👋Welcome

Hypster is a lightweight framework for defining configurations functions to optimize ML & AI workflows.

Key Features

• 🐍 Pythonic API: Intuitive & minimal syntax that feels natural to Python developers
• 🪆 Hierarchical Configurations: Support for nested and swappable configurations
• 📐 Type Safety: Built-in type hints and validation using Pydantic
• 📦 Portability: Easy serialization and loading of configurations
• 🧪 Experiment Ready: Built-in support for hyperparameter optimization
• 🎮 Interactive UI: Jupyter widgets integration for interactive parameter selection

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How Does it work?

1

Install Hypster

pip install hypster

2

Define a configuration space

from hypster import config, HP


@config
def llm_config(hp: HP):
    model_name = hp.select(["gpt-4o-mini", "gpt-4o"])
    temperature = hp.number(0.0, min=0.0, max=1.0)

3

Instantiate your configuration

results = my_config(values={"model" : "gpt-4o", "temperature" : 1.0})

4

Define an execution function

def generate(prompt: str, model_name: str, temperature: float) -> str:
    model = llm.get_model(model_name)
    response = model.prompt(prompt, temperature=temperature)
    return response

5

Execute!

generate(prompt="What is Hypster?", **results)

Discover Hypster

Getting Started

How to create & instantiate Hypster configs

Tutorials

Step-by-step guides for ML & Generative AI use-cases

Best Practices

How to make the most out of Hypster

Why Use Hypster?

In modern AI/ML development, we often need to handle multiple configurations across different scenarios. This is essential because:

1. We don't know in advance which hyperparameters will best optimize our performance metrics and satisfy our constraints.

2. We need to support multiple "modes" for different scenarios. For example:

   1. Local vs. Remote Environments, Development vs. Production Settings

   2. Different App Configurations for specific use-cases and populations

Hypster takes care of these challenges by providing a simple way to define configuration spaces and instantiate them into concrete workflows. This enables you to easily manage and optimize multiple configurations in your codebase.

Additional Reading

• Introducing Hypster - A comprehensive introduction to Hypster's core concepts and design philosophy.

• Implementing Modular RAG With Haystack & Hypster - A practical guide to building modular, LEGO-like reconfigurable RAG systems.

• 5 Pillars for Hyper-Optimized AI Workflows - Key principles for designing optimized AI systems. The process behind this article gave rise to hypster's design.