Caffe (Ubuntu 14.04 + GTX 1080)

Caffe

time (2017-2-18)

0. 硬件检查 Optional

略

1. 软件列表

• Ubuntu 14.04 amd64 iso
  › Alternative downloads › Past releases › 14.04.5 › 选择 ubuntu-14.04.5-desktop-amd64.iso
• Cuda 8.0 run
  › CUDA Toolkit Downloads › Linux+x86_64+Ubuntu+14.04+runfile › Download(1.4GB)
• Cudnn 5.1 tgz
  › Download › Agree › Download cuDNN v5.1 for CUDA 8.0 › cdDNN v5.1 Library for Linux
• MKL: Intel® Parallel Studio XE Cluster Edition for Linux:
  › Linux* › 全部勾选 Accept › 注册（教育Email+Country+Submit） › Register an Account › parallel_stuio_xe_2017_update2.tgz
• Matlab 2014a Optional

2. 系统安装

1. UltralSO 制作U盘启动盘 on Windows。
   › 启动 › 写入硬盘映像 › 选择U盘 › 写入
   
   • 提醒，写入过程会 格式化 U盘，请注意 备份 数据。
2. 开机，F2 或 DEL 进入 BIOS， 启动顺序选 U盘。退出BIOS，进入Ubuntu安装界面，选择 安装Ubuntu，默认安装。
   
   • 提醒， 选择 安装类型 时，警惕 清除整个磁盘安装 选项。
   • 推荐选择 LVM
3. 磁盘分区可以默认全划给 /dev/sda。
4. 注册 用户名，主机名，密码。
5. 重启 电脑，恢复 BIOS 中的启动顺序为 硬盘 第一。

3. 安装依赖 [1]

3.0 热身准备

1. 通用依赖

   sudo apt-get update
   sudo apt-get install -y \
       libprotobuf-dev protobuf-compiler \
       libleveldb-dev libhdf5-serial-dev libsnappy-dev \
       libopencv-dev   # opencv
   sudo apt-get install -y --no-install-recommends libboost-all-dev   # boost-c++

2. 其他依赖

   sudo apt-get install -y \
       libgflags-dev libgoogle-glog-dev \
       liblmdb-dev

3. 常用软件

   sudo apt-get install -y \
       vim cmake openssh-server lrzsz git gparted

3.1 拷贝程序

1. 挂载U盘

   sudo mkdir -p /mnt/usb    # 创建挂载目录
   sudo fdisk -l             # 查看U盘盘符
   sudo mount -t auto [/dev/sdb5] /mnt/usb    # 挂载
   ll /mnt/usb               # 测试挂载成功

2. 拷贝软件

   sudo cp -r /mnt/usb/softwares ~/
   cd ~/softwares
   sudo chown -R 755 .       # 修改文件权限

3. 卸载U盘

   sudo umount /mnt/usb      # 卸载U盘

3.2 Cuda 8.0

1. 关闭图形界面，防止 驱动更新 造成开机死循环。
   Ctrl+Alt+F1 进入字符界面。

   sudo service lightdm stop # 关闭桌面服务 

2. 安装 cuda 8.0。

   sudo ./cuda_8.0.61_375.26_linux.run # 启动cuda安装程序
   ## 安装引导
   1. Do you accept the previously read EULA?
   accept/decline/quit: accept `enter`
   2. Install NVIDIA Accelerated Graphics Driver for Linux-x86_86xxx?
   (y)es/(n)o/(q)uit: y `enter`   # version ≥ 370
   3. Do you want to install a symbolic link at /uer/local/cuda?
   (y)es/(n)o/(q)uit: y `enter`
   4. Install the CUDA 8.0 Samples?
   * (y)es/(n)o/(q)uit: y `enter`   # develop
     Enter CUDA Samples Location
     [ default is /home/usr ]: `enter`
   * (y)es/(n)o/(q)uit: n `enter`   # deploy

3. 重启图形界面。

   sudo service lightdm start # 启动桌面服务 

4. 配置环境变量

   sudo su
   echo "export PATH=/usr/local/cuda-8.0/bin:$PATH" >> /etc/profile && source /etc/profile
   echo "/usr/local/cuda/lib64
   /lib" > /etc/ld.so.conf.d/cuda.conf && ldconfig -v | grep cuda
   exit

