ubuntu16.04 + caffe + SSD + gpu 安装

昨天我们买好了硬件，今天我们开始安装caffe了，我本人安装过caffe不下10次，每次都是一大堆问题，后来终于总结了关键要点，就是操作系统.

1. 千万不要用ubuntu17.10来安装，

2. 最好的操作系统是ubuntu16.04

如果用17版本的来安装的话，很多时候会遇到要降级gcc的，降级也是非常麻烦的事，因为降级或升级的时候会需要安装很多其他的东西，无形中会打乱整个系统的安装环境，最后到时候又会遇到其他的问题，所以安装caffe的最重要环节是保持一个干净的适合的系统。

 

1.安装CUDA 8.0

安装CUDA之前，先检查机器是否安装了NVIDIA驱动。使用命令

1. nvidia-smi

查看GPU列表，同时显示了驱动的版本。也可以通过命令

1. nvidia-settings  

注意上面我的显卡是384.98,后面我们会用到。

查看GPU的详细信息。如果没有安装驱动，则执行下面的命令(注意，我的显卡是GTX1060,所以安装nvidia-384,这个命令要根据你的显卡来安装)

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1. sudo add-apt-repository ppa:graphics-drivers/ppa
2. sudo apt-get update
3. sudo apt-get install nvidia-384
4. sudo apt-get install mesa-common-dev
5. sudo apt-get install freeglut3-dev

安装NVIDIA的384版本的驱动。

下面开始安装CUDA 8.0。登陆CUDA官网去下载，不过现在官网下载的好像是9的版本，建议下载8的版本，

不要安装9的版本，可能有问题，你打开官网可能会让你安装9的版本，你可以到我的百度盘下载。

https://pan.baidu.com/s/1bp123Np

上面也同时给出了安装命令：

1. sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb
2. sudo apt-get update
3. sudo apt-get install cuda

注意：在执行上述命令之前，一定要进入安装文件所在的路径，也即是下载的CUDA安装文件所在的地方，一般是/home/Downloads，所以先运行命令

cd Downloads  

然后执行上面三行代码安装CUDA 8.0.

(4)测试CUDA的samples
cd /usr/local/cuda-8.0/samples/1_Utilities/deviceQuery
make
sudo ./deviceQuery

如果显示一些关于GPU的信息，则说明安装成功。

4.配置cuDNN
查看自己电脑显卡的计算能力：https://developer.nvidia.com/cuda-gpus

cuDNN是GPU加速计算深层神经网络的库。
首先去官网 https://developer.nvidia.com/rdp/cudnn-download 下载cuDNN，需要注册一个账号才能下载。下载版本号如下图：

我下载了一份，你可以从我的百度盘这里下载。

https://pan.baidu.com/s/1gfzs2bD

下载cuDNN5.1之后进行解压：
sudo tar -zxvf ./cudnn-8.0-linux-x64-v5.1.tgz

cd cuda;
sudo cp lib64/lib* /usr/local/cuda/lib64/;



sudo cp include/cudnn.h /usr/local/cuda/include/


更新软连接: cd /usr/local/cuda/lib64/



sudo chmod +r libcudnn.so.5.1.10



sudo ln -sf libcudnn.so.5.1.10 libcudnn.so.5



sudo ln -sf libcudnn.so.5 libcudnn.so



sudo ldconfig

请注意，请到自己解压后的lib64文件夹看这个文件libcudnn.so.5.0.5 ，电脑配置不同后面的数字型号不同，进行相应的修改，否则会报错。

2.配置Caffe

安装好CUDA之后，就可以配置Caffe了。

(1)通过下面的命令安装protobuf，leveldb，snappy，OpenCV，hdf5，boost依赖库

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1. sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
2. sudo apt-get install --no-install-recommends libboost-all-dev

(2)安装BLAS库

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1. sudo apt-get install libatlas-base-dev

(3)接着是gflags, glog和lmdb

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

(4)获取Caffe源码

git clone https://github.com/weiliu89/caffe.git

cd caffe

git checkout ssd

(5) 配置Caffe

1. cp Makefile.config.example Makefile.config

## 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 := 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 through *_61 lines for compatibility.
# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
# CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
# 		-gencode arch=compute_20,code=sm_21 \
# CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \
#		-gencode arch=compute_35,code=sm_35 \
#		-gencode arch=compute_50,code=sm_50 \
#		-gencode arch=compute_52,code=sm_52 \
#		-gencode arch=compute_60,code=sm_60 \
#		-gencode arch=compute_61,code=sm_61 \
#		-gencode arch=compute_61,code=compute_61
CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \
		-gencode arch=compute_60,code=sm_60 \
		-gencode arch=compute_61,code=sm_61 \
		-gencode arch=compute_61,code=compute_61

# 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

# 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

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/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

# NCCL acceleration switch (uncomment to build with NCCL)
# https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)
# USE_NCCL := 1

# 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 ?= @

用我上面的Makefile.config就可以了，主要是修改了以下两行.

1. INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/
2. LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial/

这一步执行没有问题，接着修改/etc/profile文件

su root

-- input password

vi /etc/profile

export PYTHONPATH=$CAFFE_ROOT/python

然后开始运行以下命令

make -j8

make pymake test -j8

make runtest -j8

没有报错的话，至此，Caffe已经安装并配置成功了。