# Ubuntu 14.04 64bit + Caffe + CUDA 7.5 + Intel MKL 配置说明

本步骤经笔者亲身实践，集百家所长，能实现Caffe在NVIDIA GPU下进行计算。

## 1. 安装开发所需的依赖包
安装开发所需要的一些基本包
```sh
sudo apt-get install build-essential  # basic requirement
sudo apt-get install vim cmake git    # tools
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 libatlas-base-dev  #required by caffe
```

## 2. 安装CUDA及驱动
### 2.1 准备工作
下文中所有资源可在**百度云** 链接：http://pan.baidu.com/s/1dEXPg3J 密码：v19o 得到。

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：

1. 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
2. 进入Ubuntu， 按 ctrl+alt+F1 进入tty， 登录tty后输入如下命令
  
   ```sh
   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。

### 2.2 下载deb包及安装CUDA
使用deb包安装CUDA及驱动能省去很多麻烦(参见[CUDA Starting Guide](http://developer.download.nvidia.com/compute/cuda/6_5/rel/docs/CUDA_Getting_Started_Linux.pdf))。下载对应于你系统的[CUDA deb包](https://developer.nvidia.com/cuda-downloads), 然后用下列命令添加软件源
```sh
 sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb
 sudo apt-get update
```
然后用下列命令安装CUDA
```sh
 sudo apt-get install cuda
```
安装完成后 reboot.
```sh
sudo reboot
```

### 2.3 安装cuDNN
**(03-25: 今天下最新的caffe回来发现编译不过啊一直CUDNN报错浪费了我几个小时没搞定! 后来才发现caffe15小时前的更新开始使用cudnn v2, 但是官网上并没有明显提示!!! 坑爹啊!)**
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
```sh
tar -zxvf cudnn-7.5-linux-x64-v5.1-rc.tgz
cd cuda
sudo cp lib64/* /usr/local/cuda/lib64/
sudo cp include/cudnn.h /usr/local/cuda/include/
```

更新软链接
```sh
cd /usr/local/cuda/lib64/
sudo rm -rf libcudnn.so libcudnn.so.5
sudo ln -s libcudnn.so.5.1.3 libcudnn.so
```

### 2.4 设置环境变量
安装完成后需要在`/etc/profile`中添加环境变量, 
```sh
sudo gedit /etc/profile
```
在文件最后添加:
```sh
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
```sh
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
```sh
sudo ldconfig
```

## 3. 测试CUDA 
进入`/usr/local/cuda/samples`, 执行下列命令来build samples
```sh
sudo make all -j4
```
编译过程

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
```sh
./deviceQuery
```
如果出现显卡信息， 则驱动及显卡安装成功：

```
pi@DeepMind:/usr/local/cuda/samples/bin/x86_64/linux/release$ ./deviceQuery
./deviceQuery Starting...

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

Detected 1 CUDA Capable device(s)

Device 0: "Quadro K2200"
  CUDA Driver Version / Runtime Version          7.5 / 7.5
  CUDA Capability Major/Minor version number:    5.0
  Total amount of global memory:                 4095 MBytes (4294246400 bytes)
  ( 5) Multiprocessors, (128) CUDA Cores/MP:     640 CUDA Cores
  GPU Max Clock rate:                            1124 MHz (1.12 GHz)
  Memory Clock rate:                             2505 Mhz
  Memory Bus Width:                              128-bit
  L2 Cache Size:                                 2097152 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 Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 7.5, CUDA Runtime Version = 7.5, NumDevs = 1, Device0 = Quadro K2200
Result = PASS

```

## 4. 安装Intel MKL 或Atlas
如果没有Intel MKL， 可以用下列命令安装免费的atlas
```sh
sudo apt-get install libatlas-base-dev
```

如果有mkl安装包[Intel® MKL](https://software.intel.com/en-us/intel-mkl)需注册后会发下载链接和激活码到注册邮箱，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。
```sh
tar -zxvf l_mkl_11.3.3.210.tgz
cd l_mkl_11.3.3.210
sudo sh install.sh
```

**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/intel64
/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
```sh
sudo ldconfig
```

## 5.0 安装OpenCV (Optional， 如果运行caffe时opencv报错， 可以重新按照此步骤安装)
参见我的另一篇博客[Ubuntu 14.04安装 OpenCV 2.4.9](http://blog.mindcont.com/2016/07/16/installing-opencv-2-4-9-in-ubuntu-14-04-lts/)

## 6.0 安装Caffe所需要的Python环境
```sh
sudo apt-get install python-numpy python-scipy python-matplotlib python-sklearn python-skimage python-h5py python-protobuf python-leveldb python-networkx python-nose python-pandas python-gflags Cython ipython
sudo apt-get install protobuf-c-compiler protobuf-compiler
```

## 7.0 安装MATLAB
Caffe提供了MATLAB接口，有需要用MATLAB的同学可以额外安装MATLAB。安装教程同Windows 下类似,首先下载 MATLAB for Linux、解压。 
```sh
sudo sh ./install.sh
```
弹出图形界面，之后同在Windows下一样进行破解激活。windows下 Matlab的安装和激活参见我的另一篇博客[Matlab连接Kinect V2](http://blog.mindcont.com/2016/05/18/Microsoft-Kinect-V2-with-Matlab/)  。安装完成后可[添加图标]( http://www.linuxidc.com/Linux/2011-01/31632.htm)

