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Hypster

Hypster is an MIT-licensed Python configuration framework for AI and machine-learning workflows. It supports hierarchical and conditional settings, type hints and validation, replayable WHEN/THEN rules, and an optional Optuna integration for hyperparameter optimization.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Hypster — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Hypster when Python workflow settings need reusable structure, conditional choices or typed parameters, or when those parameters should feed an Optuna search.

Who it is for

  • Python developers managing configuration across machine-learning experiments.
  • Teams composing nested or conditional model and workflow settings.
  • Practitioners connecting configurable parameters to Optuna searches.

Documented uses

  • Define reusable configuration functions with selectable and bounded parameters.
  • Inspect configuration structure or a conditional branch before creating an object.
  • Instantiate configured objects with explicit value overrides.
  • Map configuration parameters into Optuna trials for hyperparameter search.
Before you choose

Requirements & limits

The repository identifies the project as open source under the MIT license. Installation instructions include uv and link to guidance for pip.

  • The framework configures Python objects; model training and scoring remain part of the user's own objective code.
  • Optuna support is provided as an optional installation extra.
  • A separate optional extra is documented for notebook visualization.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Hypster with optional Optuna support

Add the framework and its documented Optuna integration to a Python project.

Before you start

  • A Python project using uv.

Steps

  1. Add the base package with the command below.

    Documented command
    uv add hypster

  2. Add the optional hyperparameter-optimization extra with the command below.

    Documented command
    uv add 'hypster[optuna]'

Expected outcome

The project declares Hypster and its optional Optuna backend.

Guide limitations

  • This covers package installation only, not configuring or running an optimization study.
Documentation 1 ↗
Source-based practical guide

Define and inspect a model configuration

Create a Python configuration function, inspect its choices, and instantiate an object with overrides.

Before you start

  • Hypster installed in the Python project.
  • A configuration function that accepts an HP argument and returns the object to configure.

Steps

  1. Import HP, explore and instantiate from hypster.

  2. Define a configuration function using Hypster parameter helpers such as select, float or int.

  3. Call explore on the function to inspect its configuration; pass values to inspect a conditional branch if needed.

  4. Call instantiate with the function and a values mapping of overrides.

Expected outcome

The function’s configuration can be inspected and used to construct an object with chosen values.

Guide limitations

  • The configuration function and any returned application or model object must be supplied by the user.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
gilad-rubin/hypster · Official homepage title · retained in its source language
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