@Marvin I am using the dask task runner. How can I configure a work pool and workers to use this?

To configure a work pool and workers to use the Dask task runner in Prefect, you can follow these steps: 1. Create a Work Pool: You can create a work pool using the Prefect UI, CLI, REST API, or the Terraform provider for Prefect Cloud. Here’s how to use the CLI:

bash
   prefect work-pool create --type <infrastructure-type> my-dask-work-pool

Replace

<infrastructure-type>

with the type of infrastructure that can execute runs from this work pool. 2. Configure the Dask Task Runner: To configure the Dask task runner, pass the address of the Dask scheduler to the

DaskTaskRunner

. You can also use adaptive scaling by specifying

adapt_kwargs

. Here’s an example of configuring the Dask task runner within a flow:

python
   from prefect import flow, task
   from prefect_dask import DaskTaskRunner

   @task
   def my_task():
       print("Executing task...")

   @flow(task_runner=DaskTaskRunner(address="<tcp://scheduler-address:8786>"))
   def my_flow():
       my_task()

   my_flow()

3. Run the Work Pool: Start a worker that connects to the work pool:

bash
   prefect worker start -p my-dask-work-pool

For more detailed information, refer to the relevant Prefect documentation: - Work Pools: https://docs.prefect.io/latest/concepts/work-pools/ - Task Runners: https://docs.prefect.io/latest/concepts/task-runners/ You can also check the issues related to Dask on GitHub for common problems and solutions: - Flow cancellation not deleting Dask Scheduler & Worker pods - Adaptive Dask Executor workers not scaling up - Purge old Dask futures

@Marvin what type of infrastructure should I use? I am running the Prefect UI on a self-hosted VM, and deploying Dask on an HPC cluster

Given your setup with a self-hosted Prefect UI on a VM and Dask running on an HPC cluster, you can approach the infrastructure configuration as follows: 1. Infrastructure Type: For executing runs from your work pool, you should use the custom infrastructure type tailored to your specific environment. This can include configurations to connect with your HPC cluster. 2. Creating a Work Pool: Create a work pool specifying custom infrastructure:

bash
   prefect work-pool create --type custom my-hpc-dask-pool

3. Configuring the Dask Task Runner: Ensure the Dask task runner connects to the scheduler address of your HPC cluster.

python
   from prefect import flow, task
   from prefect_dask import DaskTaskRunner

   @task
   def my_task():
       print("Executing task...")

   @flow(task_runner=DaskTaskRunner(address="<tcp://scheduler-address:8786>"))
   def my_flow():
       my_task()

   my_flow()

4. Starting the Worker: Start a worker that connects to the HPC cluster:

bash
   prefect worker start -p my-hpc-dask-pool

This setup enables your self-hosted Prefect UI to manage tasks executed on your HPC cluster. For further details, refer to: - Work Pools: Prefect Work Pools - Hosting Prefect: Host a Prefect Server

@Marvin How do I then run my flow on the worker?

To run your flow on your configured worker, follow these steps: 1. Deploy Your Flow: First, create a deployment for your flow. This tells Prefect how and where to run your flow.

python
   from prefect.deployments import Deployment
   from your_flow_module import my_flow  # Import your flow

   deployment = Deployment.build_from_flow(
       flow=my_flow,
       name="my-flow-deployment",
       work_pool_name="my-hpc-dask-pool"  # Use the name of your work pool
   )

   deployment.apply()

2. Run the Worker: Start a worker that connects to the work pool:

bash
   prefect worker start -p my-hpc-dask-pool

3. Trigger the Flow Run: You can trigger a flow run from the Prefect UI or the CLI. To use the CLI:

bash
   prefect deployment run my-flow-deployment

This setup ensures that your flow is deployed to the specified work pool and can be executed by the worker. For additional guidance on flow deployments and using workers, refer to the relevant Prefect tutorials: - Workers: https://docs.prefect.io/latest/tutorial/workers/ - Deployments: https://docs.prefect.io/latest/concepts/deployments/ The Prefect async workflow example can provide additional context on deploying and running flows.

@Marvin I got this error:

Unknown work pool type 'custom'. Please choose from azure-container-instance, block, cloud-run, cloud-run-v2,
docker, ecs, kubernetes, prefect-agent, process, vertex-ai.

The

custom

work pool type isn't recognized by Prefect. Based on your setup running Prefect UI on a self-hosted VM and deploying Dask on an HPC cluster, you might want to use the

process

work pool type for executing on local infrastructure or the

kubernetes

type if you're using Kubernetes for your Dask cluster management. Here's how to proceed: 1. Create a Work Pool: If you're using local infrastructure, use the

process

type:

bash
   prefect work-pool create --type process my-hpc-dask-pool

If you're using Kubernetes, use the

kubernetes

type:

bash
   prefect work-pool create --type kubernetes my-hpc-dask-pool

2. Deploy Your Flow: Create a deployment for your flow specifying the correct work pool:

python
   from prefect.deployments import Deployment
   from your_flow_module import my_flow  # Import your flow

   deployment = Deployment.build_from_flow(
       flow=my_flow,
       name="my-flow-deployment",
       work_pool_name="my-hpc-dask-pool"
   )

   deployment.apply()

3. Run the Worker: Start a worker that connects to the specified work pool:

bash
   prefect worker start -p my-hpc-dask-pool

4. Trigger the Flow Run: Trigger the flow run from the Prefect UI or via CLI:

bash
   prefect deployment run my-flow-deployment

Choose the

process

type if you are running on self-hosted VMs and the

kubernetes

type if you are leveraging Kubernetes on your HPC cluster. For more details on work pools and workers, refer to: - Work Pools: https://docs.prefect.io/latest/concepts/work-pools/ - Deployments: https://docs.prefect.io/latest/concepts/deployments/