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Caffe

Installation

Prior to installing, it is best to read through this guide and take note of the details for your platform. We have successfully compiled and run Caffe on Ubuntu 12.04, OS X 10.8, and OS X 10.9.

Prerequisites

Caffe depends on several software packages.

  • CUDA (5.0, 5.5, or 6.0).
  • BLAS (provided via ATLAS, MKL, or OpenBLAS).
  • OpenCV.
  • Boost (we have only tested 1.55)
  • glog, gflags, protobuf, leveldb, snappy, hdf5
  • For the Python wrapper
    • Python, numpy (>= 1.7), boost-provided boost.python
  • For the MATLAB wrapper
    • MATLAB with the mex compiler.

CUDA and BLAS

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. Note: you can install the CUDA libraries without a CUDA card or driver, in order to build and run Caffe on a CPU-only machine.

Caffe requires BLAS as the backend of its matrix and vector computations. There are several implementations of this library. The choice is yours:

  • ATLAS: free, open source, and so the default for Caffe.
    • Ubuntu: sudo apt-get install libatlas-base-dev
    • CentOS/RHEL: sudo yum install libatlas-devel
    • OS X: already installed as the Accelerate / vecLib Framework.
  • Intel MKL: commercial and optimized for Intel CPUs, with a free trial and student licenses.
    1. Install MKL.
    2. Set BLAS := mkl in Makefile.config
  • OpenBLAS: free and open source; this optimized and parallel BLAS could require more effort to install, although it might offer a speedup.
    1. Install OpenBLAS
    2. Set BLAS := open in Makefile.config

Python and/or Matlab wrappers (optional)

Python: The main requirements are numpy and boost.python (provided by boost). pandas is useful too and needed for some examples.

For OS X, we highly recommend using the Anaconda Python distribution, which provides most of the necessary packages, as well as the hdf5 library dependency. If you don't, please use Homebrew -- but beware of potential linking errors!

Note that if you use the Ubuntu default python, you will need to apt-get install the python-dev package to have the python headers. You can install any remaining dependencies with

pip install -r /path/to/caffe/python/requirements.txt

MATLAB: install MATLAB, and make sure that its mex is in your $PATH.

The rest of the dependencies

Linux

On Ubuntu, the remaining dependencies can be installed with

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev

And on CentOS or RHEL, you can install via yum using:

sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel

The only exceptions being the google flags library and the google logging library, which does not exist in the Ubuntu 12.04 or CentOS/RHEL repositories. To install them, do:

# glog
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
# gflags
wget https://github.com/schuhschuh/gflags/archive/master.zip
unzip master.zip
cd gflags-master
mkdir build && cd build
cmake ..
make && make install

Note that glog does not compile with the most recent gflags version (2.1), so before that is solved you will need to build with gflags first. Also note that if you have newer versions of Ubuntu, like 13.04 or 14.04, gflags may be available under apt-get.

OS X

On OS X, we highly recommend using the homebrew package manager, and ideally starting from a clean install of the OS (or from a wiped /usr/local) to avoid conflicts. In the following, we assume that you're using Anaconda Python and Homebrew.

To install the OpenCV dependency, we'll need to provide an additional source for Homebrew:

brew tap homebrew/science

If using Anaconda Python, a modification is required to the OpenCV formula. Do brew edit opencv and change the lines that look like the two lines below to exactly the two lines below.

  -DPYTHON_LIBRARY=#{py_prefix}/lib/libpython2.7.dylib
  -DPYTHON_INCLUDE_DIR=#{py_prefix}/include/python2.7

NOTE: We find that everything compiles successfully if $LD_LIBRARY_PATH is not set at all, and $DYLD_FALLBACK_LIBRARY_PATH is set to to provide CUDA, Python, and other relevant libraries (e.g. /usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib). In other ENV settings, things may not work as expected.

10.8-specific Instructions

Simply run the following:

brew install --build-from-source --with-python boost
for x in snappy leveldb protobuf gflags glog szip homebrew/science/opencv; do brew install $x; done

Building boost from source is needed to link against your local Python (exceptions might be raised during some OS X installs, but ignore these and continue). If you do not need the Python wrapper, simply doing brew install boost is fine.

Note that the HDF5 dependency is provided by Anaconda Python in this case. If you're not using Anaconda, include hdf5 in the list above.

10.9-specific Instructions

In OS X 10.9, clang++ is the default C++ compiler and uses libc++ as the standard library. However, NVIDIA CUDA (even version 6.0) currently links only with libstdc++. This makes it necessary to change the compilation settings for each of the dependencies.

We do this by modifying the homebrew formulae before installing any packages. Make sure that homebrew doesn't install any software dependencies in the background; all packages must be linked to libstdc++.

The prerequisite homebrew formulae are

boost snappy leveldb protobuf gflags glog szip homebrew/science/opencv

For each of these formulas, brew edit FORMULA, and add the ENV definitions as shown:

  def install
      # ADD THE FOLLOWING:
      ENV.append "CXXFLAGS", "-stdlib=libstdc++"
      ENV.append "CFLAGS", "-stdlib=libstdc++"
      ENV.append "LDFLAGS", "-stdlib=libstdc++ -lstdc++"
      # The following is necessary because libtool likes to strip LDFLAGS:
      ENV["CXX"] = "/usr/bin/clang++ -stdlib=libstdc++"
      ...

To edit the formulae in turn, run

for x in snappy leveldb protobuf gflags glog szip boost homebrew/science/opencv; do brew edit $x; done

After this, run

for x in snappy leveldb protobuf gflags glog szip homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
brew install --build-from-source --with-python --fresh -vd boost

Note that brew install --build-from-source --fresh -vd boost is fine if you do not need the Caffe Python wrapper.

Note that the HDF5 dependency is provided by Anaconda Python in this case. If you're not using Anaconda, include hdf5 in the list above.

Note that in order to build the caffe python wrappers you must install boost using the --with-python option:

brew install --build-from-source --with-python --fresh -vd boost

Windows

There is an unofficial Windows port of Caffe at niuzhiheng/caffe:windows. Thanks @niuzhiheng!

Compilation

Now that you have the prerequisites, edit your Makefile.config to change the paths for your setup. The defaults should work, but uncomment the relevant lines if using Anaconda Python.

cp Makefile.config.example Makefile.config
# Adjust Makefile.config (for example, if using Anaconda Python)
make all
make test
make runtest

Note that if there is no GPU in your machine, building and running CPU-only works, but GPU tests will naturally fail.

To compile the Python and MATLAB wrappers do make pycaffe and make matcaffe respectively. Be sure to set your MATLAB and Python paths in Makefile.config first! For Python support, you must add the compiled module to your $PYTHONPATH (as /path/to/caffe/python or the like).

Distribution: run make distribute to create a distribute directory with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.

Speed: for a faster build, compile in parallel by doing make all -j8 where 8 is the number of parallel threads for compilation (a good choice for the number of threads is the number of cores in your machine).

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

Hardware Questions

Laboratory Tested Hardware: Berkeley Vision runs Caffe with K40s, K20s, and Titans including models at ImageNet/ILSVRC scale. We also run on GTX series cards and GPU-equipped MacBook Pros. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.

CUDA compute capability: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Your mileage may vary.

Once installed, check your times against our reference performance numbers to make sure everything is configured properly.

Refer to the project's issue tracker for hardware/compatibility.