[<Library component: Models|Core|etc...>] MLflow Logging multiple models

[Dansk04]

Description

Hello,
in the Documentation MLflow is used for logging only one model in a run. Now there is the functionality to train several models like in the example provided below.

models = [
    KAN(input_size=2*horizon,
        h=horizon,
        scaler_type='robust',
        max_steps=2,
        early_stop_patience_steps=3),
    LSTM(input_size=2*horizon,
        h=horizon,
        scaler_type='robust',
        max_steps=2,
        early_stop_patience_steps=3)
                
]
  mlflow.pytorch.autolog(checkpoint=False)
  with mlflow.start_run() as run:
      nf = NeuralForecast(models=models, freq='M')
      nf.fit(full_train, val_size=horizon)  
      mlflow.pytorch.get_default_conda_env()
      `mlflow.pytorch.get_default_pip_requirements()

When logging with MLflow, one run is created for all models and the logged metrics cannot be differentiated between the individual models.
Is there a possibility to log each model individually when fitting nf objects which contains multiple models? Or is the only way to train each model individually?

Use case

No response