ubuntu16.04 + caffe + SSD + gpu 安裝

昨天我們買好了硬件，今天我們開始安裝caffe了，我本人安裝過caffe不下10次，每次都是一大堆問題，后來終於總結了關鍵要點，就是操作系統.

1. 千萬不要用ubuntu17.10來安裝，

2. 最好的操作系統是ubuntu16.04

如果用17版本的來安裝的話，很多時候會遇到要降級gcc的，降級也是非常麻煩的事，因為降級或升級的時候會需要安裝很多其他的東西，無形中會打亂整個系統的安裝環境，最后到時候又會遇到其他的問題，所以安裝caffe的最重要環節是保持一個干凈的適合的系統。

 

1.安裝CUDA 8.0

安裝CUDA之前，先檢查機器是否安裝了NVIDIA驅動。使用命令

1. nvidia-smi
   

查看GPU列表，同時顯示了驅動的版本。也可以通過命令

1. nvidia-settings  

注意上面我的顯卡是384.98,后面我們會用到。

查看GPU的詳細信息。如果沒有安裝驅動，則執行下面的命令(注意，我的顯卡是GTX1060,所以安裝nvidia-384,這個命令要根據你的顯卡來安裝)

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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  

安裝NVIDIA的384版本的驅動。

下面開始安裝CUDA 8.0。登陸CUDA官網去下載，不過現在官網下載的好像是9的版本，建議下載8的版本，

不要安裝9的版本，可能有問題，你打開官網可能會讓你安裝9的版本，你可以到我的百度盤下載。

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

上面也同時給出了安裝命令：

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：在執行上述命令之前，一定要進入安裝文件所在的路徑，也即是下載的CUDA安裝文件所在的地方，一般是/home/Downloads，所以先運行命令

cd Downloads  

然后執行上面三行代碼安裝CUDA 8.0.

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

如果顯示一些關於GPU的信息，則說明安裝成功。

4.配置cuDNN
查看自己電腦顯卡的計算能力：https://developer.nvidia.com/cuda-gpus
cuDNN是GPU加速計算深層神經網絡的庫。
首先去官網 https://developer.nvidia.com/rdp/cudnn-download 下載cuDNN，需要注冊一個賬號才能下載。下載版本號如下圖：

我下載了一份，你可以從我的百度盤這里下載。

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

下載cuDNN5.1之后進行解壓：
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/

更新軟連接: 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

請注意，請到自己解壓后的lib64文件夾看這個文件libcudnn.so.5.0.5 ，電腦配置不同后面的數字型號不同，進行相應的修改，否則會報錯。

2.配置Caffe

安裝好CUDA之后，就可以配置Caffe了。

(1)通過下面的命令安裝protobuf，leveldb，snappy，OpenCV，hdf5，boost依賴庫

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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)安裝BLAS庫

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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)獲取Caffe源碼

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，主要是修改了以下兩行.

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/ 
   

這一步執行沒有問題，接着修改/etc/profile文件

su root

-- input password

vi /etc/profile

export PYTHONPATH=$CAFFE_ROOT/python

然后開始運行以下命令

make -j8

make pymake test -j8

make runtest -j8

 

沒有報錯的話，至此，Caffe已經安裝並配置成功了。