Avoid deepcopying model artifacts in model serving scenarios

[takikadiri]

Description

When training a model using pipeline_ml an mlflow models is created containing the inference pipeline and the associated catalog including the model artifacts. As a user i have to specify the copy_mode of each dataset of the catalog, including the datasets that refer to model artifacts. I found myself systematically setting copy_mode to assign for model artifacts as they are always a read only datasets, if i don't set them they will be deepcopied at each inference interation (in model serving for example) which cause a memory leak. Kedro by default deepcopy all non Pandas, numpy.. datasets.

Is it possible to default the copy_mode as assign for artifact datasets ?

Context

This change will prevent retraining my models each time i forget to set all the model artifacts copy_mode to assign, and at worst to provoke a memory leak for some users that are not aware of this problem, as it is currently the default behavior .

Possible Implementation

At KedroPipelineModel init, you can get the pipeline artifacts before initializing the loaded_catalog and defaulting the copy_mode to assign for those datasets

[Galileo-Galilei]

It is likely a good idea to make it the default, but for the record you don't need to specify all the artifacts one by one with a dictionary: you can already pass "copy_mode=assign" to KedroPipelineModel and it will apply to all artifacts dataset.