@Marvin how can I set a timeout on tasks when using a DaskTaskRunner?

To set a timeout on tasks when using a

DaskTaskRunner

, you can set the

timeout

attribute in the task decorator. This attribute specifies the maximum number of seconds a task should be allowed to run. If the task exceeds this amount of time, an exception will be raised and the task will be marked as failed. Here is an example of setting a timeout for a task in Prefect:

python
from prefect import flow, task
from prefect.task_runners import DaskTaskRunner

@task(timeout=30)  # timeout set for 30 seconds
def example_task():
    # Task implementation
    ...

@flow(task_runner=DaskTaskRunner())
def my_flow():
    result = example_task()

if __name__ == "__main__":
    my_flow()

Note that the

timeout

is specified in seconds in the

@task

decorator. The documentation and examples provided do not directly address setting a task timeout with

DaskTaskRunner

. However, setting a timeout is a core feature of tasks in Prefect and the concept should be the same regardless of the task runner used. If you choose to use adaptive scaling with

DaskTaskRunner

, you pass

adapt_kwargs

with minimum and maximum worker settings to the runner, but that does not involve setting task timeouts. For detailed information on

DaskTaskRunner

, refer to the Prefect documentation on Task Runners. For other code examples, you can look at the repository here, although it does not include a timeout example. The

DaskTaskRunner

details in the documentation will provide you with additional configuration options for using Dask, such as connecting to an existing cluster and configuring adaptive scaling, which might be helpful for your overall setup but are not directly related to setting timeouts on tasks.

@Marvin what is adaptive scaling with

DaskTaskRunner

?

Adaptive scaling with the

DaskTaskRunner

in Prefect allows a Dask cluster to automatically adjust the number of worker processes it uses based on the current workload. This helps to optimize resource usage by scaling up to add more workers when there are many tasks to be processed, and scaling down to fewer workers when the workload is lighter. This way, you don't have to manually manage the number of workers for your flows or worry about having idle resources consuming your infrastructure budget unnecessarily. To use adaptive scaling with the

DaskTaskRunner

, you can configure it by passing

adapt_kwargs

when you instantiate the

DaskTaskRunner

. Within

adapt_kwargs

, you can specify a minimum and maximum number of workers that you want to allow your Dask cluster to use. Here's an example of how to set this up:

python
from prefect import flow, task
from prefect.task_runners import DaskTaskRunner

@task
def my_task():
    # Task implementation

@flow(task_runner=DaskTaskRunner(adapt_kwargs={"minimum": 2, "maximum": 10}))
def my_flow():
    # Your flow definition

if __name__ == "__main__":
    my_flow()

In the example above, the

DaskTaskRunner

is configured to scale adaptively with at least 2 workers (minimum) and up to 10 workers (maximum) based on the demand of the running tasks. For more information, you might want to consult the Prefect documentation on Task Runners, where the

DaskTaskRunner

and adaptive scaling are explained in more detail.

@Marvin timeout does not seem to be an option for

@task

-- I got the following error:

TypeError: task() got an unexpected keyword argument 'timeout'

It seems there has been some confusion. The

timeout

keyword argument I mentioned earlier does not apply to the

@task

decorator as you rightly pointed out from the error message. I apologize for any inconvenience. As for setting timeouts on tasks, it looks like Prefect Core's concept of task timeouts does not directly translate to Prefect 2.0 (

Orion

). There might be other ways to handle timeouts within a task's execution function or by using control structures within the flow, potentially relying on Dask's own timeout mechanisms if you are using

DaskTaskRunner

. Moreover, I found an issue on Prefect's GitHub regarding

timeout_seconds

not being enforced with

RayTaskRunner

. While this is a different task runner, the issue may indicate that timeouts are handled differently or not yet fully implemented in certain runners or versions of Prefect. To implement a timeout within a task manually, one could use Python's standard library features like `concurrent.futures`'

TimeoutError

. Here's an example:

python
import concurrent.futures
import time

from prefect import flow, task
from prefect.task_runners import DaskTaskRunner

@task
def my_task_with_timeout():
    with concurrent.futures.ThreadPoolExecutor() as executor:
        future = executor.submit(time_consuming_function)  # Replace this with your long-running function
        try:
            result = future.result(timeout=30)  # 30 seconds timeout
            return result
        except concurrent.futures.TimeoutError:
            raise TimeoutError("Task execution exceeded the timeout limit.")

@flow(task_runner=DaskTaskRunner())
def my_flow():
    result = my_task_with_timeout()

def time_consuming_function():
    time.sleep(60)  # This function would normally run for 60 seconds

if __name__ == "__main__":
    my_flow()

However, implementing timeouts in that way may not take full advantage of the DaskTaskRunner's features and behavior. Please do refer to the Prefect documentation for the most accurate and up-to-date information regarding timeouts as they might be addressed in updates or plugins. It might also be productive to inquire on Prefect's GitHub or community forums for solutions specific to Prefect 2.0 features.