| commit | ec51f887bf86d64afbd2fa0d4967c3e4d06026bd | [log] [tgz] |
|---|---|---|
| author | Andrey Malevich <amalevich@fb.com> | Sun Jan 22 19:18:11 2017 -0800 |
| committer | Facebook Github Bot <facebook-github-bot@users.noreply.github.com> | Sun Jan 22 19:29:16 2017 -0800 |
| tree | 6d8a7a2e9d92d3add14e1b18b3640c8891504c18 | |
| parent | be1224c0a774c32fd58eba0da5e08b7f33cba14b [diff] |
Create only one instance of SigridTransform in DPerExample. Summary: DPer example have been creating multiple copies of the transform config in net defition till this moment, that resulted in the fact that I've hit the limit of ProtoBuf (64MB) for a certain Task requests (especially visible because of the ValidationPipeline that I was adding). After this diff we're going to store SigridTransforms in one instance per machine for training (or 1 instance per reading). Difference in sizes of the plans for some simple SparseNN model ~30 MB (even including the fact that second model have validation plan as well). TODO: Do similar logic for NNPreProc as well (it's also pretty large). Reviewed By: dzhulgakov Differential Revision: D4441441 fbshipit-source-id: 4452dd86a4dc49b2c7f5b7642f443aed5720b047
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.
git clone --recursive https://github.com/bwasti/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
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
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