linux服務器端 + cuda10 + anaconda3 + py35 + opencv3 之 caffe安裝

本文算是caffe踩坑記錄，折騰了兩天，最終解決了，欣喜之餘趕緊記錄一波。

前提條件：

已安裝cuda ~~, 但cudnn沒有安裝（因爲沒權限）~~
- 更正，使用conda install cudnn即可安裝，編譯caffe的時候打開cudnn加速也是可以的
無法使用sudo權限
沒有圖形界面，是服務器端

0. 如何查看版本號

查看cuda版本 cat /usr/local/cuda/version.txt
查看cudnn版本 cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
查看python版本 python -V
查看opencv版本

進入python
import cv2
cv2.__version__

1. 安裝anaconda

上官網 https://www.anaconda.com/distribution/, 選擇對應的anaconda版本，這裏我選擇的是linux + python 3.7 版本，使用wget進行下載：

wget https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh

然後進行安裝 sh Anaconda3-2019.03-Linux-x86_64.sh，注意記得修改安裝路徑，一般不建議裝到/裏，因爲linux系統掛載方式決定了/相當於是win的C盤，最好是放在/data*/裏面。

完成後修改用戶環境變量vim ~/.bashrc，在文件末尾添加anaconda3的安裝路徑，例如：

# add anaconda env
export PATH=/data*/***/tool/anaconda3/bin:$PATH

接着更新環境變量source ~/.bashrc

2. 安裝編譯環境

新建虛擬環境
如果不想多個項目依賴衝突，還是建立個虛擬環境把各個項目給隔開吧

conda create -n caffe_py35 python=3.5

這裏-n代表命名，python=3.5規定python的版本號

conda env list 輸出所有虛擬環境名
conda activate XXX 激活XXX環境
conda deactivate 關閉已激活的XXX環境
conda remove -n XXX --all 刪除XXX環境

因此此處鍵入conda activate caffe_py35，激活python3.5環境，這是終端應該會顯示(caffe_py35)xxx@xxx: xxx$，查看python的版本號也可以發現，是py35版。

安裝opencv

conda install opencv

這裏py35對應的是opencv3，所以也不需要刻意規定opencv的版本，可通過查看opencv版本的方法判斷是否成功安裝。

3. 編譯caffe

進入caffe_py35虛擬環境
找個好地方把caffe源碼git下來，不知道是下在release版，還是直接git好，這裏我就直接git了

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

進入caffe目錄，配置Makefile.config

巨坑預警

這裏把自帶的config複製過來

cp Makefile.config.example Makefile.config

在它的基礎上進行修改。這時候你得仔細閱讀其中的每一項，找到有關python和anaconda的行，進行修改操作，修改總結一下，大致有6處：

# Uncomment if you're using OpenCV 3
OPENCV_VERSION := 3
# ---------------------
# 因爲是py35，因此這裏需要修改
# ---------------------

# 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
#-gencode arch=compute_20,code=sm_20 \
#-gencode arch=compute_20,code=sm_21 \
# ---------------------
# 因爲是cuda10，所以把 *_20 and *_21 lines 刪去
# ---------------------

# 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 := ../anaconda3/envs/caffe_py35
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
		$(ANACONDA_HOME)/include/python3.5m \
		$(ANACONDA_HOME)/lib/python3.5/site-packages/numpy/core/include
# ---------------------
# 這裏是anaconda的安裝位置，因爲是虛擬環境，一定要注意ANACONDA_HOME的位置
# ---------------------

# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
PYTHON_LIBRARIES := boost_python-py35
# ---------------------
# 需要libboost，去/usr/lib/x86_64-linux-gnu/找一找libboost_python-py35.so.1.58.0是否匹配
# ---------------------

# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial 
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial
LINKFLAGS := -Wl,-rpath,$(PYTHON_LIB)
# ---------------------
# LINKFLAGS是 * [.build_release/tools/compute_image_mean.bin] Error 1 的坑，需要重新定義lib的位置
# ---------------------

這裏我把我的Makefile.config貼出來供大家參考：

## 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
# ---------------------
# 需要用cudnn加速不，我這裏沒有cudnn，就沒有取消註釋
# ---------------------

# 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
# ---------------------
# 因爲是py35，因此這裏需要修改
# ---------------------

# 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
# ---------------------
# cuda的安裝位置，需要自行注意
# ---------------------

# 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
#-gencode arch=compute_20,code=sm_20 \
#-gencode arch=compute_20,code=sm_21 \
# ---------------------
# 因爲是cuda10，所以把 *_20 and *_21 lines 刪去
# ---------------------

# 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 := ../anaconda3/envs/caffe_py35
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
		$(ANACONDA_HOME)/include/python3.5m \
		$(ANACONDA_HOME)/lib/python3.5/site-packages/numpy/core/include
# ---------------------
# 這裏是anaconda的安裝位置，因爲是虛擬環境，一定要注意ANACONDA_HOME的位置
# ---------------------

# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
PYTHON_LIBRARIES := boost_python-py35
# ---------------------
# 需要libboost，去/usr/lib/x86_64-linux-gnu/找一找libboost_python-py35.so.1.58.0是否匹配
# ---------------------

# 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
# ---------------------
# 這裏是anaconda的安裝位置
# ---------------------

# 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 := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial
LINKFLAGS := -Wl,-rpath,$(PYTHON_LIB)
# ---------------------
# LINKFLAGS是 * [.build_release/tools/compute_image_mean.bin] Error 1 的坑，需要重新定義lib的位置
# ---------------------

# 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 ?= @

make

make clean
make all
make test
make runtest
make pycaffe

看到一切都是 - RUN OK - ，舒服了

添加caffe到環境變量

vim ~/.bashrc，在末尾加入

export PYTHONPATH=/data*/***/tool/caffe/python

import caffe的坑
1. ImportError: No module named google.protobuf.internal
  到anaconda的bin文件夾下使用pip install protobuf，千萬不要直接conda install，不然會沒用的。
2. ImportError: No module named ‘skimage’
  同樣地，到anaconda的bin裏面使用pip install scikit-image
3. ImportError: libhdf5.so.101: cannot open shared object file: No such file or directory
  到環境依賴~/.bashrc裏面進行修改，把在Makefile.config裏面的libhdf5路徑複製進去：

export LD_LIBRARY_PATH="/data*/***/tool/anaconda3/lib":$LD_LIBRARY_PATH

記得source ~/.bashrc下。

linux服務器端 + cuda10 + anaconda3 + py35 + opencv3 之 caffe安裝

目錄

linux服務器端 + cuda10 + anaconda3 + py35 + opencv3 之 caffe安裝

0. 如何查看版本號

1. 安裝anaconda

2. 安裝編譯環境

3. 編譯caffe

4. 參考資料與致謝

OpenVINO於linux無UI無sudo權限、全命令行安裝記錄

手把手教你使用pytorch+flask搭建草圖檢索系統（三）

`_GLIBCXX_USE_CXX11_ABI`有什麼作用

OpenVINO之鏈接庫

ONNX內部節點修改方法

Mac下配置sublime實現LaTeX

https://yachay.unat.edu.pe/blog/index.php?comment_area=format_blog&comment_component=blog&comment_co

linux以太網驅動總結