Caffe安装过程记录
Caffe安装过程记录
总体配置
- Ubuntu16.04
- GCC 5.4.0
- Caffe + MKL + Anaconda3
参考文档
Caffe官网
Caffe安装官方文档
Python3.5 Anaconda3 Caffe深度学习框架搭建
本文未提及的问题可能会在其中出现，请经常翻阅并仔细阅读；出现问题还是Google比较靠谱
安装过程
依赖项目
安装过程中为了防止被墙，并且更好地看见每一项是否安装成功，我选择了一个一个地装
sudo apt-get install libprotobuf-dev
sudo apt-get install libleveldb-dev
sudo apt-get install libsnappy-dev
sudo apt-get install libopencv-dev
sudo apt-get install libhdf5-serial-dev
sudo apt-get install protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install libgflags-dev
sudo apt-get install libgoogle-glog-dev
sudo apt-get install liblmdb-dev- CUDA（GPU模式依赖）因为我只装CPU-ONLY，故此步省略
- BLAS 分为ATLAS，OPENBLAS， MKL，需要选择合适自己的一项，因为我的是Intel的CPU，所以选择MKL：
- Intel MKL，先到此处下载MKL（691MB），然后解压，终端输入
./install_GUI.sh进入GUI安装界面，安装（默认路径安装即可） - 如果是默认路径安装，安装完成后输入以设置环境变量
source /opt/intel/mkl/bin/mklvars.sh intel64 - （编译时）在
Makefile.config中设置BLAS := mkl
- Intel MKL，先到此处下载MKL（691MB），然后解压，终端输入
- Python/MATLAB 我选择的是Python，并且使用的是Anaconda，所以不需要怎么配置
编译
- 首先获取源码:
git clone https://github.com/BVLC/caffe.git，推荐放在～目录下 - 接着进入
python文件夹，输入for req in $(cat requirements.txt); do conda install $req; done将需要的包装上(Anaconda使用conda安装) - 然后输入
cp Makefile.config.example Makefile.config，因为我使用了Anaconda，而且安装的是CPU模式，所以需要修改Makefile.config
- 将第8行
# CPU_ONLY := 1反注释，用以设置CPU模式 - 50-55行设置为
BLAS := mkl，设置BLAS为MKL，然后设置BLAS_INCLUDE、BLAS_LIB - 设置Anaconda：68、69行注释掉；Anaconda相关的72-75行反注释，并且设置合适的路径（要进入所设置的路径，看看是否合法）；78行反注释并修改Python版本；83行注释，84行反注释；91行反注释
- 将第8行
- 添加Caffe/Python至环境变量
echo export PYTHONPATH=$CAFE_ROOT/python:$PYTHONPATH,$CAFE_ROOT为自己的caffe所在路径，添加后需要运行source ~./bashrc变量才能生效 - 尝试
make all
出现的错误
-lboost_python3 未找到– 设置链接sudo ln -s /usr/lib/x86_64-linux-gnu/libbost_python-py35.so.1.58.0 /usr/local/lib/libboost_python3.so,其中/usr/lib/x86_64-linux-gnu/libbost_python-py35.so.1.58.0要视具体情况修改GLIBCXX_3.4.21– 输入conda install libgccgoogle protobuf– 输入conda list | grep protobuf,将所有包用conda uninstall libprotobuf protobuf移除。重装protobuf，先到Github - Google protobuf下载C++、Python两个版本的protobuf，然后依照README安装
undefined symbol: PC– 输入locate libreadline.so.6，找到后复制到anaconda/lib下TIFF–conda remove libtiff（不知是否可行，忘了记录了）
leveldb–conda uninstall leveldb
使用MNIST测试
数据集准备
caffe自带了获取数据集的script，进入caffe主目录，输入./data/mnist/get_mnist.sh,./examples/mnist/create_mnist.sh获取
运行
- 需要配置
examples/mnist/下lenet_train_test.protxt, lenet_solver.prototxt, train_lenet.sh三个文件，简单的把路径设置好就可以了，需要注意这里不能用~作为/home/user的路径，必须把绝对路径完完整整地写上去 - 运行
./train_lenet.sh - 运行结果
...
I0922 15:04:22.516085 2727 solver.cpp:310] Iteration 10000, loss = 0.00184795
I0922 15:04:22.516126 2727 solver.cpp:330] Iteration 10000, Testing net (#0)
I0922 15:04:24.965847 2730 data_layer.cpp:73] Restarting data prefetching from start.
I0922 15:04:25.071223 2727 solver.cpp:397] Test net output #0: accuracy = 0.9919
I0922 15:04:25.071271 2727 solver.cpp:397] Test net output #1: loss = 0.0264297 (* 1 = 0.0264297 loss)
I0922 15:04:25.071283 2727 solver.cpp:315] Optimization Done.
I0922 15:04:25.071291 2727 caffe.cpp:259] Optimization Done.
总结
- 不妨使用
make -j n加快速度，n替换为处理器个数 make时候会出现非常多奇奇怪怪的错误，细想一下还是跟Anaconda3有很大关系，所以如果有怎么也无法解决的问题的时候，可以尝试使用conda uninstall去把Anaconda里面的包卸载了。- 可以尝试将出错的包手动编译。不过整个过程中我是用的是GCC5，至少是能顺利通过
make,make test,make runtest的；整个过程下来也只要手动编译protobuf而已。 - 这里我没有记录安装过程中
LD_LIBRARY_PATH的变化，但是整个安装结束后是这样的
export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=~/caffe/python/caffe:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH- 下面是
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 := 0
# USE_LEVELDB := 0
# USE_LMDB := 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.
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 := mkl
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
BLAS_INCLUDE := /opt/intel/mkl/include
BLAS_LIB := /opt/intel/mkl/lib
# 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)/anaconda3
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
$(ANACONDA_HOME)/include/python3.6m \
$(ANACONDA_HOME)/lib/python3.6/site-packages/numpy/core/include
# Uncomment to use Python 3 (default is Python 2)
PYTHON_LIBRARIES := boost_python3 python3.6m
# 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 /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 ?= @