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
1 maintainer
Metadata
1 maintainer
Release files for gin-config-v2 0.8.0
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| gin_config_v2-0.8.0.tar.gz | 51.8 kB | Without provenance | Details for gin_config_v2-0.8.0.tar.gz |
Release files / gin_config_v2-0.8.0.tar.gz
| Download URL | gin_config_v2-0.8.0.tar.gz |
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| Size | 51.8 kB |
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