ubuntu16.04_caffe_GPU

1安装依赖包

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler

sudo apt-get install --no-install-recommends libboost-all-dev

sudo apt-get install libopenblas-dev liblapack-dev libatlas-base-dev

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

sudo apt-get install git cmake build-essential

安装完成后用以下指令检查是否安装成功

sudo apt-get install git cmake build-essential

2检查NVIDIA驱动是否安装好

在终端输入指令：

nvidia-smi

若出现以下界面则表示已驱动已安装成功，否则要先安装驱动

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.66                 Driver Version: 375.66                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 1070    Off  | 0000:01:00.0      On |                  N/A |
| N/A   53C    P8     9W /  N/A |    396MiB /  8105MiB |      3%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|    0      1007    G   /usr/lib/xorg/Xorg                             199MiB |
|    0      1780    G   compiz                                         157MiB |
|    0      2185    G   fcitx-qimpanel                                   6MiB |
|    0      2798    G   /usr/lib/firefox/firefox                         2MiB |
|    0     18712    G   ...el-token=81E0322A6D84C0D0322D4EB2906C9545    28MiB |
+-----------------------------------------------------------------------------+

3安装CUDA

先在官网上下载CUDA，NVIDIA提供两种安装方式：run安装，deb安装。依次介绍两种安装方式

(1)run安装

我下载的是cuda8.0的runfile（local）文件

执行指令:

sudo sh cuda_8.0.61_375.26_linux.run

之后会有一系列的安装提示,除了install NVIDIA accelerated Graphics Diver for Linux-x86_64..?就是问你要不要安装NVIDIA的驱动,由于之前我们已经安装过了,所以这里一定要填否,剩下其他的提示都填是即可

安装好之后,需要声明环境变量

sudo gedit ~/.bashrc

打开窗口之后,填写

export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

再配置环境变量和链接库

sudo gedit /etc/profile

打开窗口之后,填写

export PATH = /usr/local/cuda/bin:$PATH

最后在终端执行以下命令,使环境变量生效

sudo ldconfig

最后需要makeCUDA的samples来验证CUDA8.0是否安装成功

cd /usr/local/cuda-8.0/samples/1_Utilities/deviceQuery
make
sudo ./deviceQuery

若出现以下,则证明安装成功

CUDA Device Query (Driver API) statically linked version
Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 1070"
  CUDA Driver Version:                           8.0
  CUDA Capability Major/Minor version number:    6.1
  Total amount of global memory:                 8106 MBytes (8499494912 bytes)
  (16) Multiprocessors, (128) CUDA Cores/MP:     2048 CUDA Cores
  GPU Max Clock rate:                            1645 MHz (1.64 GHz)
  Memory Clock rate:                             4004 Mhz
  Memory Bus Width:                              256-bit
  L2 Cache Size:                                 2097152 bytes
  Max Texture Dimension Sizes                    1D=(131072) 2D=(131072, 65536) 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 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)
  Texture alignment:                             512 bytes
  Maximum memory pitch:                          2147483647 bytes
  Concurrent copy and kernel execution:          Yes with 2 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Concurrent kernel execution:                   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) >
Result = PASS

(2)deb安装

sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb

sudo apt-get update

sudo apt-get install cuda

安装完成之后,和run安装一样进行验证是否安装成功

总结:这两种方法相比较而言,第二种更简单方便,但笔者先用第二种方法安装时总是安装不成功,便舍弃方法2,最后用方法1安装成功.

4 cuDnn_v5.1下载

注意和CUDA8.0相配套使用的cuDNN版本是cuDnn_v5.1,在NVIDIA官网上可下载,但最近官网维护,貌似下不了,笔者是在网盘上找到下载的,地址为http://tensordata.cn/t/cudnn-v5-1-linux-x64/53

sudo cp cudnn.h /usr/local/cuda/include/    #复制头文件
sudo cp lib* /usr/local/cuda/lib64/    #复制动态链接库cd /usr/local/cuda/lib64/sudo rm -rf libcudnn.so libcudnn.so.5    #删除原有动态文件sudo ln -s libcudnn.so.5.0.5 libcudnn.so.5  #生成软衔接sudo ln -s libcudnn.so.5 libcudnn.so      #生成软链接

验证是否安装成功:

终端输入 nvcc -V

若出现一下,则证明安装成功

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Tue_Jan_10_13:22:03_CST_2017
Cuda compilation tools, release 8.0, V8.0.61

5opencv安装

参考我之前的博客安装即可

http://blog.csdn.net/yuanluolvge/article/details/77678348

6 caffe下载

git clone https://github.com/BVLC/caffe.git  //从github上git caffecd caffe sudo cp Makefile.config.example Makefile.config  sudo gedit Makefile.config 

这里给出我的Makefile.config文件参考:

## 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 := /usr/bin/g++-5

# 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 := open
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /home/jiaqun/local/OpenBLAS/include
# BLAS_LIB := /home/jiaqun/local/OpenBLAS/lib

# 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/R2017a
# 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/local/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/sean/local/anaconda2
 #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 /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial

# 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 := 1

# enable pretty build (comment to see full commands)
Q ?= @

接下来 配置.bashrc文件,贴出我的文件仅供参考(打开.bashrc文件再后边继续添加以下路径),因为每个人安装的路径不同

#cuda
export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
#cudnn
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64/:$LD_LIBRARY_PATH
export CPLUS_INCLUDE_PATH=/usr/local/cuda/include:$CPLUS_INCLUDE_PATH
#OpenBLAS
export LD_LIBRARY_PATH=/usr/lib:$LD_LIBRARY_PATH
export OPENBLAS_NUM_THREADS=20
#opencv
export LD_LIBRARY_PATH=/opt/ros/kinetic/lib:$LD_LIBRARY_PATH
export PATH=/opt/ros/kinetic/lib:$PATH

#caffe python
export PYTHONPATH=/home/caffe/python:$PYTHONPATH

export LC_ALL=C

开始进入caffe cd/home/caffe 编译

make -j8

make test -j8

make runtest -j8

继续编译pycaffe

make pycaffe

如果安装了matlab想要编译可以继续编译

make matcaffe