Gin provides a lightweight configuration framework for Python, based on
dependency injection. Functions or classes can be decorated with
@gin.configurable, allowing default parameter values to be supplied from a
config file (or passed via the command line) using a simple but powerful syntax.
This removes the need to define and maintain configuration objects (e.g.
protos), or write boilerplate parameter plumbing and factory code, while often
dramatically expanding a project's flexibility and configurability.
Gin is particularly well suited for machine learning experiments (e.g. using TensorFlow), which tend to have many parameters, often nested in complex ways.
Authors: Dan Holtmann-Rice, Sergio Guadarrama, Nathan Silberman Contributors: Oscar Ramirez, Marek Fiser
FAQs
Gin-Config: A lightweight configuration library for Python
We found that gin-config demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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