Hi team! I am looking at the

kedro mlflow

documentation and there is this thing called

pipeline_ml_factory

which is said to be used inside a

pipeline.py

file which I cannot find is this a kedro version issue?. Do you know any repository that uses this plugin with pytorch lightning? so I can see a more concrete example of its usage. Here is the page I've been looking

1. For Kedro mlflow and `pipeline_ml_factory`there is a great official tutorial here: https://github.com/Galileo-Galilei/kedro-mlflow-tutorial 2. For Kedro + PyTorch Lightning - https://getindata.com/blog/deep-learning-with-azure-pytorch-distributed-training-done-right-kedro/ (there is a GH repo linked there too). You don’t have to use Azure ML. Adding kedro-mlflow is simple, as PyTorch-Lightning has MLflowLogger built-in, you just need to pass proper config:

mlflow_logger = MLFlowLogger(
        experiment_name,
        tracking_uri,
    )
    mlflow_logger._run_id = mlflow.active_run().info.run_id

which can be injected e.g. from Kedro hooks and then just plug it in:

pl.Trainer(logger=mlflow_logger, ...)

This is indeed a typo in the documentation: in recent versions, ``pipeline.py`` has been renamed ``pipeline_registry.py`` . Thanks @marrrcin for pointing out the tutorial!

Thank you very much! I'll take a look

@Tomás Rojas for PyTorch + Kedro, this also might be of your interest: https://kedro-org.slack.com/archives/C03QPDQTLBG/p1678891373264199

Thanks!