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It looks like the errors you're encountering are related to task timeouts and cancellation in Prefect. Essentially, a task exceeded its allowed runtime and was subsequently cancelled. Let's break down the errors: 1. CancelledError: This indicates that a task was cancelled. The cancellation could occur for various reasons, including manual cancellation, a timeout, or upstream dependencies failing. 2. TimedOut: This error states that the task run exceeded its specified timeout limit (500 seconds in your case) and thus was terminated with a TimeoutError. To understand why you're getting these errors and to potentially resolve them, I'll look up relevant information from the Prefect documentation and GitHub issues. This will provide insight into handling and mitigating such errors. I’ll start by querying the Prefect documentation for details on task timeouts and cancellations, and then I'll search GitHub issues for any related occurrences or solutions. Please hold on for a moment. Your errors stem from a task timing out and being cancelled in Prefect. Here's a summary of what I found: 1. Task Cancellation and Timeout: Cancellation occurs if the task exceeds the specified runtime. In your case, it’s 500 seconds. The

CancelledError

is raised when the task cannot complete within the time limit, leading to a termination. 2. Timeout Settings: You can set timeouts at the task level using the

timeout_seconds

parameter in the task decorator. If you need to dynamically adjust the timeout based on parameter values, you can do so using the code example below. ### Example Usage of Timeout in Tasks Here's an example snippet on setting a task timeout:

python
from prefect import task, flow

@task(timeout_seconds=500)
def long_running_task():
    # simulate long running task
    import time; time.sleep(600)
    return "Task Completed"

@flow
def my_flow():
    long_running_task()

if __name__ == "__main__":
    my_flow()

### Dynamic Timeout To dynamically adjust `timeout_seconds`:

python
from prefect import task, flow

@task
def dynamic_task(duration):
    import time; time.sleep(duration)
    return f"Task ran for {duration} seconds"

@flow
def my_flow():
    dynamic_task(timeout_seconds=300).submit(200)  # 200 seconds
    dynamic_task(timeout_seconds=600).submit(500)  # 500 seconds

if __name__ == "__main__":
    my_flow()

### Relevant Documentation - Flow Cancellation - Task Timeouts ### GitHub Issues - CancelledError and Timeout Handling - Handling Task Timeouts Dynamically You can mitigate such issues by adjusting timeouts appropriately or optimizing the task execution times to fit within specified limits. If you run into more issues, feel free to ask. I'd be "delighted" to help again.