pySigLib - Fast Signature Kernels on CPU and GPU

Signature-based methods have recently gained significant traction in machine learning for sequential data. In particular, signature kernels have emerged as powerful discriminators and training losses for generative models on time-series, notably in quantitative finance. However, existing implementations do not scale to the dataset sizes and sequence lengths encountered in practice. We present pySigLib, a high-performance library offering optimised implementations of signatures and signature kernels on CPU and GPU, fully compatible with PyTorch’s automatic differentiation. Beyond an efficient software stack for large-scale signature-kernel computation, we introduce a novel differentiation scheme that delivers accurate gradients at a fraction of the runtime of existing libraries. We believe our library unlocks the practical use of signature kernels for generative modelling and the distillation of next-generation sequence models.