I am leaving this up for the sake of posterity. It should absolutely not be followed. I think it is probably best to just not use matcaffe for anything…
Installing Caffe is a huge pain. Here is how I am doing it. I don’t have a GPU, I want to be able to run in python2 and python3 as well as Matlab. I haven’t tested this enough to know if it works how I want it to, I am in the process of learning caffe. This is mostly just for my notes.
There is a really good
reference and the official (not so good) Install Instructions Some dependencies:
Matlab installation kind of varies based on where you get it from, butmake sure it is installed
Setting up MEX compilation
Matlab needs an older compiler, 4.9
sudo apt-get install gcc-4.9 g++-4.9
# you may need to update alternatives
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.9 20
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.9 20
sudo update-alternatives --config gcc
sudo update-alternatives --config g++
This may or may not be necessary:
sudo gedit /usr/local/MATLAB/R2017a/bin/mexopts.sh
edit line 54 from
CC='gcc-4.9' and edit line 69 from
First get all the easy stuff out of the way (copied from the
sudo apt-get update
sudo apt-get upgrade
sudo apt-get install -y build-essential cmake git pkg-config
sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install -y libatlas-base-dev
sudo apt-get install -y --no-install-recommends libboost-all-dev
sudo apt-get install -y libgflags-dev libgoogle-glog-dev liblmdb-dev
# (Python general)
sudo apt-get install -y python-pip
# (Python 2.7 development files)
sudo apt-get install -y python-dev
# sudo apt-get install -y python-numpy python-scipy # honestly not sure that this is needed
# (or, Python 3.5 development files)
sudo apt-get install -y python3-dev
# sudo apt-get install -y python3-numpy python3-scipy
# sudo apt-get install -y python-numpy python-scipy
# (OpenCV 2.4)
# sudo apt-get install -y libopencv-dev # I prefer to use 3.2 OpenCV
I follow this
guide except that I use OpenCV 3.2. Python
I chose to try to install for both python 2 and 3 in virtual environments. So:
mkvirtualenv caffe2 -p python2
mkvirtualenv caffe3 -p python3
Then I went into the python folder in the caffe repo and executed:
for req in $(cat requirements.txt); do pip install $req; done for each of the virtual env (
I then followed the instructions from the guide and did some sym linking:
sudo ln -s libhdf5_serial.so.10.1.0 libhdf5.so
sudo ln -s libhdf5_serial_hl.so.10.0.2 libhdf5_hl.so Make File
I then edited the makefile (first copy the example to make your working copy):
## 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 := 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/R2017a
# 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 := ~/.virtualenvs/caffe2/include/python2.7 \
# 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 \
# Uncomment to use Python 3 (default is Python 2)
PYTHON_LIBRARIES := boost_python3 python3.5m
PYTHON_INCLUDE := ~/.virtualenvs/caffe3/include/python3.5m \
# 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 \
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial #/usr/local/share/OpenCV/3rdparty/lib/
LIBRARIES += glog gflags protobuf leveldb snappy \
lmdb boost_system boost_filesystem hdf5_hl hdf5 m \
opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs opencv_videoio
# 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 ?= @
Finally, I build and test:
make distribute Final touch up to help python find caffe