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.

翻译：TorchKM是一款面向核机器的开源库，涵盖支持向量机、核逻辑回归及核分位数回归，并集成GPU加速。该库采用scikit-learn风格API，设计上充分利用GPU友好的线性代数运算，通过矩阵运算的智能复用加速完整训练与模型选择流程。基准测试表明，该方案在保持竞争性预测性能的同时，实现了相较于标准基线的大幅速度提升。其高效性与可编程设计使TorchKM成为AI驱动工作流中核学习组件的理想选择。代码与文档详见https://github.com/YikaiZhang95/torchkm，并可通过PyPI便捷安装。