Caffe
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.
NVCaffe
NVIDIA Caffe (NVIDIA Corporation ©2017) is an NVIDIA-maintained fork of BVLC Caffe tuned for NVIDIA GPUs, particularly in multi-GPU configurations. Here are the major features:
- 16 bit (half) floating point train and inference support.
- Mixed-precision support. It allows to store and/or compute data in either 64, 32 or 16 bit formats. Precision can be defined for every layer (forward and backward passes might be different too), or it can be set for the whole Net.
- Integration with cuDNN v6.
- Automatic selection of the best cuDNN convolution algorithm.
- Integration with v1.3.4 of NCCL library for improved multi-GPU scaling.
- Optimized GPU memory management for data and parameters storage, I/O buffers and workspace for convolutional layers.
- Parallel data parser and transformer for improved I/O performance.
- Parallel back propagation and gradient reduction on multi-GPU systems.
- Fast solvers implementation with fused CUDA kernels for weights and history update.
- Multi-GPU test phase for even memory load across multiple GPUs.
- Backward compatibility with BVLC Caffe and NVCaffe 0.15.
- Extended set of optimized models (including 16 bit floating point examples).
License and Citation
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}
}