commit	2043b3c1147bfc17b4427faf7c31662508d5cbd5	[log] [tgz]
author	Yiming Wu <wyiming@fb.com>	Mon Apr 17 14:58:37 2017 -0700
committer	Facebook Github Bot <facebook-github-bot@users.noreply.github.com>	Mon Apr 17 15:03:05 2017 -0700
tree	caf1a8c494ead90f95829154ec9bd5139271b8a5
parent	277b4eca9737af940a077e5033dffd6039783ee3 [diff]

tree: caf1a8c494ead90f95829154ec9bd5139271b8a5

README.md

Caffe2

Caffe2 is a deep learning framework made with expression, speed, and modularity in mind. It is an experimental refactoring of Caffe, and allows a more flexible way to organize computation.

License and Citation

Caffe2 is released under the BSD 2-Clause license.

Building Caffe2

Detailed build matrix (hit refresh if you see icons not showing up due to heroku):

Target	Status
Linux
Android
iOS
Linux + MKL

git clone --recursive https://github.com/caffe2/caffe2.git
cd caffe2

OS X

brew install automake protobuf
mkdir build && cd build
cmake ..
make

Ubuntu

This build is confirmed for:

Ubuntu 14.04
Ubuntu 16.06

Required Dependencies

sudo apt-get update
sudo apt-get install -y --no-install-recommends \
      build-essential \
      cmake \
      git \
      libgoogle-glog-dev \
      libprotobuf-dev \
      protobuf-compiler \
      python-dev \
      python-pip                          
sudo pip install numpy protobuf

Optional GPU Support

If you plan to use GPU instead of CPU only, then you should install NVIDIA CUDA and cuDNN, a GPU-accelerated library of primitives for deep neural networks. NVIDIA's detailed instructions or if you're feeling lucky try the quick install set of commands below.

Update your graphics card drivers first! Otherwise you may suffer from a wide range of difficult to diagnose errors.

For Ubuntu 14.04

sudo apt-get update && sudo apt-get install wget -y --no-install-recommends
wget "http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_8.0.61-1_amd64.deb"
sudo dpkg -i cuda-repo-ubuntu1404_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda

For Ubuntu 16.04

sudo apt-get update && sudo apt-get install wget -y --no-install-recommends
wget "http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda

Install cuDNN (all Ubuntu versions)

CUDNN_URL="http://developer.download.nvidia.com/compute/redist/cudnn/v5.1/cudnn-8.0-linux-x64-v5.1.tgz"
wget ${CUDNN_URL}
sudo tar -xzf cudnn-8.0-linux-x64-v5.1.tgz -C /usr/local
rm cudnn-8.0-linux-x64-v5.1.tgz && sudo ldconfig

Optional Dependencies

Note libgflags2 is for Ubuntu 14.04. libgflags-dev is for Ubuntu 16.04.

# for Ubuntu 14.04
sudo apt-get install -y --no-install-recommends libgflags2

# for Ubuntu 16.04
sudo apt-get install -y --no-install-recommends libgflags-dev

# for both Ubuntu 14.04 and 16.04
sudo apt-get install -y --no-install-recommends \
      libgtest-dev \
      libiomp-dev \
      libleveldb-dev \
      liblmdb-dev \
      libopencv-dev \
      libopenmpi-dev \
      libsnappy-dev \
      openmpi-bin \
      openmpi-doc \
      python-pydot

Check the Python section below and install optional packages before you build.

mkdir build && cd build
cmake ..
make

Android and iOS

We use CMake's Android and iOS ports to build native binaries that you can then integrate into your Android or XCode projects. See scripts/build_android.sh and scripts/build_ios.sh for more details.

For Android, one can also use gradle to build Caffe2 directly with Android Studio. An example project can be found here. Note that you may need to configure Android Studio so that it has the right SDK and NDK versions to build the code.

Raspberry Pi

For Raspbian, run scripts/build_raspbian.sh on the Raspberry Pi.

Tegra X1

To install Caffe2 on NVidia's Tegra X1 platform, simply install the latest system with the NVidia JetPack installer, and then run scripts/build_tegra_x1.sh on the Tegra device.

Python support

To run the tutorials you'll need ipython-notebooks and matplotlib, which can be installed on OS X with:

brew install matplotlib --with-python3
pip install ipython notebook

You may also find these required for specific tutorials and examples, so you can run this to get all of the prerequisites at once:

sudo pip install \
      flask \
      graphviz \
      hypothesis \
      jupyter \
      matplotlib \
      pydot python-nvd3 \
      pyyaml \
      requests \
      scikit-image \
      scipy \
      setuptools \
      tornado

Build status (known working)

Ubuntu 14.04 (GCC)

[x] Default CPU build
[x] Default GPU build

OS X (Clang)

[x] Default CPU build
[x] Default GPU build

Options (both Clang and GCC)

[x] Nervana GPU
[ ] ZMQ
[x] RocksDB
[x] MPI
[x] OpenMP
[x] No LMDB
[x] No LevelDB
[x] No OpenCV

BLAS

[x] OpenBLAS
[x] ATLAS
[ ] MKL

Other

[x] CMake 2.8 support
[x] List of dependencies for Ubuntu 14.04
[x] List of dependencies for Ubuntu 16.04
[x] List of dependencies for OS X