What do to then? You can either: - create a custom ``AbstractRunner`` to run several time the same pipeline and create a mlflow sub run each time:

python
# inside a custom AbstractRunner
with mlflow.start_run(nested=True):
   runner.run(pipeline, catalog)

- do all hyperparameter tuning inside a node, and laucnh sub run like described above:

python
# inside a node
for hyperparams in hyperparams_candidates:
  with mlflow.start_run(nested=True):
   model.train(**hyperparams)

There is an interesting discussion on the same theme here: https://github.com/Galileo-Galilei/kedro-mlflow/issues/246