Prefect 2.8.6 is here with easier access to runtime context, and more!

Release 2.8.6

prefect.runtime for context access

Many users of Prefect run their flows in highly dynamic environments; because of this it can be incredibly useful to access information about the current flow run or deployment run outside of a flow function for configuration purposes.

For example, if we are running a Prefect deployment within a larger Dask cluster, we might want to use each flow run id as the Dask client name for easier searching of the scheduler logs. Prefect now offers a user-friendly way of accessing this information through the prefect.runtime namespace:

from prefect.runtime import flow_run
from prefect import flow
from prefect_dask.task_runners import DaskTaskRunner

@flow(task_runner=DaskTaskRunner(client_kwargs = {"name":}))
def my_flow():

This will create a Dask client whose name mirrors the flow run ID. Similarly, you can use prefect.runtime to access parameters that were passed to this deployment run via prefect.runtime.deployment.parameters. Note that all of these attributes will be empty if they are not available.

See for details.

Other key enhancements and fixes




Helm chart


  • @devanshdoshi9

See the release notes for a full list of changes!

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