5. 验证是否安装成功 if installed Samples

   nvcc -V       # 版本信息release 8.0
   nvidia-smi    # 显卡管理器
   ## if installed Cuda Samples
   cd ~/NVIDIA_CUDA-8.0_Samples/1_Utilities/deviceQuery
   make
   ./deviceQuery     # 输出结尾为`pass`

3.3 Cudnn 5.1

cd ~/softwares
sudo mv cudnn-8.0-linux-x64-v5.1.solitairetheme8 cudnn-8.0-linux-x64-v5.1.tgz
sudo tar -zxvf cudnn-8.0-linux-x64-v5.1.tgz
sudo cp cuda/include/cudnn.h /usr/local/include
sudo cp cuda/lib64/libcudnn.* /usr/local/lib
sudo chmod a+r /usr/local/include/cudnn.h  
sudo chmod a+r /usr/local/lib/libcudnn.*

3.3.1 NCCL Optional

Version ≥ 1.2.3 (Linked with Cuda8.0)， 单机多卡支持。

unzip nccl-1.2.3-1-cuda8.0.zip && cd nccl-1.2.3-1-cuda8.0
make CUDA_HOME=/usr/local/cuda test -j $(nproc)
## tset
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./build/lib
./build/test/single/all_reduce_test 10000000
## install
sudo make PREFIX=/usr/local install

3.4 BLAS

基础线性代数子程序(Basic Linear Algebra Subprograms)

1. MKL: Intel® Parallel Studio XE Cluster Edition for Linux Select

   tar -zxvf parallel_studio_xe_2017_update2.tar.gz
   cd parallel_studio_xe_2017_update2
   sudo chmod a+x -R .
   sudo ./install.sh # or ./install_GUI.sh

   配置环境变量

   sudo su
   echo "/opt/intel/lib/intel64
   /opt/intel/mkl/lib/intel64" > /etc/ld.so.conf.d/intel_mkl.conf && ldconfig -v | grep intel
   exit

2. Atlas: Select

   sudo apt-get install -y libatlas-base-dev

3.5 OpenCV

Version ≥ 3.1.0 兼容cuda8.0

1. 解压源码

   unzip opencv-3.2.0.zip && cd opencv-3.2.0

2. ippicv_linux_20151201 三方依赖包下载超时[2]。

   ipp_file=../ippicv_linux_20151201.tgz
   ipp_hash=$(md5sum $ipp_file | cut -d" " -f1)
   ipp_dir=3rdparty/ippicv/downloads/linux-$ipp_hash
   mkdir -p $ipp_dir
   cp $ipp_file $ipp_dir

   • 更简单的方法，在 确保 MD5是808b791a6eac9ed78d32a7666804320e的情况下，在OpenCV源码根目录下创建目录：
     opencv-3.1.0/3rdparty/ippicv/downloads/linux-808b791a6eac9ed78d32a7666804320e
     将下载的ippicv_linux_20151201.tgz 拷贝进去。

3. 编译安装

   sudo chmod a+x .
   mkdir build && cd build
   cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..
   make -j $(nproc)  # 多线程编译
   sudo make install && sudo ldconfig       # 安装并更新环境变量

3.6 Matlab 2014a Optional [3]

1. 挂载iso镜像

   cd ~/softwares
   mkdir matlab   # 加载目录
   sudo mount -o loop MATHWORKS_R2014A.iso matlab  # 解压
   sudo chmod 755 -R matlab

2. 安装与激活

   cd matlab
   sudo ./install  # 选择不联网安装
   > 12345-67890-12345-67890   # 序列号
   > Crack/license_405329_R2014a.lic # 不联网安装激活文件

3. 破解

   sudo chmod -R a+w /usr/local/MATLAB
   sudo cp Crack/Linux/libmwservices.so /usr/local/MATLAB/R2014A/bin/glnxa64/

4. 卸载镜像

   cd .. 
   sudo umount matlab

4. 编译Caffe

4.1 配置编译选项

1. 下载Caffe release candidate 5

   wget -O caffe-rc5.zip https://codeload.github.com/BVLC/caffe/zip/rc5
   unzip caffe-rc5.zip && rm caffe-rc5.zip && cd caffe-rc5

