Hyperparameter optimization for .NET

OptunaSharp

A .NET port of Optuna — the define-by-run hyperparameter optimization framework — built on top of NumSharp. Optuna is the behavioral source of truth; OptunaSharp aligns to it.

Introduction Getting started Integrations API reference

Status: core surface ported

The core algorithm surface is in place — samplers, pruners, storages, importance, and visualization — with more landing module-by-module.

dotnet add package OptunaSharp

using OptunaSharp;
using OptunaSharp.Samplers;
var study = Optuna.CreateStudy(sampler: new TPESampler(seed: 42));
study.Optimize(trial =>
{
    double x = trial.SuggestFloat("x", -10, 10);
    return (x - 2) * (x - 2);
}, nTrials: 20);
Console.WriteLine(study.BestValue);   // 0.0004973004749973951

# The same study in Optuna (Python) prints the same value
import optuna
def objective(trial):
    x = trial.suggest_float("x", -10, 10)
    return (x - 2) ** 2
study = optuna.create_study(sampler=optuna.samplers.TPESampler(seed=42))
study.optimize(objective, n_trials=20)
print(study.best_value)   # 0.0004973004749973951

Documentation Map

Pick where you want to start.

Overview Introduction

What OptunaSharp is, the study/trial model, and how it relates to Optuna and NumSharp.

Setup Getting started

Install, build, test, and pack. How the NumSharp reference resolves.

Reference API reference

Generated from the OptunaSharp assemblies by DocFX.

Explore all integrations GitHub Repository NuGet Package NumSharp