@Marvin, how can I get the slurmcluster class from the dasktaskrunner?

Whoops, it seems like a bit of intergalactic turbulence there. I'm going to run the necessary searches again to find you some useful information on obtaining the

SlurmCluster

class from the

DaskTaskRunner

. One moment please. You can customize the

DaskTaskRunner

in Prefect to use a

SlurmCluster

by leveraging the

cluster_class

parameter. In both Prefect 2.x and 3.x,

DaskTaskRunner

allows you to specify the cluster class you want to use via the

cluster_class

argument. This is particularly useful if you have a specific setup like a Slurm-based cluster. Here’s a general approach: 1. Specify the Cluster Class: You can set the

cluster_class

parameter in

DaskTaskRunner

to your desired Dask cluster type, such as

dask_jobqueue.SLURMCluster

, which is a part of Dask's jobqueue package. 2. Cluster Configuration: Use

cluster_kwargs

to pass any additional configurations or parameters required by the

SlurmCluster

, like the partition, job extra settings, etc. Here's an example of how you might configure a

DaskTaskRunner

to use a `SlurmCluster`:

python
from prefect import task, Flow
from prefect_dask import DaskTaskRunner

@task
def hello_task():
    return "Hello, Prefect and Dask!"

with Flow("slurm-cluster-flow") as flow:
    hello_task()

flow.run(task_runner=DaskTaskRunner(
    cluster_class="dask_jobqueue.SLURMCluster",
    cluster_kwargs={
        "cores": 8, 
        "processes": 1,
        "memory": "32GB",
        "walltime": "02:00:00",
        "job_extra": {'--exclusive': ''},
    }
))

You just need to ensure that you meet the requirements of your SLURM configuration within

cluster_kwargs

. For more detailed information, you can check out the relevant documentation links: - Prefect 2.x docs: Dask Task Runner - Prefect 3.x docs: Dask Integration If you encounter any issues, feel free to reach out. It's not like I'm sulking around here with existential dread.