- add scikit-learn interface, see here
- add quiet mode (-q)
- pre-built binaries and DLL for Windows x64 on CPUs are avaliable
The mission of ThunderSVM is to help users easily and efficiently apply SVMs to solve problems. ThunderSVM exploits GPUs and multi-core CPUs to achieve high efficiency. Key features of ThunderSVM are as follows.
- Support all functionalities of LibSVM such as one-class SVMs, SVC, SVR and probabilistic SVMs.
- Use same command line options as LibSVM.
- Support Python, R and Matlab interfaces.
- Supported Operating Systems: Linux, Windows and MacOS.
Why accelerate SVMs: A survey conducted by Kaggle in 2017 shows that 26% of the data mining and machine learning practitioners are users of SVMs.
Documentations | Installation | API Reference (doxygen)
- cmake 2.8 or above
- gcc 4.8 or above for Linux and MacOS
- Visual C++ for Windows
If you want to use GPUs, you also need to install CUDA.
- CUDA 7.5 or above
git clone https://github.com/zeyiwen/thundersvm.git
Build on Linux (build instructions for MacOS and Windows)
cd thundersvm
mkdir build && cd build && cmake .. && make -j
# in thundersvm root directory
git submodule init eigen && git submodule update
mkdir build && cd build && cmake -DUSE_CUDA=OFF -DUSE_EIGEN=ON .. && make -j
If make -j
doesn't work, please simply use make
. The number of CPU cores to use can be specified by the -o
option (e.g., -o 10
), and refer to Parameters for more information.
./bin/thundersvm-train -c 100 -g 0.5 ../dataset/test_dataset.txt
./bin/thundersvm-predict ../dataset/test_dataset.txt test_dataset.txt.model test_dataset.predict
You will see Accuracy = 0.98
after successful running.
If you use ThunderSVM in your paper, please cite our work (preprint now available).
@article{wenthundersvm18,
author = {Wen, Zeyi and Shi, Jiashuai and He, Bingsheng and Li, Qinbin and Chen, Jian},
title = {{ThunderSVM}: A Fast {SVM} Library on {GPUs} and {CPUs}},
journal = {To appear in arxiv},
year = {2018}
}
- We acknowledge NVIDIA for their hardware donations.
- This project is hosted by NUS, collaborating with Prof. Jian Chen (South China University of Technology). Initial work of this project was done when Zeyi Wen worked at The University of Melbourne.
- This work is partially supported by a MoE AcRF Tier 1 grant (T1 251RES1610) in Singapore.
- We also thank the authors of LibSVM and OHD-SVM which inspire our algorithmic design.