detkit Documentation

A python package to compute some common functions involving determinant of matrices used in machine learning.

GitHub

PyPI

Anaconda Cloud

Docker Hub

Supported Platforms

Successful installation and tests have been performed on the following platforms and Python/PyPy versions shown in the table below.

Platform	Arch	Python Version				PyPy Version 1			Continuous Integration
		3.9	3.10	3.11	3.12	3.8	3.9	3.10
Linux	X86-64	✔	✔	✔	✔	✔	✔	✔
	AARCH-64	✔	✔	✔	✔	✔	✔	✔
macOS	X86-64	✔	✔	✔	✔	✔	✔	✔
	ARM-64	✔	✔	✔	✔	✔	✔	✔
Windows	X86-64	✔	✔	✔	✔	✔	✔	✔
	ARM-64 2	✔	✔	✔	✔	✔	✔	✔

Python wheels for detkit for all supported platforms and versions in the above are available through PyPI and Anaconda Cloud. If you need detkit on other platforms, architectures, and Python or PyPy versions, raise an issue on GitHub and we build its Python Wheel for you.

^{1. Wheels for PyPy are exclusively available for installation through pip and cannot be installed using conda.}

^{2. Wheels for Windows on ARM-64 architecture are exclusively available for installation through pip and cannot be installed using conda.}

Install

Install with pip from PyPI:

pip install detkit

Install with conda from Anaconda Cloud:

conda install -c s-ameli detkit

For complete installation guide, see:

• Install
  • 1. Install from Wheels
  • 2. Install in Virtual Environment
  • 3. Runtime Dependencies
  • 4. Compile from Source
  • 5. Generate Documentation
  • 6. Test Package
  • 7. Troubleshooting

Docker

The docker image comes with a pre-installed detkit, an NVIDIA graphic driver, and a compatible version of CUDA Toolkit libraries.

Pull docker image from Docker Hub:

docker pull sameli/detkit

For a complete guide, see:

• Docker
  • Install Docker
  • Get detkit Docker Image
  • Examples of Using detkit Docker Container
  • Deploy detkit Docker Container on GPU

List of Functions

detkit.logdet	Computes the logdet of a matrix.
detkit.loggdet	Computes the loggdet of a matrix.
detkit.logpdet	Computes the logpdet of a matrix.

See API Reference for a list of all functions.

Tutorials

Launch an online interactive tutorial in Jupyter notebook.

Benchmarks

See benchmark test for evaluating the numerical performance of the functions in real applications.

Features

• Functions are implemented with a novel algorithm described in [1].

• The underlying library is implemented in C++ and wrapped in cython.

• An accurate count of computational FLOPs during the execution of functions can be measured.

How to Contribute

We welcome contributions via Github’s pull request. If you do not feel comfortable modifying the code, we also welcome feature request and bug report as Github issues.

Related Projects

detkit is used in the following python packages:

A high-performance python package for machine learning using Gaussian process regression.

A high-performance python package for scalable randomized algorithms for matrix functions in machine learning.

How to Cite

If you publish work that uses detkit, please consider citing the following manuscripts.

[1]

Ameli, S., and Shadden. S. C. (2023). A Singular Woodbury and Pseudo-Determinant Matrix Identities and Application to Gaussian Process Regression. Applied Mathematics and Computation 452, 128032. DOI BibTeX PDF

@article{amc-2023,
    title = {A singular Woodbury and pseudo-determinant matrix identities and application to Gaussian process regression},
    journal = {Applied Mathematics and Computation},
    volume = {452},
    pages = {128032},
    year = {2023},
    issn = {0096-3003},
    doi = {https://doi.org/10.1016/j.amc.2023.128032},
    author = {Siavash Ameli and Shawn C. Shadden},
}

[2]

Ameli, S. (2022). DetKit, a python package for determinant matrix functions. BibTeX

@misc{zenodo.6395320,
    title = {{DetKit}, a python package for determinant matrix functions},
    author = {Ameli, S.},
    year = {2022},
    howpublished = {\url{https://pypi.org/project/detkit/}}
}