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.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Research
Four npm packages disguised as cryptographic tools steal developer credentials and send them to attacker-controlled Telegram infrastructure.
Security News
Ruby maintainers from Bundler and rbenv teams are building rv to bring Python uv's speed and unified tooling approach to Ruby development.
Security News
Following last week’s supply chain attack, Nx published findings on the GitHub Actions exploit and moved npm publishing to Trusted Publishers.
