Skip to main content

Liquid Engine standalone Python package

Project description

Liquid Engine

PyPI Python Version Downloads Docs License Tests Contributors GitHub stars GitHub forks DOI

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 and DOI

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 Enginevia pip:

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.

Project details


Download files

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

Source Distribution

liquid_engine-0.1.6.tar.gz (14.2 kB view details)

Uploaded Source

Built Distribution

liquid_engine-0.1.6-py3-none-any.whl (14.4 kB view details)

Uploaded Python 3

File details

Details for the file liquid_engine-0.1.6.tar.gz.

File metadata

  • Download URL: liquid_engine-0.1.6.tar.gz
  • Upload date:
  • Size: 14.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.12.4

File hashes

Hashes for liquid_engine-0.1.6.tar.gz
Algorithm Hash digest
SHA256 51bfb9014edcf22738aaa5c01600cd20ec8738244a0a88efa97dca86294e1410
MD5 e8dcff47c84c6cc387eeee742f78509f
BLAKE2b-256 183267d5c4023b6896c0161b480492114bb376af10caad600ea6070c8df4b491

See more details on using hashes here.

File details

Details for the file liquid_engine-0.1.6-py3-none-any.whl.

File metadata

File hashes

Hashes for liquid_engine-0.1.6-py3-none-any.whl
Algorithm Hash digest
SHA256 51004702f25cdc75d53a3a36ae1562046d101b83a860c4fa14eb3a5d5f2da056
MD5 c025123521e8d445c56d531fd3922421
BLAKE2b-256 fb5201eebd3cc208bf11ce4af4f724e8cbcecd686820136ffb0534a0f3968211

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page