clearconf 0.3.3

ClearConf is a library created to support easy and manageble python configuration. It consists in a CLI tool to manage the configuration directory, and in a python class (BaseConfig) which adds additional functionalities to a configuration class.

clearconf

ClearConf is a library created to support easy and manageble python configuration. It consists in a CLI tool to manage configuration and in a python class (BaseConfig) which adds additional functionalities to a configuration class.

Installation

To install ClearConf just run

pip install clearconf

Usage

The first step to use clearconf would be to use the CLI tool in the root of your project to initialize it:

cconf init

This will generate a config directory where you will store your configurations and a .clearconf file used by ClearConf to keep track of configurations. After this you can start populating your config directory. You can find examples of configuration files in the Example section.

❗ClearConf recursively recognize as configuration all python files ending with _conf

Finally you can import a generic configuration in your script as

from configs import Config

and use it as you please.

When the script is run, if a default configuration has been set via the CLI

cconf defaults add main.py test_conf.py

such configuration will be dynamically imported.

Otherwise, clearconf will list all the available configuration and ask you to pick one.

0: example3_conf
1: example1_conf
2: example2_conf
3: example4_conf
Choose a configuration file:

CLI

For more informations on the command line interface check the related README here

Examples

Example 1

A configuration file for machine learning could be structure as follow.

from models import MLP
from datasets import ImageNet


class Config(BaseConfig):
    seed = 1234

    class Model:
        architecture = MLP

        class Params:
            num_layers = 16
            layers_dim = [96] * num_layers


    class Data:
        dataset = ImageNet

        class Params:
            root = './data/PCN'
            split = 'PCN.json'
            subset = 'train'

The training/test script could read the configuration as follows:

from configs import Config

Model = Config.Model
Data = Config.Data

model = Model.architecture(**Model.Params.to_dict())
dataset = Data.dataset(**Data.Params.to_dict())

Example 2

It is also possible to simplify the configuration further using inheritance. For example the Model configuration seen before would look like this:

from models import MLP

class Config(BaseConfig):
    seed = 1234

    class Model(MLP):

        class Params:
            num_layers = 16
            layers_dim = [96] * num_layers

The corresponding scirp is:

from configs import Config

Model = Config.Model
model = Model(**Model.Params.to_dict())