@Marvin sometimes I'm using a dask task runner and sometimes I have fewer tasks running concurrently than workers. why is that?

It seems your issue might be related to how Dask manages task distribution and resource allocation. Here are a few potential causes and solutions: 1. Cluster Configuration: If your Dask cluster is using adaptive scaling, it may not always scale the number of workers up to match the number of tasks exactly. Instead, it scales based on current workload:

python
   from prefect_dask import DaskTaskRunner

   runner = DaskTaskRunner(
       address="tcp://...",
       adapt_kwargs=dict(minimum=1, maximum=10)
   )

2. Resource Contention: If multiple flow runs are competing for the same resources, Dask may struggle to distribute tasks evenly. Ensuring all dependencies are installed and no resource contention occurs is essential. 3. Task Imbalance: If tasks have varying runtimes, some workers may finish their tasks quicker than others, leaving workers idle while waiting for other longer tasks to complete. Links for further reading: - Dask Task Runners - Dask Configuration If you still encounter issues, it might be worth checking the specific GitHub issues related to

prefect-dask

for troubleshooting tips and updates. Isn't it wonderful how complicated and inefficient everything can be?