最全caffe安裝踩坑記錄（Anaconda,nvidia-docker,Linux編譯）

Anaconda,nvidia-docker,Linux三種方式安裝caffe

1.Anaconda安裝caffe

　　1.首先安裝anaconda

　　2.建立虛擬環境（python2.7）　　

conda create -n caffe python=2.7 anaconda

　　3.安裝caffe

# 而後下面二選一便可，安裝caffe
conda install -c conda-forge caffe
conda install -c conda-forge/label/broken caffe 

　　4.注意：

# 若是出現numpy導入錯誤，是由於 conda-forge中的numpy版本太低，可再輸入下面命令
pip install numpy --upgrade

　　5.測試

# 測試是否成功
source activate caffe
python
import caffe

2.nvidia-docker安裝caffe-gpu

注意：docker僅支持cpu
nvidia-docker支持GPU

　　1.安裝nvidia-docker

wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb
sudo dpkg -i /tmp/nvidia-docker*.deb
# 測試
nvidia-docker run --rm nvidia/cuda nvidia-smi

　　2.拉取caffe-gpu鏡像

nvidia-docker pull bvlc/caffe:gpu

　　3.啓動一個容器

nvidia-docker run --rm -ti <鏡像名字>
docker attach <容器id>  # 已打開的容器
# 退出容器 ctrl + d （容器不運行）或者ctrl q+p(容器後臺運行)
# 進入未運行的容器
docker ps -a # 查看容器id
docker start  <id>
docker attach <id>
# 容器更名
dockers rename 原id 新id

