Integrations

Can I use Hyperactive with PyTorch (not Lightning)?

Yes, create a custom objective function:

import torch

def objective(params):
    model = MyPyTorchModel(
        hidden_size=params["hidden_size"],
        dropout=params["dropout"],
    )
    # Train and evaluate your model
    train_model(model, train_loader)
    accuracy = evaluate_model(model, val_loader)
    return accuracy

How does Hyperactive compare to Optuna?

Hyperactive with native GFO backend:

• Simple, unified API

• Wide variety of optimization algorithms

• Great for hyperparameter tuning

Hyperactive with Optuna backend:

• Access Optuna’s algorithms through Hyperactive’s interface

• Combine the strengths of both libraries

Pure Optuna:

• More features (pruning, distributed, database storage)

• Larger community and ecosystem

• More configuration options

Choose based on your needs: Hyperactive for simplicity, Optuna for advanced features.

Can I use Hyperactive with other ML frameworks?

Yes, any framework works with custom objective functions:

# XGBoost example
import xgboost as xgb

def objective(params):
    model = xgb.XGBClassifier(**params)
    scores = cross_val_score(model, X, y, cv=3)
    return scores.mean()