| commit | dcefc74a0c5ab74e6464c348cc6b48ee6846892f | [log] [tgz] |
|---|---|---|
| author | Aapo Kyrola <akyrola@fb.com> | Thu Feb 02 22:26:31 2017 -0800 |
| committer | Facebook Github Bot <facebook-github-bot@users.noreply.github.com> | Thu Feb 02 22:29:22 2017 -0800 |
| tree | 848f5739d0570cd2ec50c1b784d412d4f88d9f4c | |
| parent | 5837b21691b883e80420c2e29065ab85a0ad0ac5 [diff] |
Shape and Type Inference Part1 Summary: This is a bit large diff, sorry about it. It includes basic shape and type inference functionality, based on YQ's Schema scaffolding. I added some helper functions to make it easier to write simple translations. Bigger refactoring was needed for ConvPoolBase so that we could use the shape inference already there in the schema. I annotated enough operators to be able to infer forward-pass of shapes for basic convnet, and added test for that. I intend to bootcamp some annotations and annotate enough to handle Resnets fully. Need to think about gradients, if they could be annotated in an easier way. Only shapes are now exposed to Python, types will follow later. Also the inference is not called yet anywhere but unit test. Also I am not sure if everything is in the best location in the code, but shouldn't be hard to move stuff around. Reviewed By: dzhulgakov Differential Revision: D4436818 fbshipit-source-id: eebee5937ccc9ac09c245465302388a1fae6933c
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