aiqbench analysis
TorchKM introduces a GPU-accelerated library for kernel machines that leverages efficient matrix operations to significantly speed up training and model selection pipelines. By offering a familiar scikit-learn API, it lowers the barrier for integrating advanced kernel methods like SVMs and logistic regression into high-performance workflows. The project demonstrates that substantial speedups over standard baselines are achievable without sacrificing competitive predictive performance.
TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
arXiv:2606.06742v1 Announce Type: new Abstract: 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 together with substantial speedups over standard baselines. Code and documentation are available at https://github.com/YikaiZhang95/torchkm, and the package can be easily installed via PyPI.
Abstract: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 together with substantial speedups over standard baselines. Code and documentation are available at this https URL, and the package can be easily installed via PyPI.
Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:2606.06742 [cs.LG]
(or arXiv:2606.06742v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.06742 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 176 words