@Marvin how can I retry native task, eg run_checkpoint_validation

To implement retries in Prefect for a native task, you can use the

retries

and

retry_delay_seconds

parameters when you define your task with the

@task

decorator. For example, if you want to configure a task to retry up to three times with an exponential backoff, you can do so like this:

python
from prefect import task
from prefect.tasks import exponential_backoff

@task(
    retries=3,
    retry_delay_seconds=exponential_backoff(backoff_factor=10),
    retry_jitter_factor=1,
)
def some_task_with_exponential_backoff_retries():
    # Your task logic here
    ...

In this code,

retries

sets the number of retry attempts,

retry_delay_seconds

sets the delay between retries using an exponential backoff strategy, and

retry_jitter_factor

adds randomness to the retry delay to prevent thundering herds. As for

run_checkpoint_validation

, the information provided isn't sufficient to detail its function. However, it appears to be a specific task in a Prefect flow. Generally, you would enable retries for

run_checkpoint_validation

in the same way as shown above by decorating the task definition with retry configurations. If you need more specific information about

run_checkpoint_validation

or other related aspects, you might consider searching in documentation or Github issues related to this task.

@Marvin I understand how to retry my task, but how can I retry prefect tasks? eg run_checkpoint_validation or s3_download, etc

from prefect_aws.s3 import s3_list_objects, s3_download, s3_upload
from prefect_great_expectations import run_checkpoint_validation

For third-party Prefect tasks, such as those from

prefect_aws

and

prefect_great_expectations

, the process of setting retries is similar to that of native Prefect tasks. Prefect's task configuration approach largely remains consistent across different tasks, whether they are built-in or come from external modules. Here is how you can set retries for any task, including those from

prefect_aws

and `prefect_great_expectations`:

python
from prefect import flow, task, get_run_logger
from prefect_aws.s3 import s3_download
from prefect_great_expectations import run_checkpoint_validation
from prefect.retry import constant_retry_handler

@task(retry_count=3, retry_delay_seconds=5, retry_handler=constant_retry_handler)
def my_custom_task():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Attempting to run my custom task")

@flow
def my_flow():
    # Set retries for s3_download task
    s3_download_task = s3_download.with_options(
        retry_count=2,
        retry_delay_seconds=10,
        retry_handler=constant_retry_handler
    )
    
    # Set retries for run_checkpoint_validation task
    run_checkpoint_validation_task = run_checkpoint_validation.with_options(
        retry_count=2,
        retry_delay_seconds=10,
        retry_handler=constant_retry_handler
    )
    
    # Now, use `s3_download_task` and `run_checkpoint_validation_task` within your flow
    # ... your flow logic ...

# Set global default retries and retry delay if you want to apply these settings to all tasks
# This will not override the retries or retry_delay_seconds set on individual tasks
from prefect import config
config.set_default_flow_settings(retry_count=2, retry_delay_seconds=[1, 10, 100])
config.set_default_task_settings(retry_count=2, retry_delay_seconds=[1, 10, 100])

# Run the flow
my_flow()

Please notice that task specific retry settings take precedence over global defaults, and you may utilize different retry strategies such as

exponential_retry_handler

,

linear_retry_handler

, etc., depending on the desired behavior. You can find more about configuring retry behaviors in Prefect tasks by referring to the Prefect documentation. If you need very specific information, consider searching the prefect_aws and prefect_great_expectations documentation or github repositories for any quirks related to their task retry configurations.