Based on the research and issue analysis, there are several potential causes and solutions for your DaskTaskRunner task re-running issue: 1. Resource Constraints - If your task is memory or CPU-intensive, resource limitations might cause the task to fail and restart - Solution: Monitor your resource usage and consider increasing available resources or optimizing your task's resource consumption 2. Task State Management - The issue might be related to how task states are being managed, especially if the task is long-running - Solution: Ensure your Dask cluster has enough workers and resources to handle the task load 3. Common Fixes: - Reduce data transfer between tasks if you're passing large amounts of data - Ensure your Dask cluster configuration is appropriate for your workload - Consider implementing proper error handling in your task - Check if your task is properly releasing resources after completion To help diagnose and fix the issue, could you share: 1. A minimal example of your task code? 2. The configuration you're using for the DaskTaskRunner? 3. Are you seeing any specific error messages in the logs? This would help me provide more specific guidance for your situation. Also, if you're working with particularly large datasets or compute-intensive operations, that would be useful information to know. For reference, you can find more details about DaskTaskRunner configuration in the Prefect-Dask documentation.

@Marvin is it possible that my DasktaskRunner is timing out?

Yes, timeout issues can definitely cause the behavior you're seeing with DaskTaskRunner. There are several timeout-related settings to consider: 1. Task-Level Timeout

python
   from prefect import task
   
   @task(timeout_seconds=3600)  # 1 hour timeout
   def my_task():
       ...

2. DaskTaskRunner Timeout Settings

python
   from prefect_dask import DaskTaskRunner
   
   runner = DaskTaskRunner(
       cluster_kwargs={"n_workers": 2},
       client_kwargs={"timeout": 3600}  # 1 hour timeout for client operations
   )

3. Dask's Internal Timeouts - Dask has its own timeout settings for various operations - Default task timeout in Dask is typically around 5 minutes To diagnose if timeouts are your issue: 1. Check your task logs for any timeout-related messages 2. Monitor the Dask dashboard (if enabled) during execution 3. Try increasing the timeouts using the settings above 4. Add logging to your task to see where it might be getting cut off Here's a more robust way to set up your flow with extended timeouts:

python
from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task(timeout_seconds=3600)  # Adjust based on your needs
def your_task():
    ...

@flow(
    task_runner=DaskTaskRunner(
        cluster_kwargs={"n_workers": 2},
        client_kwargs={"timeout": 3600}
    )
)
def your_flow():
    return your_task()

If you're still seeing issues after adjusting timeouts, it would be helpful to: 1. Add some logging statements in your task to see exactly where it's restarting 2. Check your task's memory usage to ensure it's not being killed by resource constraints 3. Monitor the Dask dashboard to see worker status during execution Would you be able to share how long your task typically runs before it gets restarted? This would help determine if it's hitting a specific timeout threshold.