@Marvin what should be jobspec template for my kubernetes workpool if i want to run dasktask runner with local cluster in prefect 3

To run a

DaskTaskRunner

with a

LocalCluster

in a Kubernetes work pool with Prefect 3, you generally need to define a job specification template in your Kubernetes settings. Here’s a basic guide to help you set it up: 1. Kubernetes JobSpec Template Configuration: You should modify the default base job template to include additional configurations that may be necessary for the Dask environment. This is usually done by editing the JSON representation of the base job template on the Kubernetes cluster. You can specify resources like CPU and RAM if needed. 2. DaskTaskRunner with LocalCluster: - The

DaskTaskRunner

will create a temporary

LocalCluster

for the duration of your flow run. - If you want to use specific configurations, like adaptive scaling, you can customize the

DaskTaskRunner

with parameters such as

cluster_class="distributed.LocalCluster"

and any relevant

cluster_kwargs

for your setup. 3. Advanced Settings: - You can specify additional settings within the Kubernetes job template, such as environment variables or resource requests (

cpu_request

,

memory_request

), under the advanced tab of your Kubernetes configuration. You'll find more specific details and examples in the Prefect 3.x documentation, particularly in the sections regarding Kubernetes Integration and Prefect Dask Integration. These sections provide a comprehensive guide on setting up and customizing your Kubernetes and Dask configurations. If you need more detailed customization, or encounter specific issues, I recommend checking the above resources or reaching out on Prefect's community forums for more tailored support.

@Marvin when my flow runs first it creates 5 dask workers but then retires 4 and only one is running

The behavior you're seeing, where Dask workers initially start but then are retired, could be related to Dask's adaptive scaling feature, which allows the cluster to scale based on current resource needs. This behavior might indicate that Dask is automatically adjusting the number of workers according to the workload. Here are some possible reasons and solutions for this issue: 1. Adaptive Scaling: If you're using the

DaskTaskRunner

with adaptive scaling enabled, Dask might retire workers if it determines that fewer workers are needed after the initial task distribution. You can configure this by using

adapt_kwargs

with your

DaskTaskRunner

. For instance:

python
   from prefect import flow, task
   from prefect_dask.task_runners import DaskTaskRunner

   task_runner = DaskTaskRunner(
       cluster_class="dask.distributed.LocalCluster",
       cluster_kwargs={"n_workers": 5},  # Initial number of workers
       adapt_kwargs={"minimum": 1, "maximum": 5}  # Adaptive scaling settings
   )

2. Cluster Resource Limits: Ensure that your cluster has enough resources to maintain the desired number of workers. Overcommitment or resource constraints might cause workers to scale down. 3. Unexpected Worker Retirement: There might be underlying issues causing unexpected retirement of workers. According to a GitHub issue Prefect is unable to handle rescheduling events or worker retirement, when Dask retires workers and reschedules tasks, certain errors can occur in Prefect. For more details on these configurations and behavior, consider reviewing the Prefect Dask Integration Usage Guide and exploring the GitHub issue details for potential workarounds or updates on current challenges with worker retirement. Adjusting your Dask settings or tuning your cluster's adaptive scaling behavior might resolve the issue.