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NVIDIA has discontinued support and maintenance for this repository. Everything is provided as-is with no further updates being accepted. Thanks for all the contributions and engagement!

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.
  • Layer-wise Adaptive Rate Control (LARC) and adaptive global gradient scaler for better accuracy, especially in 16-bit training.
  • Integration with cuDNN v8.
  • Automatic selection of the best cuDNN convolution algorithm.
  • Integration with v2.2 (or higher) 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, transformer and image reader 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 and higher.
  • Extended set of optimized models (including 16 bit floating point examples).
  • Experimental feature (no official support) Multi-node training (since v0.17.1, NCCL 2.2 and OpenMPI 2 required).
  • Experimental feature (no official support) TRTLayer (since v0.17.1, can be used as inference plugin).

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}
}