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.
1 maintainer
Release files for cocoonet 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| cocoonet-0.0.1.tar.gz | 3.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| cocoonet-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 6.6 kB
Release files / cocoonet-0.0.1.tar.gz
| Download URL | cocoonet-0.0.1.tar.gz |
|---|---|
| Size | 3.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
2707441cc0a1f79f6ec9d6f0616794d5ef776644fcffde52643c9e562b7f9c29
|
|
BLAKE2b-256 checksum How to use checksums |
3d7a226e83a5f4cba460d17e3a9f863e036b7658408f2ae5e2139dd31d43db81
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 26, 2026.
Transparency logRelease files / cocoonet-0.0.1-py3-none-any.whl
| Download URL | cocoonet-0.0.1-py3-none-any.whl |
|---|---|
| Size | 3.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
b8f2291737764d6d77f0a8d07bce750e03dcd4190eff73473c8eacf2960383dd
|
|
BLAKE2b-256 checksum How to use checksums |
c798372f53219af9ec3a8b5fadde8cb8b244d66f5058dd10663d2c8dac5a7d3c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 26, 2026.
Transparency log