```sh
sudo vi /usr/share/applications/Matlab.desktop
```

输入以下内容

```
[Desktop Entry]
Type=Application
Name=Matlab
GenericName=Matlab R2015b
Comment=Matlab:The Language of Technical Computing
Exec=sh /usr/local/MATLAB/R2015b/bin/matlab -desktop
Icon=/usr/local/MATLAB/Matlab.png
Terminal=false
Categories=Development;Matlab;
```

##  8. 编译Caffe
###  8.1 编译主程序
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`,
```sh
cp Makefile.config.example 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/R2015b` (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
* CUDA_ARCH 可根据你自己显卡对应的计算力[](https://developer.nvidia.com/cuda-gpus)改相应的 -gencode arch=compute_xx,code=compute_xx 。例如我的显卡是 NVIDIA K2200 对应的计算力是 5.0，所以我相应的设置为 -gencode arch=compute_50,code=compute_50 

这里是我的配置：

```
## 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 := 1
 USE_LEVELDB := 1
 USE_LMDB := 1

# 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_50,code=sm_50 \
		-gencode arch=compute_50,code=compute_50 

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
 BLAS := mkl
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
 BLAS_INCLUDE := /opt/intel/mkl/include
 BLAS_LIB := /opt/intel/mkl/lib/intel64 \
	     /opt/intel/lib/intel64

# 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/R2015b
# 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

# 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 ?= @
```

完成设置后， 开始编译
```sh
make all -j4
make test
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.

### 8.2 编译Matlab wrapper
执行如下命令
```sh
make matcaffe
```
然后就可以跑官方的matlab demo啦。

### 8.3 编译Python wrapper
```sh
 make pycaffe 
```
**注意：** 这里生成caffe 的 python 还不能够直接使用，建议输入下面的指令，将其加入到当前用户的用户变量中。
```
cd ~
gedit .bashrc
```
在打开的文件中，输入
```
export PYTHONPATH=/home/pi/caffe/python:$PYTHONPATH
```
保存后关闭，然后在命令行下输入
```
source .bashrc
```
打开一个新的终端或同时按住（Ctrl + Alt + T）,输入
```
python
import caffe
```
如果看到如下内容
```
pi@DeepMind:~$ python
Python 2.7.6 (default, Jun 22 2015, 17:58:13)
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import caffe
>>>

```
然后基本就全部安装完拉.接下来大家尽情地跑demo吧～

## 9. 参考链接

* [Ubuntu Installation](http://caffe.berkeleyvision.org/install_apt.html)
* [Ubuntu 16.04 or 15.10 Installation Guide](https://github.com/BVLC/caffe/wiki/Ubuntu-16.04-or-15.10-Installation-Guide)
* [Caffe + Ubuntu 12.04 64bit + CUDA 6.5 配置说明](https://gist.github.com/bearpaw/c38ef18ec45ba6548ec0)
* [普兒的技术传送门](http://www.cnblogs.com/platero/p/3993877.html)
* [浙商大嵌入式实验室-凉水煮茶](http://blog.csdn.net/u013476464/article/details/38071075)
* [记一个编译问题的解决过程](http://zzfei.com/archives/process-solving-a-compilation-problem.html)
* [caffe的配置过程](http://blog.csdn.net/brightming/article/details/51106629)
* [error == cudaSuccess (8 vs. 0) ](https://github.com/rbgirshick/py-faster-rcnn/issues/2)

**转载务必注明** 来自[微记元-Ubuntu 14.04 64bit + Caffe + CUDA 7.5 + Intel MKL 配置说明](http://blog.mindcont.com/2016/07/20/ubuntu1404-caffe-cuda7-5-mkl/)