Hello there, I really like the new software-defined asset abstraction. But I'm having issues wrapping my head around how to use them in this context: Let's say I want to create an asset

xyz_plot

and I want to reuse this function in multiple projects, i.e.

xyz_plot

should use

project-a/data.csv

and

project-b/data.csv

(or the resulting DataFrame of the csv) as input. How do I have to define the

xyz_plot

?

@asset
def xyz_plot(in_key: DataFrame):
    # how should in_key be really named? in_key is not fixed

Hi Stefan - asset definitions contain their dependencies. If you want to have an unbound dependency, I'd recommend using an op. As of today's release, it will be possible to define an op and then build assets from it. Something like:

project_a_data = SourceAsset(key=["project_a", "data"])

project_b_data = SourceAsset(key=["project_b", "data"])

@op
def xyz_plot(in_key: DataFrame):
    ...

plot_a = AssetsDefinition.from_op(xyz_plot, keys_by_input_name={"in_key": AssetKey(["project_a", "data"])})

plot_b = AssetsDefinition.from_op(xyz_plot, keys_by_input_name={"in_key": AssetKey(["project_b", "data"])})

@sandy perfect, this clears things up! For the ML use cases I have in mind I would use the

op

and

AssetsDefinition.from_op

approach for pretty much everything as all assets depend on an unbound dependency...I think. I have to play around with this.