merlin
03/17/2025, 5:23 PMMarvin
03/17/2025, 5:23 PMPREFECT_LOGGING_ROOT_LEVEL
to DEBUG
. This will configure the root logger to output debug level logs.
2. Configuration File: You can create a logging.yml
file in your PREFECT_HOME
directory (default is ~/.prefect
) or specify a custom path using the PREFECT_LOGGING_SETTINGS_PATH
setting. In this file, you can define the logging configuration, including setting the level to DEBUG
.
3. Using Prefect's Logger: Within your flow, you can use Prefect's get_run_logger()
to obtain a logger that is aware of the current flow or task run context. This allows for more detailed and contextual logging.
Here is a basic example of using get_run_logger()
in a flow:
python
from prefect import flow
from prefect.logging import get_run_logger
@flow
def my_flow():
logger = get_run_logger()
logger.debug("This is a debug message")
my_flow()
For more detailed information about logging configuration, you can refer to the Prefect Logging Documentation.merlin
03/17/2025, 5:25 PMlogger.debug()
. What I want to do is set the logging level to DEBUG using the python SDK, so that all debug messages will be sent to the logger in a particular flow.Marvin
03/17/2025, 5:25 PMDEBUG
for a specific flow using the Prefect Python SDK, you can use the get_run_logger()
function to obtain the logger and then set its level. Here's how you can do it:
python
from prefect import flow
from prefect.logging import get_run_logger
import logging
@flow
def my_flow():
logger = get_run_logger()
logger.setLevel(logging.DEBUG) # Set the logging level to DEBUG
logger.debug("This is a debug message")
my_flow()
This code snippet sets the logging level to DEBUG
for the logger obtained in the flow, ensuring that all debug messages are captured during the flow execution.Bring your towel and join one of the fastest growing data communities. Welcome to our second-generation open source orchestration platform, a completely rethought approach to dataflow automation.
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