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Hi @Phil!

nf.models[0].early_stop_patience_steps = 1

Wont work because it is an argument of the

callbacks

object of the Trainer. The function in that post should still work, is it giving an error?

Hi Cristian. sorry for the delay in my response. It's been a chaotic morning at LinkedIn this morning. I managed to make it work. I adapted the function above to this

def set_trainer_kwargs(
    nf: NeuralForecast, 
    max_steps: int, 
    early_stop_patience_steps: int, 
    val_check_steps: Optional[int] = None) -> None:
    """Set trainer arguments for fine-tuning a pre-trained NeuralForecast model.

    Args:
        nf: A pre-trained NeuralForecast model.
        max_steps: The maximum number of training steps.
        early_stop_patience_steps: Patience for early stopping (0 to disable).
        val_check_steps: The frequency of validation checks during training.

    Returns:
        None

    Example usage:
        trained_model_path = "./results/12315464155/"
        nf = load_neural_forecast_model(model_path=trained_model_path)
        set_trainer_kwargs(nf=nf, max_steps=1000, early_stop_patience_steps=3, val_check_steps=35)
        nf.fit(df=new_df, use_init_models=False, val_size=nf.models[0].h)
    """
    # Trainer arguments.
    trainer_kwargs = {
        # The maximum number of training steps.
        "max_steps": max_steps,
        # Display a progress bar during training.
        "callbacks": [TQDMProgressBar()],  
        # Use GPU if available, or "auto" to decide automatically.
        "accelerator": "gpu" if torch.cuda.is_available() else "auto",  
        # Use all GPUs if available, or 1 CPU if not.
        "devices": -1 if torch.cuda.is_available() else 1, 
        # Disable model checkpointing.
        "enable_checkpointing": False,
    }

    # Early stopping callback.
    # Stop training early if validation loss doesn't improve for 'patience' steps.
    if early_stop_patience_steps > 0:
        trainer_kwargs["callbacks"].append(
            EarlyStopping(monitor="ptl/val_loss", patience=early_stop_patience_steps)
        ) 
    # Set custom validation check frequency.
    if val_check_steps:
        nf.models[0].val_check_steps = val_check_steps
    
    # Update trainer arguments for the model and its initialization.
    nf.models[0].trainer_kwargs = trainer_kwargs
    nf.models_init[0].trainer_kwargs = trainer_kwargs