caffe安裝流程和遇到問題

之前caffe的安裝簡直讓我懷疑人生，後來由於忙一直沒有寫下流程和重要問題的解決辦法，這次由於在自家的電腦上配置caffe，順便下寫流程。

不多說，先上自家電腦配置，i5-4590和GTX1070

1.caffe安裝依賴庫

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

首先最好一個一個安裝，其次這裏有可能出現需要依賴庫，以下依賴庫將不被安裝，解決方法是換軟件源，我換了清華的源後立馬就好了

2.安裝CUDA

CUDA是英偉達的顯卡並行計算語言，caffe需要來使用顯卡

在離線.deb安裝：deb安裝分離線和在線，官網下載地址，推薦離線安裝

切換到下載的deb所在目錄，執行下邊的命令

sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb
sudo apt-get update
sudo apt-get install cuda

然後重啓電腦：sudo reboot

3.cuDNN

這個是顯卡計算加速的，可以不裝，加速效果遠沒有GPU對CPU的加速來的多，而且安裝還麻煩

下載cudnn-7.5-linux-x64-v5.0-ga.tgz，官網申請不到，網上自己找的，就不給地址了。

tar -zxvf cudnn-7.5-linux-x64-v5.0-ga.tgz
cd cuda
sudo cp lib/lib* /usr/local/cuda/lib64/
sudo cp include/cudnn.h /usr/local/cuda/include/

更新軟連接

cd /usr/local/cuda/lib64/
sudo chmod +r libcudnn.so.5.0.5
sudo ln -sf libcudnn.so.5.0.5 libcudnn.so.5
sudo ln -sf libcudnn.so.5 libcudnn.so
sudo ldconfig

 4，設置環境變量

在/etc/profile中添加CUDA環境變量

sudo gedit /etc/profile

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

保存後, 執行下列命令, 使環境變量立即生效

source /etc/profile

同時需要添加lib庫路徑： 在 /etc/ld.so.conf.d/加入文件 cuda.conf, 內容如下

/usr/local/cuda/lib64

保存後，執行下列命令使之立刻生效

sudo ldconfig

5，安裝CUDA SAMPLE

進入/usr/local/cuda/samples, 執行下列命令來build samples

sudo make all -j4

整個過程大概10分鐘左右, 全部編譯完成後， 進入 samples/bin/x86_64/linux/release, 運行deviceQuery

./deviceQuery

如果出現顯卡信息， 則驅動及顯卡安裝成功：

./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

NOTE:上邊的顯卡信息是從別的地方拷過來的，我的GTX650顯卡不是這些信息，如果沒有這些信息，那肯定是安裝不成功，找原因吧！

6，安裝Intel MKL 或Atlas

我沒有MKL，裝的Atlas

安裝命令：

sudo apt-get install libatlas-base-dev

7，安裝OpenCV

我是準備用caffe的python接口，OpenCV就沒安裝了

1）下載安裝腳本

2）進入目錄 Install-OpenCV/Ubuntu/2.4

3）執行腳本

sh sudo ./opencv2_4_10.sh

8，安裝Caffe所需要的Python環境

按caffe官網的推薦使用Anaconda

去Anaconda官網下載安裝包
切換到文件所在目錄，執行

bash Anaconda-2.3.0-Linux-x86_64.s<em>h</em>

NOTE:後邊的文件名按自己下的版本號更改，整個安裝過程請選擇默認

  8.1，添加Anaconda Library Path

在/etc/ld.so.conf最後加入以下路徑，並沒有出現重啓不能進入界面的問題(NOTE:下邊的username要替換)

 

/home/username/anaconda/lib

在~/.bashrc最後添加下邊路徑

export LD_LIBRARY_PATH="/home/username/anaconda/lib:$LD_LIBRARY_PATH"

這裏要注意下如果是安裝的anaconda2，那麼上面需要加上2

9，安裝python依賴庫
去caffe的github下載caffe源碼包
進入caffe-master下的python目錄

執行如下命令

for req in $(cat requirements.txt); do pip install $req; done

10，編譯Caffe

終於來到這裏了

進入caffe-master目錄，複製一份Makefile.config.examples

cp Makefile.config.example Makefile.config

修改其中的一些路徑，如果前邊和我說的一致，都選默認路徑的話，那麼配置文件應該張這個樣子

## 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

# 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 \

# 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 ?= @

需要注意的是第5行，沒裝cuDNN的需要註釋，57,58行默認的有註釋符，需要去掉
保存退出

編譯

make all -j4
make test
make runtest

編譯這邊有很多問題，如果出現未定義的引用，偶然發現一個方法很好，就是在make all前面加sudo，原因不知道有誰可以解釋下，權限不夠爲什麼會導致未定義的引用

11，編譯Python wrapper

make  pycaffe

pycaffe的編譯需要在/.bashrc中增加export PYTHONPATH = “/home/username/caffe-master/python:$PYTHONPATH”

然後source ~/.bashrc

這是在bash中，如果需要在IDE中，比如spyder或者pycharm，在其環境變量文件中添加caffe-master/python的目錄即可