🐛 Remove unnecessary dependency to input dataset in KedroPipelineModel (

#273)

CHANGELOG.md

@@ -8,6 +8,7 @@
 
 ### Fixed
 
+- :bug: Force the input dataset in ``KedroPipelineModel`` to be a ``MemoryDataSet`` to remove unnecessary dependency to the underlying Kedro ``AbstractDataSet`` used during training ([#273](https://github.com/Galileo-Galilei/kedro-mlflow/issues/273))
 -   :bug: Make ``MlflowArtifactDataset`` correctly log in mlflow Kedro DataSets without a ``_path`` attribute like ``kedro.io.PartitionedDataSet``  ([#258](https://github.com/Galileo-Galilei/kedro-mlflow/issues/258)).
 -   :bug: Automatically persist pipeline parameters when calling the ``kedro mlflow modelify`` command for consistency with how ``PipelineML`` objects are handled and for ease of use ([#282](https://github.com/Galileo-Galilei/kedro-mlflow/issues/282)).
 

kedro_mlflow/mlflow/kedro_pipeline_model.py

@@ -109,9 +109,11 @@ def _extract_pipeline_catalog(self, catalog: DataCatalog) -> DataCatalog:
         for data_set_name in self.pipeline.inputs():
             if data_set_name == self.input_name:
                 # there is no obligation that this dataset is persisted
-                # thus it is allowed to be an empty memory dataset
-                data_set = catalog._data_sets.get(data_set_name) or MemoryDataSet()
-                sub_catalog.add(data_set_name=data_set_name, data_set=data_set)
+                # and even if it is, we keep only an ampty memory dataset to avoid
+                # extra uneccessary dependencies: this dataset will be replaced at
+                # inference time and we do not need to know the original type, see
+                # https://github.com/Galileo-Galilei/kedro-mlflow/issues/273
+                sub_catalog.add(data_set_name=data_set_name, data_set=MemoryDataSet())
             else:
                 try:
                     data_set = catalog._data_sets[data_set_name]