gilad-rubin/hypster

HyPSTER - HyperParameter optimization on STERoids

[Documentation] | [Installation] | [Quick Start]

Hypster is a lightweight configuration system for AI & Machine Learning projects. It offers minimal, intuitive pythonic syntax, supporting hierarchical and swappable configurations.

Installation

You can install Hypster using pip:

pip install hypster

Quick Start

Here's a simple example of how to use Hypster:

from hypster import HP, config

@config
def my_config(hp: HP):
    chunking_strategy = hp.select(['paragraph', 'semantic', 'fixed'], default='paragraph')

    llm_model = hp.select({'haiku': 'claude-3-haiku-20240307',
                           'sonnet': 'claude-3-5-sonnet-20240620',
                           'gpt-4o-mini': 'gpt-4o-mini'}, default='gpt-4o-mini')

    llm_config = {'temperature': hp.number_input(0),
                  'max_tokens': hp.number_input(64)}

    system_prompt = hp.text_input('You are a helpful assistant. Answer with one word only')

Now we can instantiate the configs with our selections and overrides:

results = my_config(final_vars=["chunking_strategy", "llm_config", "llm_model"],
                    selections={"llm_model" : "haiku"},
                    overrides={"llm_config.temperature" : 0.5})

Inspiration

Hypster draws inspiration from Meta's hydra and hydra-zen framework. The API design is influenced by Optuna's "define-by-run" API.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.