| commit | 91f468b15c87f298d59486d1841cad9db32058d7 | [log] [tgz] |
|---|---|---|
| author | Aapo Kyrola <akyrola@fb.com> | Tue Mar 14 15:43:42 2017 -0700 |
| committer | Facebook Github Bot <facebook-github-bot@users.noreply.github.com> | Tue Mar 14 15:48:07 2017 -0700 |
| tree | 0f3ada8b765f86d0ed9f47207f5aa9bb4ac67f2b | |
| parent | 95ecf22c0cb497139bb0666e737ac9d5acf0e1f6 [diff] |
fixes to make data parallel model work for RecurrentNet + test case Summary: First, this diff includes a full test of data-parallel LSTM, which confirms it works correctly. To make it work, some changes had to be made: - cell net/step net external inputs must be namespace scoped - prevent double-namescoping of cellnet inputs - make data parallel model understand recurrentnets so the device-mapping works Reviewed By: salexspb Differential Revision: D4708840 fbshipit-source-id: 4b0ddc43642d449076a2b6f67ad1c47f84138ff4
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.
Caffe2 is released under the BSD 2-Clause license.
Detailed build matrix (hit refresh if you see icons not showing up due to heroku):
| Target | Status |
|---|---|
| Linux | |
| Mac (CPU) | |
| Android | |
| iOS | |
| Linux + MKL | |
| Windows |
git clone --recursive https://github.com/caffe2/caffe2.git cd caffe2
brew install automake protobuf mkdir build && cd build cmake .. make
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
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.
For Raspbian, run scripts/build_raspbian.sh on the Raspberry Pi.
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.
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
Ubuntu 14.04 (GCC)
OS X (Clang)
Options (both Clang and GCC)
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