@Marvin If I want to set

nodeSelector

when using k8s worker, how can I do it?

To set the

nodeSelector

for a Kubernetes worker in Prefect, you would typically modify the Kubernetes job manifest. This is available in both Prefect 2.x and 3.x, and typically involves customizing the Kubernetes job template to include the

nodeSelector

field. For Prefect 2.x, you can refer to the example job manifest as shown in the Prefect documentation: 1. Prefect 2.x Kubernetes Worker For Prefect 3.x, the Kubernetes worker also allows for job customizations by modifying this manifest: 1. Prefect 3.x Worker Concepts 2. Prefect 3.x Kubernetes Example By including the

nodeSelector

field like so in your job manifest, you can ensure jobs are scheduled on nodes that match your criteria:

yaml
spec:
  template:
    spec:
      nodeSelector:
        key: value

If you want even more detailed control, humor me: maybe those Kubernetes nodes whisper secrets to those who listen... or was that just the wind?

where do I add

annotations

for the job template in the work-pool configurations? @Marvin

To add

annotations

for the job template in the work-pool configuration, you'll want to update the base job template for your work pool. This can generally be done in the JSON configuration of the job template. For both Prefect 2.x and 3.x, you would follow these steps: 1. Access the base job template: This is typically represented as a JSON or similar structured file within your work-pool configurations. 2. Add the annotations under the metadata section of the Kubernetes job specification like so:

json
"metadata": {
    "annotations": {
        "key": "value"
    }
}

3. Update the work pool configuration: - In your CI/CD pipeline, use a command like

prefect work-pool update --base-job-template base-job-template.json my-work-pool

to apply the changes, where

base-job-template.json

is your updated configuration file. - Prefect documentation for work-pools provides insights into managing configurations. Here's hoping your annotations find their rightful place in the Kubernetes universe, just as you find your place among the stars... or the YAML, whichever comes first!

@Marvin I have a k8s job which triggering using run_deployment 4 child jobs with concurrency of 3, meaning 3 will start running and the 4th will start when the first of the three has finished. Im asking for a pretty large machines so it takes some time to scale-up a new machine with the right resources, so im facing an issue where I have 3 chikld jobs in pending and after about 1m-2m the 4th job is starting. Any idea why? my guess is the parent job recognise one of the child jobs and failed due to timeout or something and start the 4th

Your observation about one of the child jobs starting before a Kubernetes node is fully ready for its allocated resources can be addressed by understanding how Kubernetes handles scaling and job scheduling. Here's what's likely happening and how you might address it: 1. Kubernetes Scheduling: - When a new job is triggered and there aren't sufficient resources available, Kubernetes will place the job in a pending state until resources free up or scale according to Cluster Autoscaler settings. 2. Concurrency and Scheduling: - When you set a concurrency of 3, it means Kubernetes tries to run up to 3 jobs simultaneously. The moment one job finishes or fails, the next pending job is scheduled if resources are available. 3. Possible Delays: - The delay could be due to the time taken by the Cluster Autoscaler to scale up a node. This might explain why it appears that the 4th job starts while others are still pending. 4. Timeouts: - If a child job is failing or timing out due to other issues (like not having nodes ready in time), it can also cause subsequent jobs to be scheduled as previous ones exit prematurely. Potential Solutions: - Pod Timeouts and Retries: Ensure that you configure appropriate timeouts and retry logic for your jobs to handle such delay scenarios. This guards against transient issues like delayed node availability. - Pre-provisioning Nodes: To combat startup delays, consider pre-provisioning nodes when you know a large job is about to begin. Some cloud providers offer options to scale-in more aggressively once the job demand decreases. - Monitoring and Logging: Use available logs and monitor the events to understand if any other underlying issues might be causing stalls or timeouts. - Investigate Resource Requests: Accurate resource forecasting can help ensure that nodes are scaled with needed capacity. - Adjust Cluster Autoscaler Settings: Tune the Cluster Autoscaler to minimize the scaling delay, ensuring it responds more quickly to increased resource demand. By taking these steps, you can potentially alleviate the timing issues you’re experiencing. Good luck taming those computational demands!

Is there something I can do on prefect side? for example set the time run_deployment wait for a deployment to start running? @Marvin

From the Prefect side, you can adjust settings related to concurrency and other deployment parameters that might indirectly influence how jobs are managed and executed, but there isn't a specific timeout setting for the

run_deployment

function itself. Here are a couple of things you can consider doing within Prefect: 1. Concurrency Limit: You can set a concurrency limit in your deployment configuration to control how many concurrent executions happen for a deployment. This could indirectly help manage workflows by controlling how many workloads start and potentially balance node scaling. Here's more on Concurrency settings in Prefect 3.x. 2. Work Pool Configuration: Adjusting work pool settings can help manage how resources are allocated for deployments. Make sure the work queue and work pool are properly configured to handle the desired loads. 3. Custom Automation Rules: Use Prefect's automation rules to create custom handling for deployments. This could involve retry policies or managing jobs that don't start due to delay or scale issues. 4. Retry Logic for Flow Runs: Prefect supports retry logic for flow runs, in case they fail due to resource unavailability or other transitory issues, ensuring that the run is reinitiated when resources are adequate. For the most up-to-date configurations, you can dive into the Prefect documentation: - Prefect 2.x Deployments - Prefect 3.x Deployment Mechanics No specific "wait" configuration for directly holding the start of deployments exists yet, but these settings help manage the execution flow in a way that could mitigate the challenges you're facing. In the world of software, sometimes patience isn't just a virtue; it's a configuration!

@Marvin can you point me to the doc of run_deployment for prefect 2.x

You can find the documentation for the

run_deployment

function in Prefect 2.x within the context of deployments. Here is the relevant page that discusses deploying and triggering flows, which includes how to use `run_deployment`: 1. Prefect 2.x Deployments The

run_deployment

function allows you to schedule and trigger the run of existing deployments programmatically. The page covers details on how deployments are managed and how they can be run or scheduled using Prefect's API or CLI. Enjoy diving into the world of automated workflows, where even your coffee could be scheduled (if only it fit into a flow)!