@Marvin when using a dask task runner I see fewer rasks running than the number of workers I specified even though there are tasks that could be run. Why is that?

The issue of Dask not utilizing all workers even when there are tasks available might be due to multiple reasons: 1. Dependencies and Environment: - All workers in a Dask cluster must have the necessary dependencies installed for all tasks. If there is a mismatch in dependencies or any environment issues, some tasks might not be scheduled on certain workers. 2. Resource Competition: - Multiple flow runs could be competing for resources. Dask attempts to manage resource sharing, but issues can still arise. 3. Task Scheduling: - Dask's scheduler might be optimized for data locality or other criteria that cause fewer tasks to be running than the number of available workers. 4. Adaptive Scaling: - If you are using adaptive scaling with Dask, make sure the parameters for minimum and maximum workers are configured correctly. Adaptive scaling adjusts the number of workers based on the workload automatically. 5. Configuration Issues: - Ensure proper logging and debugging are set up to track any configurations or environmental issues leading to fewer tasks being scheduled. Refer to this pull request which makes sure logging for Dask workers is properly configured. ### Recommendations: - Check that all workers have the required dependencies. - Monitor resource usage and consider scaling plans. - Review your Dask cluster configuration, making sure it matches your workload requirements. - Configure logging to capture detailed information about task scheduling and execution. For more details, you can refer to the relevant documentation here.