Hi, Are there any guides on how to control the

tune

configuration when running the

Auto

models? For instance, the

Auto

models utilize the

tune

and

ray

packages, which are automatically invoked by the

Auto

models themselves. I like more control over the behavior of

ray

and

tune

, particularly regarding the output directory and the number of CPUs to allocate.

nf = NeuralForecast(
    models=[
        AutoNHITS(h=48, config=config_nhits, loss=MQLoss(), num_samples=5),
        AutoLSTM(h=48, loss=MQLoss(), num_samples=2), 
    ],
    freq='H'
)
nf.fit(df=Y_df)

Appreciate any guidance on this 🙏 https://nixtla.github.io/neuralforecast/examples/getting_started_complete.html

Hi @Afiq Johari. This is our basic tutorial on automatic hyperparameter tuning: https://nixtla.github.io/neuralforecast/examples/automatic_hyperparameter_tuning.html.