@Marvin when defining a prefect task, you can define a retry delays in seconds, which is a delay between the next retry attempt after a task failure. Is there a way to set this delay time dynamically, referencing the task parameters or exception object?

You can indeed dynamically set the retry delays for a Prefect task. Prefect allows you to pass a callable to the

retry_delay_seconds

parameter. This callable can accept the number of retries as an argument and return a list of retry delays. Here’s a relevant excerpt from the Prefect documentation: Additionally, you can pass a callable that accepts the number of retries as an argument and returns a list. Prefect includes an exponential_backoff utility that will automatically generate a list of retry delays that correspond to an exponential backoff retry strategy. Here's an example of using an exponential backoff retry strategy:

from prefect import task
from prefect.tasks import exponential_backoff

@task(retries=3, retry_delay_seconds=exponential_backoff(backoff_factor=10))
def some_task_with_exponential_backoff_retries():
    ...

Additionally, you might want to add jitter to the delay times to avoid scenarios where many tasks retry simultaneously. If you want to reference the task parameters or exception object, you would need to define a custom callable that generates delays based on these values. Example:

from prefect import task
import random

def custom_retry_delays(exception, retries):
    if isinstance(exception, specific_exception_type):
        return [2, 4, 6, 8]
    else:
        return [random.uniform(1, 3) for _ in range(retries)]

@task(retries=3, retry_delay_seconds=lambda retries: custom_retry_delays(exception_object, retries))
def my_task():
    ...

For detailed information, you can check the full documentation here. Make sure to adapt the

custom_retry_delays

function to your specific use case.

@Marvin But there's no such example of custom retry delays function that takes an exception as a parameter in the documentation you provided

You're right. Prefect's current documentation does not directly support a custom retry delay function that dynamically takes an exception as a parameter. However, you can work around this limitation by implementing custom logic within the task's retry handler. You can use a workaround by handling exceptions within the task and adjusting the retry behavior externally. Below is an example of how you might implement custom retry logic within a Prefect task:

from prefect import task, flow
from prefect.exceptions import FailedRun

def calculate_retry_delay(exception, attempt):
    if isinstance(exception, ZeroDivisionError):
        return attempt * 2  # Exponential backoff for specific exceptions
    return 5  # Default delay

@task(retries=3)
def my_task(param):
    try:
        # Your task logic here
        if param == 'error':
            raise ZeroDivisionError("Example error")
    except Exception as e:
        delay = calculate_retry_delay(e, my_task.run_context.run_count)
        raise FailedRun(f"Retry delay: {delay} seconds") from e

@flow
def my_flow():
    my_task('error')

my_flow()

In this example: 1. The

calculate_retry_delay

function determines the delay based on the exception type and retry attempt. 2. The

my_task

function includes error handling logic, adjusting its behavior based on the exception raised. 3. Use

my_task.run_context.run_count

to access the current retry attempt count and calculate the appropriate delay. This pattern isn't direct and doesn't modify the

retry_delay_seconds

attribute dynamically, but it allows you to implement custom logic to manage retries conditionally. For dynamically managing retry delays directly as per Prefect's capabilities, you typically need to work within provided configurations and known patterns, such as exponential backoff. For the most accurate, detailed, and up-to-date information, check the Prefect documentation. If you need more assistance or specific examples, the Prefect community is responsive to feature requests and improvements on GitHub - creating an issue might influence future updates to the library.