https://github.com/newplan/nvcaffe-dlbooster
special_designed_for_nvcaffe
https://github.com/newplan/nvcaffe-dlbooster
Last synced: about 1 year ago
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special_designed_for_nvcaffe
- Host: GitHub
- URL: https://github.com/newplan/nvcaffe-dlbooster
- Owner: NEWPLAN
- License: other
- Created: 2018-07-02T01:22:55.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2022-10-16T22:50:26.000Z (over 3 years ago)
- Last Synced: 2025-02-12T09:34:53.237Z (over 1 year ago)
- Language: C++
- Size: 12.3 MB
- Stars: 0
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# 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](http://bvlc.eecs.berkeley.edu))
and community contributors.
# NVCaffe
NVIDIA Caffe ([NVIDIA Corporation ©2017](http://nvidia.com)) 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](https://developer.nvidia.com/cudnn) v7**.
* **Automatic selection of the best cuDNN convolution algorithm**.
* **Integration with v2.2 of [NCCL library](https://github.com/NVIDIA/nccl)**
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).
## License and Citation
Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/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}
}
## Useful notes
Libturbojpeg library is used since 0.16.5. It has a packaging bug. Please execute the following (required for Makefile, optional for CMake):
```
sudo apt-get install libturbojpeg
sudo ln -s /usr/lib/x86_64-linux-gnu/libturbojpeg.so.0.1.0 /usr/lib/x86_64-linux-gnu/libturbojpeg.so
```