hey folks, is it possible to load an asset via

load_asset_value

with a different IO manager than when it was written out?

hi @Chaitya! this is not currently possible -- mind sharing a bit more about your use case here? one pattern we've recommended in the past is to have a single IOManager whose load_input function branches based on the

dagster_type

of the input it's loading the asset into. In this case, you could pass in a

dagster_type

argument to

load_asset_value

for example, you might have an asset stored as a parquet file somewhere, but you can either load that into a spark dataframe or a pandas dataframe.

hey @owen, can you illustrate more about this example: one asset read and writes with pandas, but the downstream asset works with Spark?

here's an example! 😄 https://docs.dagster.io/guides/dagster/software-defined-assets#defining-a-multi-type-io-manager

@owen Specifically in my upstream IO manager, I output stuff using a directory using a dagster

run_id

prefix. Downstream, i'm loading my asset in a separate jupyter notebook that has no context about the dagster run ID... as a result, the IO manager has no idea how to load the asset (because there's no run context within

load_asset_value

. I would like to avoid making the IO manager configurable (and hardcoding the run ID in the run config). The reason i want to specify a different IO manager is that I'd like to make a specific IO manager that I can use in this case so that I don't have to make my original IO manager configurable by run id.