Getting started

This package is adapted to a project where you need to run a number of experiments. In this setup, it can be useful to gather all the parameters in the project to a common location, some “config files”, so you can access and modify them easily. This package is based on YAML, therefore your config files should be YAML files. One such YAML file could be :

gpu: true
data_path: "./data"
learning_rate: 0.01

Those will be the default values for those three parameters, so we will keep them in the file my_project/configs/default.yaml. Then, we just need to subclass the Configuration class in this package so your project-specific subclass knows where to find the default values for your project. A minimal project-specific subclass looks like:

from yaecs import Configuration

class ProjectSpecific(Configuration):
    @staticmethod
    def get_default_config_path():
        return "./configs/default.yaml"

    def parameters_pre_processing(self):
        return {}

That’s all there is to it! Now if we use config = ProjectSpecific.load_config(), we can then call config.data_path or config.learning_rate to get their values as defined in the default config. We don’t need to specify where to get the default config because a project should only ever have one default config, which centralises all the parameters in that project. Since the location of the default config is a project constant, it is defined in your project-specific subclass and there is no need to clutter your main code with it. Now, for example, your main.py could look like:

from project_config import ProjectSpecific

if __name__ == "__main__":
    config = ProjectSpecific.load_config()
    config.merge_from_command_line()
    print(config.details())

Then, calling python main.py --learning_rate=0.001, the call to merge_from_command_line would parse the command line and find the pre-existing parameter learning_rate, then change its value to 0.001. Thus, the printed result would yield:

MAIN CONFIG :
Configuration hierarchy :
> ./configs/default.yaml

 - gpu : true
 - data_path : ./data
 - learning_rate : 0.001

The Configuration hierarchy tells you about the creation history of the config, in this case only the default config was used. Then, all parameters are displayed. There are of course many other features in this package which you can use to organise your parameters, hierarchise your experiments etc. The idea being that once the bare minimum presented above is set up, scaling up is just as simple.

You can learn more about all these features in our DOCUMENTATION.

config_history

The Config History is a side-feature of the main Config System. It can be configured for any project which uses the Config System and provides a flexible framework to easily build graphs representing past experiments. In these graphs, each node represents an experiment, and vertices are drawn between your experiments to visualise easily which parameters changed from one node to another.

The graph can be coloured to show your most successful experiments, or grouped by parameters to see how well they have been explored in your experiment history. This makes it very useful to review your past work, share it with colleagues or make unexpected correlations appear.

Please refer to our DOCUMENTATION to learn more about its setup and usage.