@Marvin Heya! So we started getting silent errors and no output from Tensorflow models that we're training with Prefect - I tried this

prefect config set PREFECT_LOGGING_EXTRA_LOGGERS="tensorflow,dask,scipy,pandas,numpy,keras,scikeras,sklearn"

but it doesn't fix it. Is there an equivalent of the Prefect 1 option

log_stdout

? I really just want Prefect to log everything that would normally appear on the screen.

To capture all print outputs and log them in Prefect 2.x and 3.x, you can use the

log_prints

parameter at both the flow and task levels. This parameter logs print statements to Prefect's logger. ### Example for Prefect 2.x Here's an example of how to use it in Prefect 2.x:

python
from prefect import task, flow

@task(log_prints=True)
def my_task():
    print("This will be logged.")

@flow(log_prints=True)
def my_flow():
    print("Flow print statement will be logged.")
    my_task()

my_flow()

### Example for Prefect 3.x For Prefect 3.x, the syntax is quite similar:

python
from prefect import flow

@flow(log_prints=True)
def my_flow():
    print("This will be logged.")
    # Add your tasks here

my_flow()

This should resolve the issue of silent errors and no output by ensuring that all print statements within your tasks and flows are properly logged. For further details, check out the respective documentation: - Prefect 2.x: Prefect Logging Guide - Prefect 3.x: Prefect Logging Configuration If the issue persists, please let me know!

@Marvin That didn't fix it

Alright, let's dive a bit deeper. Since

log_prints

didn't resolve the issue, we might need to configure the logging settings more specifically to capture all outputs, including those from TensorFlow. Let's ensure your environment is fully configured to handle logs from all necessary sources. In addition to setting

log_prints

, you can configure the root logger level and specific logger levels for libraries. ### Step-by-Step Logging Configuration 1. Set Environment Variables: Set environment variables to configure the logging levels for each library you're interested in. 2. Modify Flow and Task Definitions: Ensure

log_prints

is set for both flow and tasks. ### Example for Prefect 2.x

python
import os
from prefect import task, flow
import logging

# Ensure environment variables are set for logging
os.environ["PREFECT_LOGGING_ROOT_LEVEL"] = "DEBUG"
os.environ["PREFECT_LOGGING_LOGGERS_TENSORFLOW_LEVEL"] = "DEBUG"

@task(log_prints=True)
def my_task():
    print("Task print statement will be logged.")
    import tensorflow as tf
    tf.get_logger().setLevel('DEBUG')
    # Example TensorFlow usage
    print(tf.constant("TensorFlow log test"))

@flow(log_prints=True)
def my_flow():
    print("Flow print statement will be logged.")
    my_task()

my_flow()

### Example for Prefect 3.x

python
import os
from prefect import flow
import logging

# Ensure environment variables are set for logging
os.environ["PREFECT_LOGGING_ROOT_LEVEL"] = "DEBUG"
os.environ["PREFECT_LOGGING_LOGGERS_TENSORFLOW_LEVEL"] = "DEBUG"

@flow(log_prints=True)
def my_flow():
    print("Flow print statement will be logged.")
    import tensorflow as tf
    tf.get_logger().setLevel('DEBUG')
    # Example TensorFlow usage
    print(tf.constant("TensorFlow log test"))

my_flow()

### Environment Variables Configuration Add these environment variables to your system or Prefect configuration:

bash
export PREFECT_LOGGING_ROOT_LEVEL="DEBUG"
export PREFECT_LOGGING_LOGGERS_TENSORFLOW_LEVEL="DEBUG"
export PREFECT_LOGGING_LOGGERS_PANDAS_LEVEL="DEBUG"
# Add other libraries as needed

Finally, ensure that the TensorFlow logger is configured correctly within your flow or task. If this still doesn't capture all TensorFlow logs, it might be helpful to directly configure the TensorFlow logger within the task or flow itself, as shown in the examples above. If the problem persists, please share any additional error messages or behaviors you observe. This will help in further diagnosing the issue.