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Caffe: a fast open framework for deep learning. Caffe-pslite: run deep learning in a cluster with ps-lite (including SSP model)

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Caffe

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Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

Check out the project site for all the details like

and step-by-step examples.

Custom distributions

Distributed train via using Caffe

We run distributed train in cluster, including the heterogeneous environment setting.

The 3rdparty lib: ps-lite with ssp support

We also give a demo in example/ps. (test_ssp.cc, using 'make' to generate the binary file)

  • ./examples/mnist/local.sh 2 10 examples/ps/test_ssp build/tools/caffe

To use this version of Caffe (SSP model), please acknowledge our contribution or cite this github url, many thanks.

Community

Join the chat at https://gitter.im/BVLC/caffe

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!

License and Citation

Caffe is released under the BSD 2-Clause license. The BAIR/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}
}

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Caffe: a fast open framework for deep learning. Caffe-pslite: run deep learning in a cluster with ps-lite (including SSP model)

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  • C++ 80.0%
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