Caffe (Ubuntu 14.04 + GTX 1080)（转）

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转载最新推荐文章于 2026-03-12 17:00:08 发布 661 阅读 AI 写同款· GEO 优化 ›

ubuntu14.04 caffe环境的配置本人学习深度学习所配置的环境，包括英伟达驱动的安装，如果安装之后进不了桌面，处于循环登录状态，请按照里面文档说明操作。还有cuda的配置，caffe的配置。因为文件大小限制，英伟达与cuda8.0请自行下载。立即下载

[原始]

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 2014aOptional

2. 系统安装

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。
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
磁盘分区可以默认全划给 /dev/sda。
注册用户名，主机名，密码。
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

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++

其他依赖

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

常用软件

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

安装NVIDIA驱动

   参考 http://blog.youkuaiyun.com/YuhangWUST/article/details/72782254
参考 https://jingyan.baidu.com/article/d7130635c5a86113fdf47532.html

3.1 拷贝程序

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

拷贝软件

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

卸载U盘

   sudo umount /mnt/usb      # 卸载U盘

3.2 Cuda 8.0

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 
```

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: n `enter`
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

重启图形界面。

   sudo service lightdm start # 启动桌面服务

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"> /etc/ld.so.conf.d/cuda.conf && ldconfig -v | grep cuda
exit

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)

MKL: Intel® Parallel Studio XE Cluster Edition for LinuxSelect

   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

Atlas: Select

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

3.5 OpenCV

Version ≥ 3.1.0 兼容cuda8.0

解压源码

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

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。

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]

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

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

破解

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

卸载镜像
```
   cd .. 
sudo umount matlab
```

4. 编译Caffe

4.1 配置编译选项

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

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 make matcaffe -j $(nproc)
sudo make 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)

[1] Ubuntu 14.04+cuda8.0+opencv3.1+caffe↩
[2] OpenCV 3.1下载 ippicv_linux_20151201失败↩
[3] 在Ubuntu14.04下安装matlab2014a↩
[4] https://github.com/BVLC/caffe/pull/1667↩

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#caffe #Ubuntu

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