Tensors and Dynamic neural networks in Python with strong GPU acceleration
Find a file
2017-01-04 10:45:20 -08:00
.travis Added alternative numpy installation option 2016-12-29 17:00:50 -05:00
caffe/proto Install fixes 2016-12-21 09:14:04 -05:00
caffe2 CMakeLists for db, queue, sgd 2017-01-04 10:45:20 -08:00
cmake USE_OPENMP option added 2017-01-04 12:44:47 -05:00
docs added documentation 2016-12-28 14:48:51 -05:00
third_party added google/benchmark and tidied up Cuda build 2016-12-27 08:49:41 -08:00
.Doxyfile Adding optional Eigen code. Added a switch USE_SYSTEM_EIGEN in Env. Misc changes. 2015-10-18 16:55:24 -07:00
.gitignore Adding optional Eigen code. Added a switch USE_SYSTEM_EIGEN in Env. Misc changes. 2015-10-18 16:55:24 -07:00
.gitmodules added google/benchmark and tidied up Cuda build 2016-12-27 08:49:41 -08:00
.travis.yml Attempt to get numpy working with travis 2016-12-29 17:29:41 -05:00
build.py Gpu transform 2017-01-03 17:59:34 -08:00
build_android.py fbsync 2016-10-07 15:47:52 -07:00
build_android_prepare.py fbsync 2016-10-07 15:47:52 -07:00
CMakeLists.txt USE_OPENMP option added 2017-01-04 12:44:47 -05:00
Makefile more build updates: 2016-08-02 23:28:23 -07:00
README.md Update README.md 2017-01-03 10:48:57 -08:00

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

Build Status

git clone https://github.com/bwasti/caffe2.git
cd caffe2

OS X

brew install openblas glog gtest automake protobuf leveled lmdb
mkdir build && cd build
cmake .. -DBLAS=OpenBLAS -DUSE_OPENCV=off
make

Ubuntu

sudo apt-get install libprotobuf-dev protobuf-compiler libatlas-base-dev libgoogle-glog-dev libgtest-dev liblmdb-dev libleveldb-dev libsnappy-dev python-dev python-pip libiomp-dev libopencv-dev libpthread-stubs0-dev cmake
sudo pip install numpy
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_8.0.44-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1404_8.0.44-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda
sudo apt-get install git

CUDNN_URL="http://developer.download.nvidia.com/compute/redist/cudnn/v5.1/cudnn-8.0-linux-x64-v5.1.tgz" &&
curl -fsSL ${CUDNN_URL} -O &&
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

mkdir build && cd build
cmake ..
make

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

Build status (known working)

Ubuntu 14.04 (GCC)

  • Default CPU build
  • Default GPU build

OS X (Clang)

  • Default CPU build
  • Default GPU build

Options (both Clang and GCC)

  • Nervana GPU
  • ZMQ
  • RocksDB
  • MPI
  • OpenMP
  • No LMDB
  • No LevelDB
  • No OpenCV

BLAS

  • OpenBLAS
  • ATLAS
  • MKL

Other

  • CMake 2.8 support
  • List of dependencies for Ubuntu 14.04
  • List of dependencies for OS X