v0.1.0

Hyperion v0.1.0 - Initial Alpha Release

⚠️ Note: This is an early alpha release. The API may change significantly in future versions.

Hyperion is a modern hyperparameter optimization framework built for the agentic era. Unlike conventional libraries, it orchestrates and reasons about long-running, parallel experiments through an event-driven, agent-based architecture. Experiments are modeled as a dynamic exploration tree, enabling efficient branching, pruning, and adaptation across parallel runs while maintaining a transparent reasoning trace.

Key Features

• 🎯 Multiple Search Strategies: Random, Grid, Beam Search, Bayesian Optimization, Population-Based Training
• 🤖 Agent Integration: LLM-driven and rule-based agents for intelligent optimization
• 🌳 Lineage-Aware Trials: First-class support for branching search with trial ancestry tracking
• 📊 Full Observability: Complete event log with decision rationale and reproducible experiments
• 🚀 Progressive Scaling: From in-memory prototypes to distributed execution
• 🔧 Ergonomic API: High-level tune() API with progressive disclosure to framework internals

Installation

pip install hyperion-opt

Quick Start

from hyperion import tune, Float, Choice

best_trial = tune(
    objective=train_model,
    space={
        "learning_rate": Float(0.001, 0.1),
        "batch_size": Choice([32, 64, 128]),
    },
    max_trials=100,
)

Documentation

• API Guide
• Framework Guide
• Examples

What's Next

This framework was developed to explore next-generation approaches to AutoML with an emphasis on interpretability, agent integration, and systematic exploration strategies.

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Full Changelog: v0.1.0...v0.1.0