Hello channel 👋 I have two Kedro pipelines, say TrainA and TrainB. The pipelines are quite similar in that • most node pairs are identical, i.e. they use the same function and the same input/output configuration • some nodes use the same function and their input/output configuration differs only by the parameters that are read in. Right now, the two pipelines live in two different Python modules, namely tr`train_a.py` and

train_b.py

. My idea is to separate the process from the product, i.e. creating a general pipeline for training that can be used to train both A and B given their respective configuration parameters—they can possibly live in their own

parameters.yaml

so to avoid override and/or conflict. The two questions related are: 1. does this harmonise with the Kedro principles? 2. is this possible in Kedro? Thank you. 🙏

I think this is a perfect use-case for our namespaced pipelines construct (also known as modular pipelines) as you can

.replace(parameters={'base_param':'some_other_param'})

Thank you! I will try straight away and I will follow up in this thread. 🤩

so the idea of this pattern is that the code is static, but it possible to override the catalog or parameter information sort of like a sub-class

yes, this rephrasing is correct—the code is made in such a way that when you call e.g.

params:my_params

inside one node, both configuration YAML file will be passing the required parameter.

can you not use the same parameter key twice?

I am afraid I don't fully understand, can you elaborate on that?