This was experimental branch of Caffe for OpenCL, we know recommend you use the now official OpenCL port of Caffe in BVLC GitHub Repo at https://github.com/BVLC/caffe/tree/opencl
This is an OpenCL implementation of Caffe, a mainstream DNN framework (https://github.com/BVLC/caffe). It includes a largely complete Caffe feature set as of August 2015. The project is under active development to improve performance and add new features. Contributions from the community are welcome.
OpenCL (https://en.wikipedia.org/wiki/OpenCL) is an open standard parallel programming language for heterogeneous platforms. OpenCL is supported by a variety of commercial chip manufacturers.
We have three branches in this repo.
-stable, the stable branch for users
-dev, the developer branch, we encourage people to contribute on this branch
-master, the original Caffe's master branch against which our code is synchronized.
-All Caffe layers ported to OpenCL
-Performance improvement by batched implementation for conv layer based on clBLAS
-The user can choose the optimal batch number depending on H/W properties, image size and minibatch size
-Supports OpenCL 2.0, 1.2
-Implemented in C++ and OpenCL, maintaining the same interfaces as the original Caffe
-Users can directly run DNN models: AlexNet, VGG-16 and VGG-19
Note: More features are planned in the near future. Currently this implementation has been verified and tuned on AMD devices (CPUs/GPUs/APUs). Compatibility across different chip manufacturers will be considered for future addition.
We intend to keep updating the latest performance as we make optimizations. Fury results are preliminary and are actively being improved.
- Training speed (Model: AlexNet, minibatch size 128)
| Platform | Speed (images per second) |
|---|---|
| AMD W9100 & A10-7850k | 255 |
| AMD R9 Fury & A10-7850k | 261 |
| AMD R290X @1000MHz & A10-7850k | 268 |
| AMD S9150 @900MHz & Xeon E5-2640 | 227 |
- Recognition speed (Model: AlexNet, minibatch size 128)
| Platform | Speed (images per second) |
|---|---|
| AMD W9100 & A10-7850k | 590 |
| AMD R9 Fury & A10-7850k | 699 |
| AMD R290X @1000MHz & A10-7850k | 606 |
| AMD S9150 @900MHz & Xeon E5-2640 | 452 |
For more information on how to install, use or contribute to this code base, please visit our wiki page: https://github.com/amd/OpenCL-caffe/wiki
Junli Gu, Yibing Liu, Yuan Gao, Maohua Zhu
We thank Mauricio Breternitz, Hanjin Chu and Greg Stoner for their technical suggestions and support.
If you have any questions, please send an email to [email protected]
As an open source project, we hope to maintain an open dynamics and sharing culture. We encourage the contribution and support from the community to improve it together.
The original Caffe is provided in the BSD 2-Clause license open source license. The OpenCL ports written by AMD is covered by AMD license. We encourage the contribution and support from external, your contribution will be covered either by BSD 2-Clause license or whichever your preferred license.
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!
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}
}