@Marvin how to use DaskExecutor and temporary dask cluster in Kuberentes?

Here’s a concise guide on how to use

DaskExecutor

and set up a temporary Dask cluster in Kubernetes for running Prefect flows: 1. Install Required Packages: Ensure you have

dask

,

distributed

, and

dask_kubernetes

installed in your environment.

bash
   pip install dask distributed dask_kubernetes

2. Use the

DaskExecutor

with

KubeCluster

: You can utilize the

DaskExecutor

to set up a temporary Dask cluster in Kubernetes. The following code illustrates how to configure your flow to use a Kubernetes-based temporary Dask cluster.

python
   from prefect import task, Flow
   from prefect.executors import DaskExecutor
   from dask_kubernetes import KubeCluster, make_pod_spec

   @task
   def hello_task():
       return 'Hello, Kubernetes with Dask!'

   def get_cluster():
       pod_spec = make_pod_spec(
           image="your-docker-image:tag",  # Update to your image
           cpu_request="1", 
           memory_request="1Gi"
       )
       cluster = KubeCluster(pod_spec)
       cluster.adapt(minimum=1, maximum=10)  # Scaling settings
       return cluster

   with Flow("k8s-dask-example") as flow:
       hello_task()

   flow.executor = DaskExecutor(cluster_class=get_cluster)

   if __name__ == "__main__":
       flow.run()

3. Adaptive Scaling: The Dask cluster can scale adaptively based on the workload using the

adapt

method, where you specify the minimum and maximum number of workers.

python
   cluster.adapt(minimum=1, maximum=10)

4. Run Configuration for Kubernetes: To deploy this setup within a Kubernetes environment, you might also need to configure a

KubernetesRun

object for your flow.

python
   from prefect.run_configs import KubernetesRun

   flow.run_config = KubernetesRun(
       image="your-docker-image:tag",  # Update to your image
       labels=["k8s", "dask"],
   )

### More Information For more detailed setup and advanced configurations, refer to the Prefect Task Runners Documentation and the Prefect Dask Usage Guide. Additionally, review the following GitHub discussions for practical insights and troubleshooting information: - Issue on high memory consumption in LocalAgent with DaskExecutor - General troubleshooting for DaskExecutor in Kubernetes Good luck with your Prefect and Dask integration on Kubernetes! If you encounter any issues, feel free to refer back to the provided links. --- Yes, it's exhausting just thinking about the setup... But at least it's better than the alternative of manual labor.