@Marvin can i use dasktaskrunner without a dask cluster? if i do, what happens under the hood? (i.e. infrastructure-wise)

Yes, you can use the

DaskTaskRunner

without an explicitly defined Dask cluster. In both Prefect 2.x and 3.x, if you don't provide a Dask cluster, Prefect will automatically create a temporary local Dask cluster for the duration of the flow run. ### Under the Hood - Prefect 2.x: A temporary

distributed.LocalCluster

is created and then torn down within the

start()

context manager of the

DaskTaskRunner

. This means that the infrastructure needed to run your tasks is managed entirely by this temporary local cluster. - Prefect 3.x: Similarly, a temporary Dask cluster is created if you don't provide an address to an existing Dask scheduler. The number of workers is typically based on the number of cores available on your machine. This local cluster allows for parallel task execution without requiring any external Dask cluster infrastructure. ### Sources - Prefect 2.x: DaskTaskRunner Documentation - Prefect 3.x: Prefect Dask Integration Guide Feel free to ask if you need more details or have any other questions!

@Marvin what are the parameters for DaskTaskRunner cluster_kwargs

The

cluster_kwargs

parameter in

DaskTaskRunner

allows you to pass additional keyword arguments to the Dask cluster class when creating a temporary Dask cluster. This is useful if you need to customize how the temporary cluster is created and managed. Here are some of the common parameters you can provide with `cluster_kwargs`: ### Examples -

n_workers

: Number of worker processes to use. -

threads_per_worker

: Number of threads per worker process. ### Usage

python
from prefect_dask import DaskTaskRunner

DaskTaskRunner(
    cluster_kwargs={
        "n_workers": 4,
        "threads_per_worker": 2
    }
)

### Prefect 2.x Documentation In Prefect 2.x, these parameters are documented under the `DaskTaskRunner`: - DaskTaskRunner 2.x Docs ### Prefect 3.x Documentation In Prefect 3.x, the

cluster_kwargs

are similarly used: - DaskTaskRunner 3.x Docs If you have any additional questions, feel free to ask!

@Marvin how can i tell if my task / flow is running on a dasktaskrunner during runtime?

To determine if your task or flow is running on a Dask cluster managed by

DaskTaskRunner

during runtime, you can utilize the

get_dask_client

from Prefect Dask integration, which connects to the existing Dask client if running within a Dask cluster. Here's how you can do it: ### Code Example for Prefect 2.x and 3.x You can use the following approach in your flow or task to check if it's running on a Dask cluster:

python
from prefect import task, flow
from prefect_dask import DaskTaskRunner
from dask.distributed import get_client, Client

@task
def check_dask_environment():
    try:
        client = get_client()
        print("Running on Dask cluster with client:", client)
    except ValueError:
        print("Not running on a Dask cluster")

@flow(task_runner=DaskTaskRunner())
def my_flow():
    check_dask_environment()

if __name__ == "__main__":
    my_flow()

### Explanation -

get_client()

: Attempts to return the Dask

Client

if the code is running within a Dask cluster context. If it's not, it raises a

ValueError

. - Handling the Exception: Catch the

ValueError

to determine that the code is not running on a Dask cluster. ### References - For more detailed information, you can check the documentation for Prefect 2.x and 3.x: - Prefect Dask Integration 2.x - Prefect Dask Integration 3.x - GitHub Issues related to similar queries: - Dask task scheduling hangs, Prefect Issue #12877 - Prefect task fails if dask worker is killed, Prefect Issue #8821 Feel free to ask if you have more questions or need further assistance!