Metadata-Version: 2.4
Name: cocoonet
Version: 0.0.1
Summary: Scientific evaluation of machine learning models with local and distributed workers.
Author-email: Siwei Luo <siwei1102@outlook.de>
Project-URL: Homepage, https://github.com/siwei66/cocoonet
Project-URL: Repository, https://github.com/siwei66/cocoonet
Project-URL: Issues, https://github.com/siwei66/cocoonet/issues
Keywords: machine-learning,model-evaluation,applied-machine-learning,scientific-computing,distributed-computing
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: System :: Distributed Computing
Requires-Python: <3.15,>=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: aiofiles<26,>=25.1
Requires-Dist: dill<1,>=0.4
Provides-Extra: dev
Requires-Dist: pytest>=9.0; extra == "dev"
Requires-Dist: black>=26.5; extra == "dev"
Requires-Dist: ruff>=0.15; extra == "dev"
Requires-Dist: mypy>=2.1; extra == "dev"
Provides-Extra: test
Requires-Dist: pytest>=9.0; extra == "test"
Provides-Extra: docs
Requires-Dist: sphinx<10,>=9.1; extra == "docs"
Requires-Dist: pydata-sphinx-theme<1,>=0.21; extra == "docs"
Requires-Dist: numpydoc<2,>=1.11; extra == "docs"
Dynamic: license-file

# CocooNet

## A Python framework for scientific evaluation of machine learning models using local and distributed workers designed for academic and applied research

CocooNet is a Python framework for scientific evaluation of machine learning models using local and distributed workers, designed for academic and applied research involving small- to moderate-scale models. It coordinates independent model evaluations and collects structured evaluation reports for scikit-learn-like and PyTorch classifiers and regressors, including validation results, performance metrics, residual analysis, basic visualizations, and optional training-sample influence analysis. Evaluation workloads can be executed using CPUs, CPU multiprocessing, and GPUs across available local and remote resources.
