Gin
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
Release files for gin-config 0.2.0
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| File | Interpreter | ABI | Platform | Reset |
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| gin_config-0.2.0-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Release files / gin_config-0.2.0-py2.py3-none-any.whl
| Download URL | gin_config-0.2.0-py2.py3-none-any.whl |
|---|---|
| Size | 37.4 kB |
| Tags | Python 2 Python 3 |
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twine/1.11.0 pkginfo/1.4.2 requests/2.20.0 setuptools/39.2.0 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16
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