Anaconda沙盒+Caffe编译安装 v2

环境说明：

Ubuntu 16.04
CUDA = 9.0
CUDNN = 7.5.1
python = 2.7
protobuf = 2.6.1
gcc = 5.4.0
g++ = 5.4.0

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安装步骤

1. 创建虚拟环境并安装依赖

用 conda 创建一个虚拟环境 caffe27（名字随意，这里用27的意思是python版本是2.7的意思），激活该环境，并进入该环境目录下 clone 一下 caffe 的源文件：

# 创建虚拟环境
conda create -n caffe27 python=2.7
# 激活环境
conda activate caffe27
# 进入环境目录下
cd anaconda/envs/caffe27/
# 下载caffe文件
git clone https://github.com/BVLC/caffe.git

安装系统级的依赖：

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

在沙盒caffe27内安装依赖：

# 可以直接安装所有需要的包
conda install caffe-gpu
# 这里下载的protobuf是3.7版本，为了不和后面要使用的2.6.1版本冲突，先卸载掉
conda uninstall protobuf
conda uninstall libprotobuf
# 这里自动安装了cudnn7.3.1，但是我们需要的是cudnn7.5.1且已经安装好了，所以卸载掉
conda uninstall cudnn

2. 修改配置文件

修改配置文件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 := 1
USE_LEVELDB := 1
USE_LMDB := 1
# 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 := /data/zyy/usr/local/cuda-9.0
# 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/envs/caffe27
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) $(ANACONDA_HOME)/include /usr/local/include /usr/include/hdf5/serial/
LIBRARY_DIRS := $(PYTHON_LIB) $(ANACONDA_HOME)/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 ?= @

CXXFLAGS+=-std=c++11
CUSTOM_CXX := g++ -std=c++11

具体这些怎么改参考另一篇：Ubuntu 16.04下Caffe的配置过程

3. 编译caffe

# 在caffe根目录下
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=Release -D BLAS=Open -D BUILD_SHARED_LIBS=Off ..

特别关注一下 cmake 显示的 protobuf 版本信息，查看当前使用的 protobuf 的版本：

whereis protoc
which protoc
protoc --version

cmake 输出的配置信息，关注一下版本都是否正确：


继续编译：（可能会有warning，但没有error就行）

make all -j8
make install -j8
make runtest -j8

make all -j8 输出：

make install -j8 输出：

runtest 最后输出：

最后注意环境变量里要在PYTHONPATH中添加caffe/python的路径，并更新环境变量：（注意是caffe工程根目录下的python！）

export PYTHONPATH="/data/zyy/usr/local/anaconda/envs/caffe27/caffe/python"

# 更新环境变量
source ~/.bashrc

# 重新进入虚拟环境
conda activate caffe27

再编译 python 用到的 caffe 文件，否则 python 无法 import caffe：

make pycaffe -j8

4. 验证caffe

验证一下 caffe 是否安装成功，只要 import 没有报错就行。

conda activate caffe27
python
import caffe
# 查看caffe版本
print caffe.__version__

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安装问题记录

2019/7/4 8:38更新

（1）camke … 时出现 "cuDNN not found"

在第一步 cmake .. 时会打印 Caffe 配置信息，仔细查看 NVIDIA CUDA 下的 cuDNN 信息，出现下面的情况 Not found，为什么找不到具体原因不明。

解决方案：在 cmake 时手动指定一下 cuDNN 的路径（下面无视我的 cuda 版本，这是另一个 cuda 环境，和上文配置无关）

# 注意分别是cuDNN解压出来的include和lib64，不是cuda下面的
cmake  -DCUDNN_INCLUDE="/data/zyy/usr/local/cuda/cuda8.0/cuda/include" -DCUDNN_LIBRARY="/data/zyy/usr/local/cuda/cuda8.0/cuda/lib64/libcudnn.so"