2. 修改编译配置文件

   cp Makefile.config.example Makefile.config
   sed -i 's/# USE_CUDNN := 1/USE_CUDNN := 1/' Makefile.config  
   sed -i 's/# WITH_PYTHON_LAYER := 1/WITH_PYTHON_LAYER := 1/' Makefile.config  
   sed -i 's/# OPENCV_VERSION := 3/OPENCV_VERSION := 3/' Makefile.config  
   sed -i 's/BLAS := atlas/BLAS := mkl/' Makefile.config   # if installed mkl

4.2 编译与测试

make all -j $(nproc)
make test -j $(nproc)
make runtest -j $(nproc)

4.3 编译PyCaffe Optional

使用系统原生Python version 2.7.6

sudo apt-get install -y \
    python-pip python-dev \
    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
make pycaffe -j $(nproc)
make pytest -j $(nproc)

4.4 编译MatCaffe Optional

Matlab_2014a for Unix

sed -i 's/CXXFLAGS += -MMD -MP/CXXFLAGS += -MMD -MP -std=c++11/' Makefile
sudo matcaffe -j $(nproc)
sudo mattest -j $(nproc)

5 Cmake cuda [4]

5.1 Configure

vim CMakeLists.txt
- Change USE_NCCL to "ON"
vim cmake/Dependencies.cmake
- find "set(BLAS "Atlas" ...)"
- change "Atlas" to "MKL"

5.2 Cmake

mkdir cmake_build && cd cmake_build && cmake ..

5.3 Make

cmake . -DCMAKE_BUILD_TYPE=Debug     # 跳转到Debug模式下
make -j $(nproc) && make install
cmake . -DCMAKE_BUILD_TYPE=Release   # 跳转到Release模式下
make -j $(nproc) && make install
make symlink    # softlink cmake_build to build

• Debug 和 Release 下生成的可执行文件不会相互覆盖，Debug下都会有 -d 的后缀。

5.4 cmake shared-lib with caffe and JNI

Predict.java

public static native HashMap<String, Float> predict(List<CalculateInfo> images,
                                                  int reset,
                                                  String modelFile,
                                                  String trainedFile,
                                                  int batchSize,
                                                  int gpuId);

Signature head file

javac com/persist/.../Predict.java  # get Predict.class
javah com.persist...Predict     # get Predict.h

CMakeLists.txt

cmake_minimum_required(VERSION 2.8.8)
set(SrcFile classify.cpp)
# file(GLOB_RECURSE examples_srcs "${PROJECT_SOURCE_DIR}/porn-jni/*.cpp")
find_package(JNI)
if (JNI_FOUND)
    message (STATUS "JNI_INCLUDE_DIRS=${JNI_INCLUDE_DIRS}")
    message (STATUS "JNI_LIBRARIES=${JNI_LIBRARIES}")
endif()
find_package(OpenCV REQUIRED)
if (OpenCV_FOUND)
    message (STATUS "OpenCV_INCLUDE_DIRS=${OpenCV_INCLUDE_DIRS}")
    message (STATUS "OpenCV_LIBS=${OpenCV_LIBS}")
endif()
find_package(Caffe REQUIRED)
if (Caffe_FOUND)
    message (STATUS "Caffe_INCLUDE_DIRS=${Caffe_INCLUDE_DIRS}")
    message (STATUS "Caffe_DEFINITIONS=${Caffe_DEFINITIONS}")
    message (STATUS "Caffe_LIBRARIES=${Caffe_LIBRARIES}")
endif()
message (STATUS "Caffe_Link=${Caffe_Link}")
include_directories(${JNI_INCLUDE_DIRS} ${OpenCV_INCLUDE_DIRS} ${Caffe_INCLUDE_DIRS})
add_library(PornJni SHARED ${SrcFile})
target_link_libraries(PornJni ${Caffe_Link} ${JNI_LIBRARIES} ${OpenCV_LIBS} ${Caffe_LIBRARIES})

cmake

mkdir cmake_build && cd cmake_build
cmake .. && make -j $(nproc)