Based on the research and GitHub issues, I can provide a clear but somewhat disappointing answer: 1. Regarding canceling individual tasks that are still running after flow completion: - Prefect does not currently support direct cancellation of individual tasks - The primary cancellation mechanism works at the flow run level, not the task level - There isn't a built-in way to cancel specific tasks that are still running after a flow is marked as completed 2. Regarding canceling tasks by tag name: - This is not currently supported - While Prefect does support tags for tasks and can use them for concurrency limits, there's no direct cancellation functionality by tag The closest available options are: 1. Cancel the entire flow run using either: - The Prefect UI - The CLI command:

prefect flow-run cancel [FLOW_RUN_ID]

2. As a workaround for future runs, you could: - Implement timeout mechanisms in your tasks - Use task-level error handling - Structure your flows to have better isolation between tasks There is an open feature request (https://github.com/PrefectHQ/prefect/issues/18298) for individual task cancellation, which suggests this is a recognized need in the community. If you're experiencing this issue frequently, I'd recommend: 1. Investigating why tasks continue running after flow completion (this isn't typical behavior) 2. Adding robust timeout mechanisms to your tasks 3. Considering restructuring your flow to have better task isolation 4. If needed, you can upvote or comment on the GitHub issue to show support for individual task cancellation functionality Would you like me to provide more details about implementing any of these workarounds or about flow-level cancellation mechanisms?

Based on the research and GitHub issues, I can provide a clear answer: It is not possible to cancel a flow run that is already marked as "Completed" in Prefect Cloud UI. This is because: 1. The "Completed" state is a terminal state in Prefect's state machine 2. Terminal states (like "Completed", "Failed", "Cancelled") are final and cannot be changed 3. The cancellation functionality is only available for active flow runs that are still in progress If you're seeing tasks still running even though the flow run is marked as "Completed", this is actually an unusual situation that shouldn't occur under normal circumstances. You might want to: 1. Check your flow's structure to ensure tasks aren't running independently of the flow's lifecycle 2. Review your task implementation for any background processes or threads that might continue after the flow completes 3. Consider opening a GitHub issue if you can reproduce this behavior consistently, as it might indicate a bug For future flow runs, you can cancel active runs using: - The Prefect UI (while the flow is still running) - The CLI command

prefect flow-run cancel [FLOW_RUN_ID]

- The Python API through a Prefect client Would you like me to provide more details about how to prevent tasks from running after flow completion, or about best practices for flow run lifecycle management?

Based on the research and issues, I can provide some operational solutions for your specific scenario with zombie tasks occupying concurrency slots. Here are the available options, in order of effectiveness: 1. Delete and Reset Concurrency Limits

bash
   # First, delete the existing concurrency limit for the tag
   prefect concurrency-limit delete -t "your-tag-name"
   
   # Then create it again with the desired limit
   prefect concurrency-limit create -t "your-tag-name" -v your-limit

This is the most direct way to free up the slots, though it's a bit of a sledgehammer approach. 2. Set Temporary Zero Limit

bash
   # Set the limit to 0 temporarily
   prefect concurrency-limit create -t "your-tag-name" -v 0
   
   # After tasks are cleared, reset to desired limit
   prefect concurrency-limit create -t "your-tag-name" -v your-limit

This can force-terminate tasks using that tag's concurrency slots. 3. Inspect Current Usage

bash
   prefect concurrency-limit inspect -t "your-tag-name"

This will show you exactly which task runs are occupying the slots, which can be useful for debugging or more targeted cleanup. Important Operational Notes: - These solutions are focused on freeing up the concurrency slots, not necessarily stopping the actual running processes - The underlying processes might continue running even after freeing the concurrency slots - If you're running in a container orchestration system like Kubernetes, you might need to also clean up the corresponding pods/containers - These are emergency operational fixes - once the immediate issue is resolved, it would be worth investigating why tasks are outliving their parent flows Would you like me to provide more details about any of these approaches or explain how to verify that the slots have been properly freed?