Goptuna
A decentralized hyperparameter optimization framework for Go, inspired by Optuna, supporting Bayesian optimization and evolution strategies.
What is Goptuna?
Goptuna is a hyperparameter optimization framework written in Go, inspired by Optuna. It automates the search for optimal parameters in machine learning models and other systems by evaluating objective functions across defined search spaces. The framework supports various state-of-the-art algorithms like TPE and CMA-ES to efficiently navigate complex parameter landscapes.
Go developers and machine learning engineers who need to tune hyperparameters for models, optimize system configurations (e.g., server goroutines, cache sizes), or run distributed optimization experiments.
Developers choose Goptuna for its pure Go implementation, which ensures easy integration into Go projects, and its support for distributed, concurrent optimization with a built-in dashboard for visualization, offering a robust alternative to Python-based tools like Optuna.
Overview
A hyperparameter optimization framework, inspired by Optuna.
Use Cases
Best For
- Tuning hyperparameters for machine learning models in Go environments
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