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此處使用的基礎鏡像為 nvcr.io/nvidia/digits:18.06，鏡像大小為6.04GB，可從nvidia官方pull此鏡像；
容器配置：
CUDA：9.0
CUDNN：7.0
注：此文檔建立在已會使用python2.7版本的DIGITS基礎之上
使用CUDA9是因為要使用tensorflow_hub，版本需要兼容
tensorflow-gpu==1.12.0
tensorflow-hub==0.5.0
鏡像中含有python3.5與python2.7兩個版本，直接使用python3.5
修改系統python默認值，使用python3為默認啟動：
sudo update-alternatives --install /usr/bin/python python /usr/bin/python2 100
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3 150
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一、編譯安裝caffe
從github下載caffe源碼，准備編譯，下載地址：https://github.com/BVLC/caffe.git
【CUDA與CUDNN請查找對應的安裝教程，此處忽略】
進入caffe目錄
1、安裝依賴：
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install —no-install-recommends libboost-all-dev
sudo apt-get install libopenblas-dev liblapack-dev libatlas-base-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
2、修改Makefile.config文件：
sudo cp Makefile.config.example Makefile.config
根據需求修改Makefile.config中的內容，我修改之后的內容如下：
## 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 := 1
# 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_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
# 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/python3.5 \
/usr/include/ \
/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 \
/usr/lib/python3.5 \
/usr/local/lib/python3.5
# 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 /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 $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
# enable pretty build (comment to see full commands)
Q ?= @
3、修改Makefile文件：
①、大概在427行
將：
NVCCFLAGS +=-ccbin=$(CXX) -Xcompiler-fPIC $(COMMON_FLAGS)
替換為：
NVCCFLAGS += -D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
②、大概在182行
將：
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5
改為：
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial
4、開始編譯：
①、make all
②、make runtest
③、make pycaffe
注：中途不出錯，則證明編譯安裝成功python3.5的caffe，如果拋出異常，則根據error搜索對應解決方案。
追加幾個我遇到的異常與對應解決方案：
異常：fatal error: pyconfig.h: No such file or directory
解決：export CPLUS_INCLUDE_PATH=/usr/include/python2.7
異常：/usr/bin/ld: cannot find -lboost_python3
解決：cd /usr/lib/x86_64-linux-gnu
sudo ln -s libboost_python-py35.so libboost_python3.so
5、使用caffe：
進入python解釋器：python
import caffe
異常：ImportError: dynamic module does not define module export function (PyInit__caffe)
解決：將編譯的caffe路徑添加到環境變量中：export PYTHONPATH=/opt/caffe/python/:$PYTHONPATH
二、安裝DIGITS，地址：https://github.com/NVIDIA/DIGITS.git
1、digits官方沒有推出Python3版本，需要自己把py2的代碼升級到py3
代碼語法問題可以使用python3自帶的升級腳本（腳本位置很好找，找不到的請百度）：python 3.5/Tools/scripts/2to3.py
2、使用2to3腳本升級完語法之后，代碼里還有很多py3不兼容的地方，請逐一修改
3、下載DIGITS的python依賴，pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
3、代碼修改完成之后，設置最后一步，將caffe的啟動文件軟連接到/usr/local/bin/下：
ln -s 源地址 目標地址
示例：ln -s /opt/caffe/build/tools/caffe /usr/local/bin/caffe
（把自己編譯的opt下的caffe啟動文件連接到系統path路徑中）
三、啟動DIGITS，使用caffe訓練模型
1、進入DIGITS目錄，啟動服務
python -m digits
2、用瀏覽器訪問服務，創建數據集，創建模型即可。
PS：有DIGITS二次開發經驗的朋友可以聯系交流，目前主要開發tensorflow_hub，tensorflow_pb，tensorflow，tensorRT等相關功能。