question about s3 interaction within tasks: I have around 100 tasks that run in parallel in a deployment and all interact with s3 (specifically they check if a file exists and if so they do some stuff) all the implementations I see eventually create some client interface which is ok, on the first task it takes 0.2 seconds to init s3 client, and by the time we reach the 100th task it takes around 10 seconds. which is wayyyy to much for me. is there a best practice to share objects/ s3 clients between tasks and flows that run in the same infra? stuff that I looked at: https://boto3.amazonaws.com/v1/documentation/api/latest/index.html https://pypi.org/project/aioboto3/ https://prefecthq.github.io/prefect-aws/s3/ https://docs.prefect.io/latest/concepts/filesystems/#s3

where are your tasks running? depending on your infra, seems like you could either use a global s3_client var, or pass the same client to each of the tasks?

running in local hosted agent (pod on kubernetes over aws) when passing an s3 client as a parameter I got a pydantic validation error assumed you are only supposed to pass basic pydantic types that can be validated

does setting the s3_client type from s3 as the type hint in the task func help or it still fails (disclaimer: i don't work at prefect, just use it a fair amount and happened to be on slack this morning and see this)

will give that a shot tomorrow and see