gin-config
Gin-Config: A lightweight configuration library for Python
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Installation is straightforward with no runtime dependencies.
- The package is actively maintained with recent commits, though the latest release dates to 2021-11-03, suggesting the project may be in a stable maintenance phase rather than active development.
License · maintenance · safety
Apache 2.0 (permissive) — Gin is licensed under Apache 2.0, a permissive license that allows commercial and private use with minimal restrictions, making it safe to adopt in most projects.
last release 2021-11-03 (1745 days) · last repo commit 2026-07-02 · 2,154 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 578,677 downloads/mo, #5,922 on PyPI
Alternatives
Verify before relying
pip install gin-config
import gin
@gin.configurable
def train(learning_rate=None, batch_size=None):
pass
gin.parse_config_file('config.gin')
train()- Whether the package supports modern Python versions beyond 3.7 (classifiers list only up to 3.7)
- Current maintenance status and whether the 2021 release date indicates active development or stable maintenance mode
- Specific example parameter values for typical machine learning configurations
What it is and 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.
The 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.
Use it for
- 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
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
gin-config on PyPI
Before you install
Installation is straightforward with no runtime dependencies. The package is actively maintained with recent commits, though the latest release dates to 2021-11-03, suggesting the project may be in a stable maintenance phase rather than active development.
License in practice
Gin is licensed under Apache 2.0, a permissive license that allows commercial and private use with minimal restrictions, making it safe to adopt in most projects.
Quickstart
pip install gin-config
import gin
@gin.configurable
def train(learning_rate=None, batch_size=None):
pass
gin.parse_config_file('config.gin')
train()Verify before relying
- Whether the package supports modern Python versions beyond 3.7 (classifiers list only up to 3.7)
- Current maintenance status and whether the 2021 release date indicates active development or stable maintenance mode
- Specific example parameter values for typical machine learning configurations
Package facts
| License | Apache 2.0 permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 1,745 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 578,677 / month, #5,922 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 2Programming Language :: Python :: 2.7Programming Language :: Python :: 3Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: gin_config-0.5.0-py3-none-any.whl
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