pysiglib 4.0.0rc2

The high-performance toolkit for rough path computation

Documentation | Installation | API reference | Paper

pySigLib brings path signatures, log-signatures, branched signatures, and signature kernels into one accelerated toolkit. It provides NumPy, PyTorch, and JAX support, with automatic differentiation for PyTorch and JAX and multithreaded C++ or native CUDA execution.

Installation

pip install pysiglib

# Add CUDA support
pip install "pysiglib[cuda]"

The JAX integration is included in the wheel. Install JAX separately with pip install jax if you want to use it. For source builds and platform-specific guidance, see the installation guide.

Quick start

import numpy as np
import pysiglib

path = np.random.default_rng().normal(size=(32, 1000, 10))
signature = pysiglib.sig(path, degree=5)

Paths have shape (path length, dimension) or (batch size, path length, dimension). Computation runs on the device where the input already lives.

Why pySigLib?

• A unified toolkit for rough path computations - signatures, log-signatures, branched signatures, and signature kernels.
• Accelerated CPU and CUDA implementations for large workloads.
• Native NumPy, PyTorch, and JAX support without moving data between frameworks.
• Automatic differentiation with PyTorch and JAX, including jit and vmap support in JAX.
• Cross-platform - Windows, Linux and Mac systems supported.

Capabilities

Framework integrations

PyTorch autograd

Signatures compose directly with the rest of a PyTorch model:

import torch
from pysiglib.torch_api import sig

path = torch.randn(32, 1000, 10, device="cuda", requires_grad=True)
sig(path, degree=5).sum().backward()

JAX transforms

The JAX API supports jit, vmap, and grad:

import jax
import jax.numpy as jnp
from pysiglib.jax_api import sig

@jax.jit
def signature_norm(path):
    return jnp.sum(sig(path, degree=5) ** 2)

path = jax.random.normal(jax.random.key(0), (1000, 10))
gradient = jax.grad(signature_norm)(path)

See the documentation for complete examples and the full API reference.

Citation

If the library supports your research, please consider citing the paper:

@article{shmelev2025pysiglib,
  title={pySigLib -- Fast Signature-Based Computations on CPU and GPU},
  author={Shmelev, Daniil and Salvi, Cristopher},
  journal={arXiv preprint arXiv:2509.10613},
  year={2025}
}

Contributing

Contributions are welcome. Please open an issue first to discuss a change, then submit a pull request.

Sponsors

If you'd like to support development, please consider sponsoring the project.