Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors.
Check out the project site for all the details like
- DIY Deep Learning for Vision with Caffe
- Tutorial Documentation
- BVLC reference models and the community model zoo
- Installation instructions
and step-by-step examples.
Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues.
Happy brewing!
This fork is dedicated to improving Caffe performance when running on CPU, in particular Intel® Xeon processors (HSW+)
Build procedure is the same as on bvlc-caffe-master branch. Both Make and CMake can be used. When OpenMP is available will be used automatically.
Run procedure is the same as on bvlc-caffe-master branch.
Current implementation uses OpenMP threads. By default the number of OpenMP threads is set to the number of CPU cores. Each one thread is bound to a single core to achieve best performance results. It is however possible to use own configuration by providing right one through OpenMP environmental variables like OMP_NUM_THREADS or GOMP_CPU_AFFINITY.
If some system tool like numactl is used to control CPU affinity, by default caffe will prevent to use more than one thread per core. When less than required cores are specified, caffe will limit execution of OpenMP threads to specified cores only.
Intel Caffe multinode allows you to execute deep neural network training on multiple machines.
You should read our Wiki to understand how it works. For quick start read Multinode quickstart guide, next Multinode How to ...?
Please see also prepared examples for cifar10 and Googlenet.
For cifar10 example look at examples/cifar10/train_full_multinode.sh file. The script will run data server, synchronous parameter server and 4 clients. Prepared proto solvers should result in exactly the same behavior as single node full cifar training.
It use the MPI setup with explicit all reduce. This will run 5 processes on hosts set with host, and each process will calculate it's own gradients, and propagate it up with a tree structure to the root, which will apply them and propagate parameters down also in a tree structure.
Data server is for convenience. By the default you could use data shard prepared on each node separetely, either by shuffling the data uniquely or by creating a subset of your training data. The remote data layer can be used to get data from data server. It can also be used to cache data from the server in order to reduce the network traffic. Use only TCP protocol with data server. In the case of choosing caching policy USE_CACHE_WHEN_FULL, it will first download cache_size batches and then will randomized the cached data for actual training.
For Googlenet example look at models/bvlc_googlenet/solver_client.prototxt. The solver tries to offset the bigger batch size with bigger learning rate. According to paper:
@article{
Author = {Forrest N. Iandola, Khalid Ashraf, Matthew W. Moskewicz, Kurt Keutzer},
Journal = {arXiv preprint arXiv:1511.00175},
Title = {FireCaffe: near-linear acceleration of deep neural network training on compute clusters},
Year = {2016}
}
this should use 72 epochs to train Googlenet.
Caffe is released under the BSD 2-Clause license. The BVLC reference models are released for unrestricted use.
Please cite Caffe in your publications if it helps your research:
@article{jia2014caffe,
Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
Journal = {arXiv preprint arXiv:1408.5093},
Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
Year = {2014}
}