Hello, i want to save multiple keras models in seperate partitions. I can save a single model without problems, however, when I try to switch to partitionedDataset i constantly run into errors when trying to save. My dataset is defined as: model_partitioned_{name}: type: partitions.PartitionedDataset path: data/07_model_output/versioned/{name}/ filename_suffix: ".tf" dataset: type: tensorflow.TensorFlowModelDataset save_args: save_format: tf Trying to save the corrsponding data leads to the following error: (<class 'kedro.io.core.DatasetError'>, DatasetError('Failed while saving data to dataset kedro_datasets.partitions.partitioned_dataset.PartitionedDataset(filepath=.../data_analysis/data/07_model_output/versioned/monte_carlo_models\', dataset="kedro_datasets.tensorflow.tensorflow_model_dataset.TensorFlowModelDataset(save_args={\'save_format\': \'tf\'}, load_args={\'errors\': \'ignore\'})").\nThe first argument to

Layer.call

must always be passed.'), <traceback object at 0x0000025E444A4540>) When debugging, i can save each model individually by model.save(), so i assume the error message is not truly valid.

Hi Fabian, Thanks for sharing this issue! To help debug this better, could you provide some additional information? • What version of

kedro

are you using? • What version of

kedro-datasets

are you using? • Can you share the node function that's saving these models? (How are you returning the models? as a dict?) • Can you share your catalog entry for this partitioned dataset? • Could you share the full error traceback? thanks.