Goptuna

Overview

Goptuna is a hyperparameter optimization framework written in Go, inspired by Optuna. It provides a lightweight, native library for automated search of optimal parameters in machine learning models or any other optimization problem.

Best for

Best for
Go developers who need a fast, embedded hyperparameter optimization library.

Use cases

• Tuning hyperparameters for Go-based machine learning pipelines
• Running automated parameter searches for simulation or configuration optimization
• Integrating Bayesian optimization into Go applications for decision-making

Notes

Goptuna is a hyperparameter optimization framework written in Go, inspired by Optuna. It provides a lightweight, native library for automated search of optimal parameters in machine learning models or any other optimization problem.

277 stars on GitHub. Last updated 2025-08-12. Licensed MIT.

Use cases

• Tuning hyperparameters for Go-based machine learning pipelines
• Running automated parameter searches for simulation or configuration optimization
• Integrating Bayesian optimization into Go applications for decision-making

Pros

• Native Go implementation with no external Python dependencies
• Lightweight and easy to embed in existing Go codebases
• Supports common optimization algorithms (e.g., TPE, CMA-ES)

Cons

• Smaller community and fewer prebuilt samplers compared to Optuna
• Limited documentation and examples relative to more mature frameworks
• Not designed for distributed or large-scale parallel optimization out of the box

Indexed from awesome-llmops and enriched against its public facts.