coqui-ai/coqpit

Simple but maybe too simple config management through python data classes. We use it for machine learning.

57
/ 100
Established

This helps machine learning engineers manage experimental settings. It takes configuration details like model parameters, dataset paths, and training settings, and outputs a structured, shareable configuration file. This is for machine learning practitioners who need to define, validate, and share their experiment setups consistently across different environments and team members.

108 stars. Used by 2 other packages. No commits in the last 6 months. Available on PyPI.

Use this if you need a lightweight, Python-native way to define, validate, and serialize your machine learning model configurations, especially when collaborating or deploying to different platforms.

Not ideal if your configuration heavily relies on `Union` types for console arguments, requires serialization formats other than JSON, or involves `List` types with multiple item type annotations.

machine-learning-engineering experiment-management model-configuration ML-operations hyperparameter-tuning
Stale 6m
Maintenance 0 / 25
Adoption 11 / 25
Maturity 25 / 25
Community 21 / 25

How are scores calculated?

Stars

108

Forks

39

Language

Python

License

MIT

Last pushed

Apr 12, 2023

Commits (30d)

0

Dependencies

1

Reverse dependents

2

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