@Marvin: How can prefect help in managing dask cluster lifetime?

Prefect can help manage the lifecycle of a Dask cluster through the use of DaskExecutor. This can be configured to start, stop, and scale the Dask cluster for the lifetime of a flow run. Below are some necessary points and resources: 1. Executor Class: An

Executor

class manages the Dask cluster's lifetime using the standard Dask cluster interface. Here’s an example:

python
   from prefect.executors import DaskExecutor

   executor = DaskExecutor(
       cluster_class="dask_cloudprovider.FargateCluster",
       cluster_kwargs={"image": "my-fancy-prefect-image"},
       # Additional config here
   )

2. Cancellation Support: The

DaskExecutor

can be configured to handle state transitions like

Cancelling

. This allows Prefect to shut down the cluster if a flow run is canceled. 3. Integration with Kubernetes: Examples show specific configurations for using Dask with Kubernetes. This involves deprecating previous classes in favor of a unified

DaskExecutor

approach:

python
   from prefect.environments import KubernetesJobEnvironment

   environment = KubernetesJobEnvironment(
       executor_class=DaskExecutor,
       executor_kwargs=dict(
           cluster_class="dask_kubernetes.KubeCluster",
           cluster_kwargs={"n_workers": 2, "resources": {"memory": "2GB"}}
       )
   )

More detailed discussions and examples may be found in specific Github issues, such as: - Decouple Dask and Environments - Support for cancellation of active flow runs Let me know if you need more specific examples or details.