caffe/INSTALL.md

Installation

To build and test Caffe do

make
make test
make runtest

You may need to minimally tinker with paths in Makefile.config and maybe the Makefile itself.

Note that building and running CPU-only works, but GPU tests will naturally fail.

We mostly used Ubuntu 12.04 for development, and here we describe the step-to-step guide on installing Caffe on Ubuntu.

Prerequisites

• CUDA
• Boost
• MKL (but see the boost-eigen branch for a boost/Eigen3 port)
• OpenCV
• glog, gflags, protobuf, leveldb, snappy
• For the Python wrapper: python, numpy (>= 1.7 preferred), and boost_python
• For the Matlab wrapper: Matlab with mex

Caffe requires the CUDA NVCC compiler to compile its GPU code. To install CUDA, go to the NVidia CUDA website and follow installation instructions there. Caffe is verified to compile with both CUDA 5.0 and 5.5.

Caffe also needs Intel MKL as the backend of its matrix computation and vectorized computations. We are in the process of removing MKL dependency, but for now you will need to have an MKL installation. You can obtain a trial license or an academic license (if you are a student).

If you would like to compile the Python wrapper, you will need to install python, numpy and boost_python. You can either compile them from scratch or use a pre-packaged solution like Anaconda or Enthought Canopy. Note that if you use the Ubuntu default python, you will need to apt-install the python-dev package to have the python headers.

If you would like to compile the Matlab wrapper, you will need to install Matlab.

You will also need other packages, most of which can be installed via apt-get using:

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev

The only exception being the google logging library, which does not exist in the Ubuntu 12.04 repository. To install it, do:

wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
tar zxvf glog-0.3.3.tar.gz
./configure
make && make install

After setting all the prerequisites, you should modify the Makefile.config file and change the paths to those on your computer.

Compilation

After installing the prerequisites, simply do make all to compile Caffe. If you would like to compile the Python and Matlab wrappers, do

make pycaffe
make matcaffe

Optionally, you can run make distribute to create a build directory that contains all the necessary files, including the headers, compiled shared libraries, and binary files that you can distribute over different machines.

To use Caffe with python, you will need to add /path/to/caffe/python or /path/to/caffe/build/python to your PYTHONPATH.

Now that you have compiled Caffe, check out the MNIST demo and the pretrained ImageNet example.