7b8c7b11d2
Summary: After this, we should have contbuild guarding the Windows build both with and without CUDA. This includes a series of changes that are needed to make Windows build, specifically: (1) Various flags that are needed in the cmake system, specially dealing with /MD, /MT, cuda, cudnn, whole static linking, etc. (2) Contbuild scripts based on appveyo. (3) For Windows build, note that one will need to use "cmake --build" to build stuff so that the build type is consistent between configuration and actual build. see scripts\build_windows.bat for details. (4) In logging.h, ERROR is already defined by Windows. I don't have a good solution now, and as a result, LOG(ERROR) on windows is going to be LOG(INFO). (5) variable length array is not supported by MSVC (and it is not part of C++ standard). As a result I replaced them with vectors. (6) sched.h is not available on Windows, so akyrola 's awesome simple async net might encounter some slowdown due to no affinity setting on Windows. (7) MSVC has a Closes https://github.com/caffe2/caffe2/pull/183 Reviewed By: ajtulloch Differential Revision: D4657831 Pulled By: Yangqing fbshipit-source-id: 070ded372ed78a7e3e3919fdffa1d337640f146e
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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
git clone --recursive https://github.com/caffe2/caffe2.git
cd caffe2
OS X
brew install automake protobuf
mkdir build && cd build
cmake ..
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 python-protobuf
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
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
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