{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/7"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/5"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/11"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"Gin provides a lightweight configuration framework that lets you decorate Python functions and classes to accept parameter values from config files or command-line arguments, eliminating the need for manual configuration object management.","skillfed_tags":["configuration-management","dependency-injection","ml-experiments"],"use_cases":["Configure machine learning model hyperparameters from external config files for experiments","Manage nested configuration for complex applications without writing factory or parameter-plumbing code","Run the same script with different parameter sets by changing only the config file, not the code","Share reproducible experiment configurations across team members by version-controlling config files","Simplify parameter handling by reading structured configuration from files instead of manual argument parsing"],"what_it_does":"Gin is a configuration framework built on dependency injection principles that lets you externalize function and class parameters into config files rather than hardcoding them. You decorate functions with @gin.configurable, then supply values via a simple config syntax in files or on the command line. This is especially useful for machine learning experiments where you have many nested parameters that change frequently between runs.\n\nThe package has no runtime dependencies and installs cleanly. It's designed to reduce boilerplate around configuration management and parameter passing, making it easier to experiment with different settings without modifying code. The framework is particularly well-suited for projects with complex, hierarchical parameter structures.","worth_installing":"Yes, if you run machine learning experiments or manage complex nested configurations. Gin eliminates boilerplate and makes experiments reproducible and shareable. The permissive Apache 2.0 license and zero runtime dependencies make adoption low-risk. The stable maintenance status is appropriate for a mature configuration library. Install it if parameter management is a pain point in your workflow."},"id":"gin-config","links":{"html":"https://skillfed.io/packages/gin-config","md":"https://skillfed.io/packages/gin-config.md","pypi":"https://pypi.org/project/gin-config/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2021-11-03","license_spdx":null,"license_treatment":"permissive","name":"gin-config","python_support":"unspecified","summary":"Gin-Config: A lightweight configuration library for Python"},"popularity":{"monthly_downloads":578677,"position":5922,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.0"}