How to use logging in Prefect - a tutorial by Andrew Brookins

This blog post includes:

  • logging in tasks and flows
  • filtering logs in the UI
  • setting extra loggers using environment variable PREFECT_LOGGING_EXTRA_LOGGERS
  • formatting logs
  • custom log configuration via a YAML file

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Very useful thanks. I have some questions about logging in prefect2:

  • can I use the standard logging.getLogger() instead of get_run_logger()? This seems to work via the root log configuration. only difference seems to be it does not have task/flow metadata but is it likely to cause other issues?
  • can i do log=logging.getLogger() at the top of the file to define a log for all tasks? This won’t work with get_run_logger as it may not be in a task/flow when imported. It does seem to work so far but I am not sure if it will be a problem sometimes when multiprocessing.
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Yes, you can! But this is considered an extra logger, so you would need to add it to your prefect configuration e.g., as an environment variable. Check this usage example:

Yes, exactly as shown in the example. This is true only for the extra logger, though. Defining Prefect logger globally wouldn’t work - see this topic:


this all works fine for sequentialtaskrunner. However it does not seem to format the output for dask or ray. Output I get and test code is below. What am I missing?

[I know there is a known issue which prevents it showing on orion but here I am talking about console output.]


11:15:29.331 | WARNING | Task run 'testtask-3dde1f43-0' - warning hi
11:15:29.333 | WARNING | root - warning std root log
11:15:29.335 | WARNING | test1 - warning extra log


warning hi
warning std root log
warning extra log


(begin_task_run pid=6347) warning hi
(begin_task_run pid=6347) warning std root log
(begin_task_run pid=6347) warning extra log
import os


from prefect.flows import flow
from prefect.tasks import task

from prefect_dask.task_runners import DaskTaskRunner as runner

# from prefect.task_runners import SequentialTaskRunner as runner
# from prefect_ray.task_runners import RayTaskRunner as runner
from prefect import get_run_logger
import logging

def testflow():

def testtask():
    log = get_run_logger()
    log.warning("warning hi")
    log.debug("debug hi")

    log = logging.getLogger()
    log.warning("warning std root log")
    log.debug("debug std root log")

    log = logging.getLogger("test1")
    log.warning("warning extra log")
    log.debug("debug extra log")

if __name__ == "__main__":

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Thanks for sharing the example! I created an issue here:

At the moment, it seems that there’s a bug(?) with dask that only allows workers’ log configurations to be configured only through a ~/.config/dask/distributed.yaml file.

So you can populate that file like this:

  version: 1
    log-format: '%(name)s - %(levelname)s - %(message)s'
      format: "DASK ADMIN %(asctime)s __ %(levelname)-7s __ %(name)s __ %(message)s"
      datefmt: "%H:%M:%S"
      formatter: custom
      class: logging.StreamHandler
      level: INFO
      level: INFO
        - console

And this:

import dask
from prefect import flow, task, get_run_logger
from prefect_dask import DaskTaskRunner

def lazy_exponent(args):
    logger = get_run_logger()
    x, y = args
    result = x**y
    # the logging call to keep tabs on the computation
    logger.warning(f"Computed exponent {x}^{y} = {result}")
    return result

def test_flow():
    inputs = [[1, 2], [3, 4]]
    results = []
    for i in inputs:
    return results


Should output:

17:18:53.170 | INFO    | prefect.engine - Created flow run 'encouraging-frog' for flow 'test-flow'
17:18:53.171 | INFO    | Flow run 'encouraging-frog' - Using task runner 'DaskTaskRunner'
17:18:53.173 | INFO    | prefect.task_runner.dask - Creating a new Dask cluster with `distributed.deploy.local.LocalCluster`
17:18:53.653 | WARNING | bokeh.server.util - Host wildcard '*' will allow connections originating from multiple (or possibly all) hostnames or IPs. Use non-wildcard values to restrict access explicitly
17:18:55.548 | INFO    | prefect.task_runner.dask - The Dask dashboard is available at
17:18:55.946 | INFO    | Flow run 'encouraging-frog' - Created task run 'lazy_exponent-fddbc240-0' for task 'lazy_exponent'
17:18:56.272 | INFO    | Flow run 'encouraging-frog' - Created task run 'lazy_exponent-fddbc240-1' for task 'lazy_exponent'
DASK ADMIN 17:18:58 __ WARNING __ prefect.task_runs __ Computed exponent 1^2 = 1
WARNING:prefect.task_runs:Computed exponent 1^2 = 1
DASK ADMIN 17:18:58 __ WARNING __ prefect.task_runs __ Computed exponent 3^4 = 81
WARNING:prefect.task_runs:Computed exponent 3^4 = 81
DASK ADMIN 17:18:58 __ INFO    __ prefect.task_runs __ Finished in state Completed()
INFO:prefect.task_runs:Finished in state Completed()
DASK ADMIN 17:18:58 __ INFO    __ prefect.task_runs __ Finished in state Completed()
INFO:prefect.task_runs:Finished in state Completed()
17:19:00.407 | INFO    | Flow run 'encouraging-frog' - Finished in state Completed('All states completed.')
[Completed(message=None, type=COMPLETED, result=1, task_run_id=897ebb3b-9c28-4ff5-9eff-10e1d61c2af9),
 Completed(message=None, type=COMPLETED, result=81, task_run_id=42ead4a3-d1e2-4c5c-b822-1e31cc35ad4a)]
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This partly works. I read the logging.yml file in and put it under a “logging” key and saved it in the dask config folder. It formats the logs correctly. However the “extra” loggers are at level WARNING even though I set the level as INFO which works in a sequential runner.

I tried DaskTaskRunner(cluster_kwargs=dict(env=dict(PREFECT_LOGGING_EXTRA_LOGGERS=“mylog”))

I believe you would need to configure your extra logger level on the extra logger itself. Afaik the log level set with PREFECT_LOGGING_LEVEL is only for the Prefect logger

Yes I set the extra logger as info. It show as info on sequential runner but not in dask

Ahh that must be somehow configured on Dask probably. I don’t know how though. Keep us posted if you find out more

OK. I have now done that using code below. It now handles logs as expected to both console and orion. However I suggest this should be done by Prefect2. Also something similar will be needed for Ray which also does not handle extra loggers.

def setup_dask_logging():
    # read log settings
    with open(os.environ["PREFECT_LOGGING_SETTINGS_PATH"]) as f:
        logset =
    for x in set(re.findall("\${.*}", logset)):
        logset = logset.replace(x, os.environ.get(x[2:-1], "INFO"))
    logset = yaml.safe_load(logset)

    # set extra loggers
    extra_settings = logset["loggers"]["prefect.extra"].copy()
    extras = [
        for x in os.environ.get("PREFECT_LOGGING_EXTRA_LOGGERS", "").split(",")
    for extra in extras:
        logset["loggers"][extra] = extra_settings

    # TODO update rather than overwrite?
    # save and copy to dask location
    HOME = os.path.expanduser("~")
    PREFECTX = os.path.abspath(os.path.dirname(__file__))
    DASK_SRC = f"{PREFECTX}/logging_daskdistributed.yaml"
    DASK_TGT = f"{HOME}/.config/dask/distributed.yaml"
    with open(DASK_SRC, "w") as f:
    os.makedirs(os.path.dirname(DASK_TGT), exist_ok=True)
    shutil.copy(DASK_SRC, DASK_TGT)

Wow, this looks complicated and definitely too much boilerplate if you would have to do that in every flow. Let me open an issue

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