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Hierarchical configuration framework

Project description

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Confugue is a hierarchical configuration framework for Python. It provides a wrapper class for nested configuration dictionaries (usually loaded from YAML files), which can be used to easily configure complicated object hierarchies.

The package is ideal for configuring deep learning experiments. These often have large numbers of hyperparameters, and managing all their values globally can quickly get tedious. Instead, Confugue allows each part of the deep learning model to be automatically supplied with hyperparameters from a YAML configuration file, eliminating the need to pass them around. The nested structure of the configuration file follows the hierarchy of the model architecture, with each part of the model having access to the corresponding section of the file.

As an example, here is a simplified code snippet from a deep learning project which uses Confugue:

class Model:

    def __init__(self, vocabulary, use_sampling=False):
        self.embeddings = self._cfg['embedding_layer'].configure(EmbeddingLayer,
        self.decoder = self._cfg['decoder'].configure(RNNDecoder,

class RNNDecoder:

    def __init__(self, vocabulary, embedding_layer):
        self.cell = self._cfg['cell'].configure(tf.keras.layers.GRUCell,
        self.output_projection = self._cfg['output_projection'].configure(

The model could then be configured using the following config file, overriding the values specified in the code and filling in the ones that are missing.

  output_size: 300
    class: !!python/name:tensorflow.keras.layers.LSTMCell
    units: 1024
use_sampling: True

Installation & Documentation

The package is available from PyPI and can be installed with pip install confugue.

The docs include:

Comparison with other frameworks


Confugue is somewhat similar to Gin, but is much more minimalistic yet, in some ways, more powerful. Some advantages of Confugue over Gin are:

  • It is straightforward to configure many objects of the same type with different parameters for each; with Gin, this is possible, but it requires using scopes.

  • Any function or class can be configured without having been explicitly registered.

  • Config files may override the type of an object (or the function being called) while preserving the default parameters provided by the caller.

  • It is possible to access (and even manipulate) configuration values explicitly instead of (or in addition to) having them supplied as parameters.

  • The structure of the config file is nested – typically following the call hierarchy – compared to Gin’s linear structure.

On the other hand, Confugue doesn’t have some of the advanced features of Gin, such as config file inclusion or ‘operative configuration’ logging. It also doesn’t support macros, but a similar effect can be achieved using PyYAML’s aliases.

Some other differences (which may be viewed as advantages or disadvantages in different situations) are:

  • Gin config files specify default values for function parameters, which can be overridden by the caller. In Confugue, on the other hand, the config file has the final say.

  • Gin will seamlessly load defaults from the configuration file every time a configurable function or class is called. Confugue is more explicit in that the caller first has to ask for a specific key from the configuration file.


Sacred also offers configuration functionality, but its goals are much broader, focusing on experiment management (including keeping track of metrics and other information). Confugue, on the other hand, is not specifically targeted to scientific experimentation (even though it is particularly well suited for machine learning experiments). As for the configuration mechanism itself, Sacred has so-called ‘captured functions’ which resemble configurable functions in Confugue or Gin, but does not offer the same ability to configure arbitrary objects in a hierarchical way.

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