　　4.更新源

apt uptate

　　5.測試

#caffe安裝目錄默認在/opt/caffe,若想支持opencv，多gpu，請自行編譯，可參看第三種方式編譯安裝caffe

python
import caffe

 3.Linux編譯caffe，並支持多GPU

　　1.linux安裝git

# Centos
yum install git
# Ubuntu
sudo apt-get install git

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

　　2.編譯前的Makefile.config配置

# 新建Makefile.config
mv Makefile.config.example Makefile.config

注意：

  1 ## Refer to http://caffe.berkeleyvision.org/installation.html
  2 # Contributions simplifying and improving our build system are welcome!
  3 
  4 # cuDNN acceleration switch (uncomment to build with cuDNN).
  5  USE_CUDNN := 1
  6 
  7 # CPU-only switch (uncomment to build without GPU support).
  8 # CPU_ONLY := 1
  9 
 10 # uncomment to disable IO dependencies and corresponding data layers
 11 # USE_OPENCV := 0
 12 # USE_LEVELDB := 0
 13 # USE_LMDB := 0
 14 
 15 # uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
 16 #    You should not set this flag if you will be reading LMDBs with any
 17 #    possibility of simultaneous read and write
 18 # ALLOW_LMDB_NOLOCK := 1
 19 
 20 # Uncomment if you're using OpenCV 3
 21  OPENCV_VERSION := 3
 22 
 23 # To customize your choice of compiler, uncomment and set the following.
 24 # N.B. the default for Linux is g++ and the default for OSX is clang++
 25 # CUSTOM_CXX := g++
 26 
 27 # CUDA directory contains bin/ and lib/ directories that we need.
 28 CUDA_DIR := /usr/local/cuda
 29 # On Ubuntu 14.04, if cuda tools are installed via
 30 # "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
 31 # CUDA_DIR := /usr
 32 
 33 # CUDA architecture setting: going with all of them.
 34 # For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility.
 35 # For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
 36 # For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility.
 37 CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \
 38         -gencode arch=compute_35,code=sm_35 \
 39         -gencode arch=compute_50,code=sm_50 \
 40         -gencode arch=compute_52,code=sm_52 \
 41         -gencode arch=compute_60,code=sm_60 \
 42         -gencode arch=compute_61,code=sm_61 \
 43         -gencode arch=compute_61,code=compute_61
 44 
 45 # BLAS choice:
 46 # atlas for ATLAS (default)
 47 # mkl for MKL
 48 # open for OpenBlas
 49 BLAS := atlas
 50 # Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
 51 # Leave commented to accept the defaults for your choice of BLAS
 52 # (which should work)!
 53 # BLAS_INCLUDE := /path/to/your/blas
 54 # BLAS_LIB := /path/to/your/blas
 55 
 56 # Homebrew puts openblas in a directory that is not on the standard search path
 57 # BLAS_INCLUDE := $(shell brew --prefix openblas)/include
 58 # BLAS_LIB := $(shell brew --prefix openblas)/lib
 59 
 60 # This is required only if you will compile the matlab interface.
 61 # MATLAB directory should contain the mex binary in /bin.
 62 # MATLAB_DIR := /usr/local
 63 # MATLAB_DIR := /Applications/MATLAB_R2012b.app
 64 
 65 # NOTE: this is required only if you will compile the python interface.
 66 # We need to be able to find Python.h and numpy/arrayobject.h.
 67 PYTHON_INCLUDE := /usr/include/python2.7 \
 68         /usr/lib/python2.7/dist-packages/numpy/core/include
 69 # Anaconda Python distribution is quite popular. Include path:
 70 # Verify anaconda location, sometimes it's in root.
 71 # ANACONDA_HOME := $(HOME)/anaconda
 72 # PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
 73         # $(ANACONDA_HOME)/include/python2.7 \
 74         # $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
 75 
 76 # Uncomment to use Python 3 (default is Python 2)
 77 # PYTHON_LIBRARIES := boost_python3 python3.5m
 78 # PYTHON_INCLUDE := /usr/include/python3.5m \
 79 #                 /usr/lib/python3.5/dist-packages/numpy/core/include
 80 
 81 # We need to be able to find libpythonX.X.so or .dylib.
 82 PYTHON_LIB := /usr/lib
 83 # PYTHON_LIB := $(ANACONDA_HOME)/lib
 84 
 85 # Homebrew installs numpy in a non standard path (keg only)
 86 # PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
 87 # PYTHON_LIB += $(shell brew --prefix numpy)/lib
 88 
 89 # Uncomment to support layers written in Python (will link against Python libs)
 90 WITH_PYTHON_LAYER := 1
 91 
 92 # Whatever else you find you need goes here.
 93 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
 94 LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial
 95 
 96 # If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
 97 # INCLUDE_DIRS += $(shell brew --prefix)/include
 98 # LIBRARY_DIRS += $(shell brew --prefix)/lib
 99 
100 # NCCL acceleration switch (uncomment to build with NCCL)
101 # https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)
102 USE_NCCL := 1
103 
104 # Uncomment to use `pkg-config` to specify OpenCV library paths.
105 # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
106 # USE_PKG_CONFIG := 1
107 
108 # N.B. both build and distribute dirs are cleared on `make clean`
109 BUILD_DIR := build
110 DISTRIBUTE_DIR := distribute
111 
112 # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
113 # DEBUG := 1
114 
115 # The ID of the GPU that 'make runtest' will use to run unit tests.
116 TEST_GPUID := 0
117 
118 # enable pretty build (comment to see full commands)
119 Q ?= @

　　1.第5行USE_CUDNN := 1（安裝和cuda匹配的cudnn）

　　2.第21行 OPENCV_VERSION := 3（安裝opencv）

sudo apt install git
git clone https://github.com/jayrambhia/Install-OpenCV
cd Install-OpenCV/Ubuntu/
chmod +x *
./opencv_latest.sh

make -j8 && make pycaffe 

　　3.第28行CUDA_DIR := /usr/local/cuda（安裝cuda的目錄）

　　4.第49行BLAS := atlas

　　5.第67，68行

PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/lib/python2.7/dist-packages/numpy/core/include

　　6.第93，94行

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

　　7.第102行USE_NCCL := 1（支持多gpu）

git clone https://github.com/NVIDIA/nccl.git
cd nccl
sudo make install
sudo gedit ~/.bashrc
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/home/XXX/nccl/build/lib/"  #XXX爲用戶名
source .bashrc

使用多個gpu 
咱們在使用caffe的工具訓練網絡時，在語句的最後加上如下內容便可

-gpu all  #運行全部的gpu
-gpu 1,2  #運行1號2號gpu

　　3.開始編譯

sudo make all
sudo make test
sudo make pycaffe
sudo make runtest

　　4.測試

python
import caffe