是否使用debug模式，打开此选项则可以在eclipse或者NSight中debug程序
## 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_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 \
# 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
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 ?= @
完成设置后， 开始编译
sudo make all -j4
sudo make test
sudo make runtest注意 -j4 是指使用几个线程来同时编译， 可以加快速度， j后面的数字可以根据CPU core的个数来决定， 我的CPU使4核， 所以-j4.
直接使用上面的make可能会出现如下错误:
.build_release/lib/libcaffe.so：对‘cv::imencode(cv::String const&, cv::_InputArray const&, std::vector<unsigned char, std::allocator<unsigned char> >&, std::vector<int, std::allocator<int> > const&)’未定义的引用
.build_release/lib/libcaffe.so：对‘cv::imdecode(cv::_InputArray const&, int)’未定义的引用
.build_release/lib/libcaffe.so：对‘cv::imread(cv::String const&, int)’未定义的引用所以最好是使用下面的cmake来进行编译http://caffe.berkeleyvision.org/installation.html#compilation：
mkdir build
cd build
cmake ..
make all
make install
make runtest执行如下命令
sudo make matcaffe
然后就可以跑官方的matlab demo啦。
sudo make pycaffe
然后基本就全部安装完拉.
接下来大家尽情地跑demo吧～