smolgp-logo
smolgp
State Space Models for O(Linear/Log) Gaussian Processes

docs Tests codecov Journal arXiv DOI

smolgp is a standalone extension of the tinygp package that implements scalable & GPU-parallelizable Gaussian Processes in JAX using the state space representation. It is particularly suited for integrated measurements (such as long exposures in astronomy), jointly modeling data from multiple instruments, and for scalable implementations of popular kernels that traditionally lack quasiseparable structure (e.g. the quasiperiodic kernel).

The smolgp API is designed to be as similar to tinygp as possible. In almost all cases, you can simply find-and-replace "smol" with "tiny" in your existing code.

Main features

  1. A Kalman filter and RTS smoother compatible with tinygp-like GP kernels.
  2. Scalable (O(N)) solving with integrated (and possibly overlapping) measurements from multiple instruments (see Rubenzahl and Hattori et al. 2026).
  3. Parallelized versions of 1 (see Särkkä and García-Fernández 2020) and 2 (see Rubenzahl and Hattori et al. 2026).
  4. Approximations of popular GP kernels that lack quasiseparability (e.g., ExpSineSquared, Quasiperiodic) that can utilize the O(N) state space solvers.
  5. A convenient and optimally-efficient model-building framework to assemble multicomponent GPs and compute per-component distributions.

Check out the docs for more information, including tutorials: https://smolgp.readthedocs.io/

Please raise issues here and/or reach out to Ryan Rubenzahl and/or So Hattori.

Installation

You can install the most recent release from PyPI, e.g. with uv:

uv add smolgp

Or, you can simply clone this repository and install locally:

git clone https://github.com/smolgp-dev/smolgp.git
cd smolgp
uv pip install -e .

Citation

DOI Journal arXiv

If you use smolgp in your research, please cite the relevant software release and published paper. The cffconvert tool can be used to generate a bibtex entry from the included CITATION.cff (or just use the "cite this repository" button on the GitHub sidebar).

Author & Contact

GitHub followers GitHub followers

This repo is maintained by Ryan Rubenzahl and So Hattori.

Project links

Data verified by PyPI on Aug 10, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.3.2)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for rrubenzahl from gravatar.com rrubenzahl

Credits

Author: Ryan Rubenzahl

GitHub Statistics

Data verified by PyPI on Aug 10, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.11

Provides Extra

cuda12 cuda13

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

smolgp-0.1.7.tar.gz (3.7 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

smolgp-0.1.7-py3-none-any.whl (67.8 kB view details)

Uploaded Python 3

File details

Details for the file smolgp-0.1.7.tar.gz.

File metadata

  • Download URL: smolgp-0.1.7.tar.gz
  • Upload date:
  • Size: 3.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for smolgp-0.1.7.tar.gz
Algorithm Hash digest
SHA256 73eb14bc6408779e33048df0151445965a597636a990da19cbb81af493213745
MD5 a0ef1c8a10177a5125be6a7b55763503
BLAKE2b-256 2f349860711477fa14d0ee12b97ad26310384153ab71a3c16cfe22a2b58765e9

See more details on using hashes here.

Provenance

The following attestation bundles were made for smolgp-0.1.7.tar.gz:

Publisher: pypi-publish.yml on smolgp-dev/smolgp

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file smolgp-0.1.7-py3-none-any.whl.

File metadata

  • Download URL: smolgp-0.1.7-py3-none-any.whl
  • Upload date:
  • Size: 67.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for smolgp-0.1.7-py3-none-any.whl
Algorithm Hash digest
SHA256 5123ef18240ea53f77adc3d5ac6c3da330004526c28d0f5f18b6e4dc6f66424f
MD5 8716a78837edccea48ee7af8a3ffa2e4
BLAKE2b-256 bb8a7299950631a1a8db7f7d3885206bd63156b690eecb64edcb3710d82c06a5

See more details on using hashes here.

Provenance

The following attestation bundles were made for smolgp-0.1.7-py3-none-any.whl:

Publisher: pypi-publish.yml on smolgp-dev/smolgp

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.3.2

2 files

0.3.1

2 files

0.3.0

2 files

0.2.0

2 files

0.1.8

2 files

This release

0.1.7 This release

2 files

0.1.5

2 files

0.1.4

2 files

0.1.3

2 files

0.1.2

2 files

0.1.1

2 files

0.1.0

2 files

0.0.1

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page