imate, short for Implicit Matrix Trace Estimator, is a modular and high-performance C++/CUDA library distributed as a Python package that provides scalable randomized algorithms for the computationally expensive matrix functions in machine learning.

Links

Install

Install with pip

pypi

pip install imate

Install with conda

conda-version

conda install s-ameli::imate

Docker Image

docker-pull deploy-docker

docker pull sameli/imate

Supported Platforms

Successful installation and tests performed on the following operating systems, architectures, and Python and PyPy versions:

Platform

Arch

Device

Python Version

PyPy Version 1

Continuous Integration

3.10

3.11

3.12

3.13

3.14

3.9

3.10

3.11

Linux

X86-64

CPU

✔

✔

✔

✔

✔

✔

✔

✔

build-linux

GPU

✔

✔

✔

✔

✔

✔

✔

✔

AARCH-64

CPU

✔

✔

✔

✔

✔

✔

✔

✔

GPU

✔

✔

✔

✔

✔

✔

✔

✔

macOS

X86-64

CPU

✔

✔

✔

✔

✔

✔

✔

✔

build-macos

GPU 2

✖

✖

✖

✖

✖

✖

✖

✖

ARM-64

CPU

✔

✔

✔

✔

✔

✖

✔

✔

GPU 2

✖

✖

✖

✖

✖

✖

✖

✖

Windows

X86-64

CPU

✔

✔

✔

✔

✔

✖

✖

✖

build-windows

GPU

✔

✔

✔

✔

✔

✖

✖

✖

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

1. Our wheels for PyPy are exclusively available through pip and cannot be installed using conda.
2. MacOS does not natively support NVIDIA GPUs.

Supported GPU Architectures

imate can run on CUDA-capable multi-GPU devices. Using the docker container is the easiest way to run imate on GPU devices. The supported GPU micro-architectures and CUDA version are as follows:

Version \ Arch

Kepler

Maxwell

Pascal

Volta

Turing

Ampere

Ada

Hopper

Blackwell

CUDA 9

✖

✖

✖

✖

✖

✖

✖

✖

✖

CUDA 10

✔

✔

✔

✔

✔

✖

✖

✖

✖

CUDA 11

✖

✖

✔

✔

✔

✔

✔

✔

✖

CUDA 12

✖

✖

✔

✔

✔

✔

✔

✔

✔

CUDA 13

✖

✖

✖

✖

✔

✔

✔

✔

✔

Documentation

deploy-docs binder

See documentation, including:

Performance

imate is scalable to very large matrices. Its core library for basic linear algebraic operations is faster than OpenBLAS, and its pseudo-random generator is a hundred-fold faster than the implementation in the standard C++ library.

Read about the performance of imate in practical applications:

How to Contribute

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

How to Cite

If you publish work that uses imate, please consider citing the manuscripts available here.

License

license

This project uses a BSD 3-clause license, in hopes that it will be accessible to most projects. If you require a different license, please raise an issue and we will consider a dual license.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

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

Credits

Author: Siavash Ameli

License

BSD License

Requires

Python >=3.8

Provides Extra

extra test docs

Tags

matrix-computations matrix-inverse interpolation-techniques cholesky-decomposition randomized-algorithms lanczos-iteration parameter-estimation radial-basis-function polynomial-bases orthogonal-polynomials cross-validation

Classifiers

Report project as malware

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