This article refers to the caffe official website tutorial and two online tutorials: Ubuntu14.04+CPU+Python Caffe installation tutorial , Caffe learning series (13): data visualization environment (python interface) configuration is written, because the process is more twists and turns, record it down for future reference
Install various dependencies for compiling caffe
Install base dependencies
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
Install BLAS
sudo apt-get install libatlas-base-dev
some other dependencies
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
Python coffee
Install Python
sudo apt-get install python-dev
###Install anaconda Download anaconda from Tsinghua's mirror. According to the version number provided by anaconda's official website, find the corresponding version from the directory of Tsinghua's mirror as Anaconda2-5.0.1-Linux-x86_64.sh (python2.7 version)
wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda2-5.0.1-Linux-x86_64.sh
After the download is complete, run this sh. During the installation process, you will be asked the installation path, just press Enter to default. There is a place asking you whether to add the anaconda installation path to the environment variable (.bashrc), this must be entered yes.
After the installation is successful, an anaconda2 folder will be generated in the root directory of the current user, which contains the installed content.
Download Caffe
git clone https://github.com/BVLC/caffe.git
configure python
Add the python folder in the root directory of caffe to the environment variable, and after compiling, you can import caffe to use it
Open the configuration file bashrc
sudo vi ~/.bashrc
add at the end
export PYTHONPATH=$(caffe_path)/python:$PYTHONPATH
Note that $(caffe_path) needs to be configured according to the actual situation of your caffe installation path
Save and exit, update the configuration file
source ~/.bashrc
Modify Makefile.config
First, copy Makefile.config.example into Makefile.config in the root directory of caffe
cp Makefile.config.example Makefile.config
Then modify Makefile.config, my configuration is as follows
## 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).
#因为使用的是cpu模式，所以要放开
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.
# 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
# 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.
#因为使用了anaconda所以要配置anaconda的地址，如果不配置则会出现找不到*.h的情况
ANACONDA_HOME := $(HOME)/anaconda2
#使用anaconda的头文件
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
#使用anaconda的库
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
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
# 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 ?= @
compile caffe
make pycaffe -j8
make all -j8
make test -j8
make runtest -j8
run python
Enter caffe/python and run python
python
Python 2.7.14 |Anaconda custom (64-bit)| (default, Oct 16 2017, 17:29:19)
[GCC 7.2.0] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import caffe
>>>
Entering import caffe and no response means success, but if there is a problem error: No module named google.protobuf.internal, run the following code:
conda install protobuf
Next, run the examples that come with caffe, you can refer to the two simple examples that come with running caffe