TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
About
TorchKM is an open-source library for kernel machines, including support vector machines, kernel logistic regression, and kernel quantile regression, with GPU acceleration. The library features a scikit-learn-style API and is designed to exploit GPU-friendly linear algebra, accelerating the full training and model-selection pipeline through intelligent reuse of matrix operations. Benchmarks show competitive predictive performance with substantial speedups over standard baselines. The efficiency and programmable design also make TorchKM a kernel-learning component for AI-driven workflows. Code and documentation are available at https://github.com/YikaiZhang95/torchkm, and the package can be easily installed via PyPI.
Yikai Zhang, Gaoxiang Jia, Jie Ding, Boxiang Wang• 2026
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Kernel Learning | Simulated Data (n=10000, p=10) | Objective Value0.47 | 3 | |
| Kernel Learning | Simulated Data (n=10000, p=1000) | Objective Value0.48 | 3 | |
| Kernel Learning | Simulated Data (n=20000, p=10) | Objective Value0.45 | 3 | |
| Kernel Learning | Simulated Data (n=20000, p=100) | Objective Value0.28 | 3 | |
| Kernel Learning | Simulated Data n=20000, p=1000 | Time (s)129.3 | 3 | |
| Kernel Learning | Simulated Data (n=10000, p=100) | Objective Value0.37 | 3 | |
| Classification | a9a LIBSVM (test) | Accuracy85.1 | 2 | |
| Classification | w8a LIBSVM (test) | Accuracy98.1 | 2 | |
| Classification | ijcnn1 LIBSVM (test) | Accuracy98.2 | 2 | |
| Classification | covtype LIBSVM (test) | Accuracy80.7 | 2 |
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