hypster 0.4.0

A flexible configuration system for Python projects

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Hypster is a lightweight configuration framework for managing and optimizing AI & ML workflows

⚠️ Hypster is in active development and not yet battle-tested in production. If you’re gaining value and want to promote it to production, please reach out!

Key Features

• 🐍 Pythonic API: Intuitive & minimal syntax that feels natural to Python developers
• 🪆 Hierarchical, Conditional Configurations: Support for nested and swappable configurations
• 📐 Type Safety: Built-in type hints and validation
• 🧪 Hyperparameter Optimization Built-In: Native, first-class optuna support

Installation

You can install Hypster using uv:

uv add hypster
# optional HPO backend
uv add 'hypster[optuna]'

Or using pip:

pip install hypster

Quick Start

Define a configuration function and instantiate it with overrides:

from hypster import HP, explore, instantiate
from llm import LLM

def llm_config(hp: HP):
    model_name = hp.select(["gpt-4o-mini", "gpt-4o"], name="model_name")
    temperature = hp.float(0.7, name="temperature", min=0.0, max=1.0)
    max_tokens = hp.int(256, name="max_tokens", min=1, max=4096)
    llm = LLM(model_name=model_name, temperature=temperature, max_tokens=max_tokens)
    return llm

explore(llm_config)
# llm_config
# ├── model_name: select = "gpt-4o-mini"  (options: ["gpt-4o-mini", "gpt-4o"])
# ├── temperature: float = 0.7  (0.0-1.0)
# └── max_tokens: int = 256  (1-4096)

llm = instantiate(llm_config, values={"model_name": "gpt-4o-mini", "temperature": 0.3})
llm.invoke("How's your day going?")

Use explore(..., values=...) to inspect a specific conditional branch before you instantiate it, or explore(..., return_info=True) to get a JSON-serializable schema object.

HPO with Optuna

import optuna
from hypster.hpo.types import HpoInt, HpoFloat, HpoCategorical
from hypster.hpo.optuna import suggest_values


def objective(trial: optuna.Trial) -> float:
    values = suggest_values(trial, config=model_cfg)
    model = instantiate(model_cfg, values=values)
    X, y = make_classification(
        n_samples=400, n_features=20, n_informative=10, random_state=42
    )
    return cross_val_score(model, X, y, cv=3, n_jobs=-1).mean()

study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=30)

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.