Metadata-Version: 2.1
Name: liquid_engine
Version: 0.1.5
Summary: Liquid Engine standalone Python package
Author: Bruno M. Saraiva, António D. Brito, Inês M. Cunha, Ricardo Henriques
Author-email: bruno.msaraiva2@gmail.com, antmsbrito95@gmail.com, inescunha200@gmail.com, ricardo.jv.henriques@gmail.com
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Testing
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: numpy <2,>=1.22
Requires-Dist: scikit-learn >=1.1.0
Requires-Dist: scipy >=1.8
Requires-Dist: pyyaml >=6.0
Requires-Dist: importlib-resources

# Liquid Engine

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Liquid Engine - Accelerating Bioimage Analysis with dynamic selection of algorithm variations

---

## Liquid Engine

The Liquid Engine is a high-performance, adaptive framework designed to optimize computational workflows for bioimage analysis. It dynamically generates optimized CPU and GPU-based code variations and selects the fastest combination based on input parameters and device performance, significantly enhancing computational speed. The Liquid Engine employs a machine learning-based Agent to predict the optimal combination of implementations, adaptively responding to delays and performance variations.

Key features include:

    - Multiple Implementations: Utilizes various acceleration strategies such as PyOpenCL, CUDA, Cython, Numba, Transonic, and Dask to deliver optimal performance.
    - Machine Learning Agent: Predicts the best-performing implementation combinations and adapts dynamically to ensure maximum efficiency.
    - Automatic Benchmarking: Continuously benchmarks different implementations to maintain a historical record of runtimes and improve performance over time.
    - Seamless Integration: Can easily be integrated into any existing workflow with no extra work for end users.

The Liquid Engine's adaptability and optimization capabilities make it a powerful tool for researchers handling extensive microscopy datasets and requiring high computational efficiency.

if you found this work useful, please cite: [preprint](https://www.biorxiv.org/content/10.1101/2023.08.13.553080v1) and  [![DOI](https://zenodo.org/badge/505388398.svg)](https://zenodo.org/badge/latestdoi/505388398)



## Instalation

`Liquid Engine` is compatible and tested with Python 3.9, 3.10 and 3.11 in MacOS, Windows and Linux.
You can install `Liquid Engine`via [pip]:

```shell
pip install liquid_engine
```

## License

Distributed under the terms of the [CC-By v4.0] license,
"Liquid Engine" is free and open source software

## Issues

If you encounter any problems, please [file an issue] along with a detailed description.

[CC-By v4.0]: https://creativecommons.org/licenses/by/4.0/
[file an issue]: https://github.com/HenriquesLab/LiquidEngine/issues
[pip]: https://pypi.org/project/pip/
