commit | 0c47d345dfe4a8fd4dd52e124a87240f73ed0ef6 | [log] [tgz] |
---|---|---|
author | Yury Zemlyanskiy <urikz@fb.com> | Mon Mar 27 17:18:09 2017 -0700 |
committer | Facebook Github Bot <facebook-github-bot@users.noreply.github.com> | Mon Mar 27 17:32:39 2017 -0700 |
tree | 7c44aa4384001f6c1613bf1819ab697b1cff2fcf | |
parent | db9cae4d343ee9f8f24e1876b18b9b3909438e60 [diff] |
Multi-gpu training for OSS seq2seq Summary: Use data_parallel_model for seq2seq multi-gpu training. The main reason for complexity here is that GatherOp hasn't yet been implemented on GPU. This diff also adds better cliping procedure - clip by global norm rather than by absolute value. Differential Revision: D4778691 fbshipit-source-id: bff184dae02ecc227413fef51f48a4726e5d3825
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 |
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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)
BLAS
Other