@Marvin when using dasktaskrunner with kubernetes workpool in prefect 3.0 will the dask workers utilize resources of kubernetes job or deployed prefect worker ?

In Prefect 3.0, when using

DaskTaskRunner

with a

Kubernetes WorkPool

, the Dask workers are configured to utilize resources specified for the Kubernetes jobs. This means that the resources are managed by the Kubernetes cluster configured for the Dask workers rather than relying on the resources of the deployed Prefect worker. Dask's resource allocation such as GPUs or CPUs used by the workers can be annotated, and these resources are abstracted in Dask and not inferred directly from the system (i.e., they don't use the Prefect worker's resources directly). The documentation outlines how you can define resource annotations and configure Dask clusters, which can adaptively scale based on the workload. Details and examples can be found in the Prefect Dask integration documentation (source, source). If you have more specific configurations or constraints for your Kubernetes environment, you would need to set these up accordingly in your Dask cluster configuration within Prefect.

@Marvin how to configure Kubernetes cluster for the Dask workers in kubernetes workpool prefect 3

To configure a Kubernetes cluster for Dask workers in a Kubernetes WorkPool with Prefect 3.0, you can follow these general steps and guidelines: 1. Cluster Configuration: - Prefect provides an option to create a cluster for your Dask workers using various Dask cluster-managers. In a Kubernetes environment, you'll typically specify configurations via a

KubernetesClusterConfig

block. - Generally, you can leave the cluster config blank if the worker already has appropriate access and permissions for interacting with your desired Kubernetes setup. 2. Resource Requests: - You can modify the base job template to add or delete specific fields, such as resource requests. For example, adding a CPU request can be configured in the JSON representation of the job template under the "Advanced" tab. - Example configuration for a CPU request:

json
     "cpu_request": {
       "title": "CPU Request",
       "description": "The CPU allocation to request for this pod.",
       "default": "default",
       "type": "string"
     }

- This setting allows modifying the amount of resources requested for each Dask worker pod, tailoring your cluster's utilization as needed. 3. Adaptive Scaling: - Prefect's

DaskTaskRunner

supports adaptive scaling. This means you can specify a minimum and maximum number of workers, letting the cluster scale up and down based on your workload dynamically. You can pass

adapt_kwargs

to the

DaskTaskRunner

to use this feature. 4. Flow Deployment: - When deploying a flow using Dask in Kubernetes, make sure your deployment configuration specifies the appropriate worker pool and resource allocations. - Ensure that your Kubernetes cluster and Dask workers are set up to handle communication appropriately, using correct addresses and ports. More detailed examples and further configurations can be found in the documentation links provided: Kubernetes Infrastructure Examples and Prefect Dask Integration. Additionally, there are no current GitHub issues directly relevant to Kubernetes cluster setup for Dask workers in Prefect 3.0, indicating that setup problems might be more specific to individual configurations rather than systemic issues. If you encounter difficulties, it might be useful to check the project's GitHub issues or community forums for specific troubleshooting advice.