linux上caffe安装（docker 超简易操作版本）

由于caffe实在是太难安装了，需要依赖的库太多，安装了一天都没安装好（每安一个库都能 遇到一堆问题），最后选择在docker上安装，轻轻松松解决问题。

1.下载docker镜像

https://hub.docker.com/search/?isAutomated=0&isOfficial=0&page=1&pullCount=0&q=caffe&starCount=0
这里有各个版本的caffe docker，随意选择一个(我选择的第一个)
$ docker pull bvlc/caffe:gpu

然后查看docker images里面会有下好的这个docker，复制image id (这里是47dee10d8ba0)

$ nvidia-docker run -it -v 挂载目录:挂载目录 --name your_name 47dee10d8ba0 /bin/bash
然后就进入docker了

2.测试mnist例子

进入/opt/caffe

docker带的caffe目录，不需要再编译

可以试验下这里的mnist例子。

下载mnist数据集到这个目录下
$ ./data/mnist/get_mnist.sh

转换格式，在examples/mnist生成了两个目录：mnist_test_lmdb和mnist_train_lmdb
$ ./examples/mnist/create_mnist.sh

在 /opt/caffe文件夹下运行
$ ./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt

训练结果

成功！

3. tips

1.docker是个好东西，安装依赖包遇到一个又一个问题太要命了，不过安装过程让我对linux多些了解
2.安装好带caffe的docker，可以直接在pythom后import cafffe，但是在我从github上clone的caffe库里运行mnist的例子不行，还是得编译caffe，因为import各种包的时候位置不对，所以必须得进入docker自带的caffe文件夹里。

参考：https://www.cnblogs.com/wmlj/p/8681216.html # [运行caffe自带的mnist实例教程]

4. 后记（编译caffe）

以上步骤，可以使用标准的caffe了，但是caffe这个东西，和tensorflow不一样，它的层还得用c++写在源代码里，重新编译一次caffe才能用。
我需要用的一个代码，自带一个caffe文件夹，下面是他重写过的caffe，要想运行它的代码，就不能用标准caffe而是得编译他的caffe。
在caffe文件夹下，修改Makefile.config文件，

## 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
# This code is taken from https://github.com/sh1r0/caffe-android-lib
# USE_HDF5 := 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.
# For CUDA >= 9.0, comment the *_20 and *_21 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_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

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
#BLAS := atlas
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 := /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/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)/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 /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 ?= @
CUSTOM_CXX := g++ -std=c++11

然后
$ make clean
$ make all -j8 #使用8个进程
$ make pycaffe
这样就编译好了，因为之前的docker已经是把其他依赖库都装好了的，所以只需要重新编译下就行了。
如果还是报错“ImportError: No module named _caffe”，说明python的路径问题，sys.path.insert(0,"/caffe/python")即可