There are two main ways to set the log level to debug for a deployment: 1. Using Environment Variables (Recommended):

bash
export PREFECT_LOGGING_LEVEL=DEBUG

You can set this in your deployment environment or include it in your deployment configuration. 2. Using Python Code:

python
from prefect import flow
from prefect.logging import get_run_logger

@flow
def my_flow():
    logger = get_run_logger()
    logger.setLevel("DEBUG")
    logger.debug("This is a debug message")

If you're using a deployment configuration file (yaml), you can include the environment variable:

yaml
deployments:
  - name: my-deployment
    flow_name: my-flow
    env:
      PREFECT_LOGGING_LEVEL: DEBUG

The environment variable approach is generally preferred as it allows you to modify logging behavior without changing your code. You can find more detailed information about logging customization in the documentation. Remember that DEBUG level logging will produce significantly more output than the default INFO level, so use it judiciously, typically for development or troubleshooting purposes.

Based on the research, here's why your custom logging.yml file isn't working in Prefect Cloud and how to fix it: 1. Root Cause: When running in Prefect Cloud, the logging configuration file needs to be available in the execution environment where your flows actually run. Your local

logging.yml

file isn't automatically transferred to the cloud execution environment. 2. Solutions: A. Use Environment Variables (Recommended Approach): Instead of using a logging.yml file, set the log levels using environment variables in your deployment configuration:

yaml
   deployments:
     - name: your-deployment
       flow_name: your-flow
       env:
         PREFECT_LOGGING_LEVEL_PREFECT_FLOW_RUNS: DEBUG
         PREFECT_LOGGING_LEVEL_PREFECT_TASK_RUNS: DEBUG

B. Package the Logging Configuration: If you really need to use the YAML file, you'll need to: 1. Include the logging.yml file in your project 2. Set the

PREFECT_LOGGING_CONFIG_PATH

to point to its location relative to your flow code 3. Ensure the file is deployed along with your flow code 3. Best Practices: - Use environment variables for cloud deployments as they're more reliable and easier to manage - Keep in mind that excessive DEBUG logging in cloud environments can impact performance - You can verify the logging configuration is applied by checking the first few flow runs and confirming the debug messages appear 4. Verification: To verify your logging configuration is working, you can add a simple debug message at the start of your flow:

python
   from prefect import flow
   from prefect.logging import get_run_logger

   @flow
   def my_flow():
       logger = get_run_logger()
       logger.debug("Debug logging test")  # This should appear if configured correctly

Remember that while debug logging can be valuable for troubleshooting, it generates significantly more log data, which could impact performance and storage in a cloud environment. Use it judiciously and consider turning it off once you've resolved any issues you're investigating.

Based on the research, here's the correct way to enable DEBUG level logging in Prefect Cloud: 1. Set the Base Logging Level: Instead of setting specific loggers, set the base logging level to DEBUG:

yaml
   deployments:
     - name: your-deployment
       flow_name: your-flow
       env:
         PREFECT_LOGGING_LEVEL: DEBUG

2. Enable Log Prints in Your Flow: Make sure your flow and tasks are configured to capture prints:

python
   from prefect import flow, task, get_run_logger

   @task(log_prints=True)
   def my_task():
       logger = get_run_logger()
       logger.debug("Debug message from task")
       print("This will also be captured")

   @flow(log_prints=True)
   def my_flow():
       logger = get_run_logger()
       logger.debug("Debug message from flow")
       my_task()

3. Verify Configuration: You can verify the logging level is set correctly by adding a debug message at the start of your flow:

python
   @flow(log_prints=True)
   def my_flow():
       logger = get_run_logger()
       logger.debug("If you see this, DEBUG logging is enabled")

If you're still not seeing DEBUG logs in the UI after implementing these changes, you might need to: 1. Ensure the worker running your flow has the environment variable set 2. Check if there are any proxy or intermediary services that might be filtering logs 3. Verify that the flow is actually generating debug logs by checking the worker's local logs Remember that excessive DEBUG logging can impact performance and generate large amounts of log data, so use it judiciously in production environments.