@Marvin how can I use

FlowRunContext.get().flow.name

to get a flow name within a mapped task that uses the

DaskTaskRunner

?

In order to retrieve the flow name within a mapped task when using the

DaskTaskRunner

, you typically would access

prefect.context

, which contains runtime information during flow and task execution. However, the specific

FlowRunContext.get().flow.name

usage is not standard in the newer versions of Prefect, and it's not clear from the current Prefect documentation or code excerpts retrieved. Since

FlowRunContext

appears to be an unconventional way to access the flow name and because Prefect 2.x introduces several changes including how context is managed, I would recommend instead using a combination of

get_run_context()

function and the flow/task runtime API to access flow-related information. If the

FlowRunContext

were still the proper way to do so, assuming the API did not undergo a breaking change, in a mapped task you could access the flow name by fetching the current flow run context, something along these lines:

python
from prefect import flow, get_run_context

@flow
def my_flow():
    context = get_run_context()
    flow_name = context.flow.name
    # Now you can use flow_name within your task

In mapped tasks, this should work similarly, as the context is available in each individual task run, regardless of whether it is a mapped task or not. To get a definitive answer, I would recommend to consult the latest official Prefect documentation or community forums, as the newer versions of Prefect, especially 2.x, have changed how some functionalities are implemented.

@Marvin is there a way to get flow name from a TaskRunContext?

In Prefect, the

TaskRunContext

can be used to access information about the current task run. When you are within a task run and want to access the flow run context, which contains the flow name, you can do so explicitly. According to the Prefect documentation, you can access the

FlowRunContext

even from within a

TaskRunContext

. Here is how you can access the flow name from within a task:

python
from prefect.context import FlowRunContext, TaskRunContext

task_run_ctx = TaskRunContext.get()  # Get the TaskRunContext
flow_run_ctx = FlowRunContext.get()  # Get the FlowRunContext

# Access the flow name from the FlowRunContext
flow_name = flow_run_ctx.flow_name if flow_run_ctx is not None else None

# Now you can use flow_name within your task

Note that the flow run context is not sent to distributed task workers by default because it is costly to serialize and deserialize. If you need to access the flow name in a distributed environment, such as when using the

DaskTaskRunner

, you would need to ensure that the relevant context information is passed to the task workers.

@Marvin how do I pass flow run context information to a task worker with

DaskTaskRunner

?

The current excerpts don't provide explicit information on how to pass flow run context to the

DaskTaskRunner

workers. However, in general, when using Dask (or any distributed system), you need to ensure that any necessary context is properly serialized and sent to the worker processes. As per Prefect best practices, here's a general approach to ensure that the flow context information, which you need within a task, is available when using a `DaskTaskRunner`: 1. Import

DaskTaskRunner

from

prefect_dask.task_runners

. 2. Make sure necessary Prefect collections are installed, such as

prefect-dask

. 3. In your flow definition, assign

DaskTaskRunner

as the task runner. 4. Configure the task runner to connect to your Dask cluster. Here is an example configuration:

python
from prefect import flow
from prefect_dask.task_runners import DaskTaskRunner

@flow(task_runner=DaskTaskRunner())
def my_flow():
    # Define your flow here
    pass

# Rest of your code, including task definitions

Now, if you need to access Prefect context information within your task, and you're using the

DaskTaskRunner

, you would want to make sure the information is serializable and possibly use Prefect's own mechanisms (like the

get_run_context()

function mentioned in Prefect's task runner documentation) to propagate that information to the tasks. Since Dask requires data to be serializable to distribute it to workers, any data passed to tasks, such as context data, needs to be able to be pickled. As a result, flow and task runners might not automatically transmit the flow run context to distributed workers to avoid the overhead. If you have a specific context value needed within your task that is serializable, you may pass it directly as a task argument, or consider storing context information in a place that is accessible within the Dask workers (such as a distributed cache or configuration store). For the most up-to-date information, always refer to the latest Prefect documentation or visit Prefect community forums for more help.