| commit | c907c7c7dc8c98bd6059f27fa8064c9b46665589 | [log] [tgz] |
|---|---|---|
| author | Pieter Noordhuis <pietern@fb.com> | Wed Apr 12 13:59:34 2017 -0700 |
| committer | Facebook Github Bot <facebook-github-bot@users.noreply.github.com> | Wed Apr 12 14:03:51 2017 -0700 |
| tree | 7a9207bf7984167ccedeb2ca13deaa2303f0f764 | |
| parent | f5ac83b060bc8f6ece26d015b47a3e9844047406 [diff] |
Update resnet50_trainer example Summary: A few fixes in this commit: the epoch size is now rounded down to the closest integer multiple of the global batch size (batch per GPU * GPUs per hosts * hosts per run). The num_shards and shard_id parameters are now passed to CreateDB so multiple processes actually train on different subsets of data. The LR step size is scaled by the number of hosts in the run. The test accuracy is only determined after each epoch instead of after every so many iterations. Differential Revision: D4871505 fbshipit-source-id: d2703dc7cf1e0f76710d9d7c09cd362a42fe0598
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
This build is confirmed for:
sudo apt-get update sudo apt-get install -y --no-install-recommends \ build-essential \ cmake \ git \ libgoogle-glog-dev \ libprotobuf-dev \ protobuf-compiler \ python-dev \ python-pip sudo pip install numpy protobuf
If you plan to use GPU instead of CPU only, then you should install NVIDIA CUDA and cuDNN, a GPU-accelerated library of primitives for deep neural networks. NVIDIA's detailed instructions or if you're feeling lucky try the quick install set of commands below.
Update your graphics card drivers first! Otherwise you may suffer from a wide range of difficult to diagnose errors.
For Ubuntu 14.04
sudo apt-get update && sudo apt-get install wget -y --no-install-recommends wget "http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_8.0.61-1_amd64.deb" sudo dpkg -i cuda-repo-ubuntu1404_8.0.61-1_amd64.deb sudo apt-get update sudo apt-get install cuda
For Ubuntu 16.04
sudo apt-get update && sudo apt-get install wget -y --no-install-recommends wget "http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb" sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb sudo apt-get update sudo apt-get install cuda
CUDNN_URL="http://developer.download.nvidia.com/compute/redist/cudnn/v5.1/cudnn-8.0-linux-x64-v5.1.tgz"
wget ${CUDNN_URL}
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
Note
libgflags2is for Ubuntu 14.04.libgflags-devis for Ubuntu 16.04.
# for Ubuntu 14.04 sudo apt-get install -y --no-install-recommends libgflags2
# for Ubuntu 16.04 sudo apt-get install -y --no-install-recommends libgflags-dev
# for both Ubuntu 14.04 and 16.04 sudo apt-get install -y --no-install-recommends \ libgtest-dev \ libiomp-dev \ libleveldb-dev \ liblmdb-dev \ libopencv-dev \ libopenmpi-dev \ libsnappy-dev \ openmpi-bin \ openmpi-doc \ python-pydot
Check the Python section below and install optional packages before you build.
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
You may also find these required for specific tutorials and examples, so you can run this to get all of the prerequisites at once:
sudo pip install \
flask \
graphviz \
hypothesis \
jupyter \
matplotlib \
pydot python-nvd3 \
pyyaml \
requests \
scikit-image \
scipy \
setuptools \
tornado
Ubuntu 14.04 (GCC)
OS X (Clang)
Options (both Clang and GCC)
BLAS
Other