commit | 85fad20a5a8490932b3a9bc2c9a012a50cfb3b89 | [log] [tgz] |
---|---|---|
author | Viswanath Sivakumar <viswanath@fb.com> | Mon Mar 13 21:45:01 2017 -0700 |
committer | Facebook Github Bot <facebook-github-bot@users.noreply.github.com> | Mon Mar 13 21:47:56 2017 -0700 |
tree | e9bb826b08a0d9c0c7424ad25faa5e54e8907e28 | |
parent | 84ba7c1acbb748f1e12bc1af920a3825cf088767 [diff] |
Gracefully handle empty input to Dropout Summary: Context: https://fb.facebook.com/groups/1405155842844877/permalink/1677762748917517/. DropoutOp and DropoutGradientOp already handle input of size 0 gracefully. The CHECK isn't needed. I think this should fix the crash in xray detection models where num region proposals are zero. Differential Revision: D4697254 fbshipit-source-id: afd06975f2ad4b2e59f15d12b0aa332f6eb3f1af
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