Based on the research and my knowledge of Prefect, here are several approaches you can use to debug your remote flows: 1. Use the Prefect UI (Easiest) - The Prefect UI provides detailed logging and visualization of your flow runs - You can see the exact parameters passed, execution timeline, and any errors that occur - Access logs in real-time through the UI at

<http://127.0.0.1:4200>

(default) 2. Enhanced Logging

python
   from prefect import flow, task
   import logging

   # Set up logging at the flow level
   logger = logging.getLogger("prefect")
   logger.setLevel(logging.DEBUG)

   @flow
   def your_flow(**kwargs):
       logger.debug(f"Flow parameters: {kwargs}")
       # Your flow code here

3. Remote Debugging with debugpy - This approach allows you to attach a debugger to your remote flow:

python
   from prefect import flow
   import debugpy

   @flow
   def your_flow(**kwargs):
       # Add this at the start of your flow
       debugpy.listen(("0.0.0.0", 5678))
       debugpy.wait_for_client()  # Pause until debugger attaches
       
       # Your flow code here

- In VS Code, create a launch configuration:

json
   {
       "name": "Attach to Remote Flow",
       "type": "python",
       "request": "attach",
       "connect": {
           "host": "localhost",
           "port": 5678
       }
   }

4. Local Development Mode - Instead of running the flow remotely, you can temporarily run it locally for debugging:

python
   # Instead of run_deployment, call the flow directly
   from your_flow_module import your_flow
   
   flow_run = await your_flow(
       cos_item_link=request.cos_item_link,
       contact_id=request.contact_id,
       organization_id=organization_id,
       ideal_customer_profile=ideal_customer_profile,
       user_input=request.user_input
   )

5. Add Structured Logging

python
   from prefect import flow, task
   from prefect.logging import get_run_logger

   @task
   async def process_data(data):
       logger = get_run_logger()
       <http://logger.info|logger.info>(f"Processing data: {data}")
       # Your processing code here

   @flow
   async def your_flow(**kwargs):
       logger = get_run_logger()
       <http://logger.info|logger.info>(f"Starting flow with parameters: {kwargs}")
       # Your flow code here

6. Use Task-Level Debugging - Break down your flow into smaller tasks - Add debugging statements at task boundaries - This makes it easier to identify where issues occur:

python
   @task
   async def validation_task(data):
       logger = get_run_logger()
       logger.debug(f"Validating data: {data}")
       assert data is not None, "Data cannot be None"
       return data

   @flow
   async def your_flow(**kwargs):
       data = await validation_task(kwargs)
       # Continue with processing

For your specific case, I would recommend starting with approach #3 (debugpy) if you need to step through the code, or #1 (Prefect UI) if you just need to monitor execution and see logs. The debugpy approach is particularly powerful because it allows you to: - Set breakpoints in your flow code - Inspect variables during execution - Step through the code line by line - Examine the stack trace in case of errors Remember that when using

serve

, the flow is running in a separate process, so traditional debugging methods might not work directly. That's why using either debugpy or the Prefect UI logging capabilities is particularly important in this context. Would you like me to elaborate on any of these approaches or provide more specific examples for your use case?

Based on the research and my experience, let me provide a step-by-step guide to set up remote debugging with debugpy for your Prefect flow: 1. First, install debugpy:

bash
pip install debugpy

2. Modify your flow code to include the debugpy setup. Here's how to adapt your example:

python
from prefect import flow
import debugpy

@flow
async def computer_vision_flow(
    cos_item_link: str,
    contact_id: str,
    organization_id: str,
    ideal_customer_profile: dict,
    user_input: str
):
    # Set up debugpy at the start of your flow
    # Only enable in development/when needed
    if "ENABLE_DEBUGPY" in os.environ:
        debugpy.listen(("0.0.0.0", 5678))
        print("⏳ Waiting for debugger to attach...")
        debugpy.wait_for_client()
        print("🔍 Debugger attached!")

    # Your existing flow code here
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting computer vision processing...")
    # ... rest of your flow code

3. Set up VS Code for debugging: Create a new launch configuration in `.vscode/launch.json`:

json
{
    "version": "0.2.0",
    "configurations": [
        {
            "name": "Attach to Prefect Flow",
            "type": "python",
            "request": "attach",
            "connect": {
                "host": "localhost",
                "port": 5678
            },
            "pathMappings": [
                {
                    "localRoot": "${workspaceFolder}",
                    "remoteRoot": "." // Adjust if your remote path differs
                }
            ],
            "justMyCode": true
        }
    ]
}

4. Start your flow with debugging enabled:

bash
# Enable debugpy when starting your flow
export ENABLE_DEBUGPY=1
prefect worker start -p your-work-pool

5. Debug your flow: 1. Set breakpoints in your flow code in VS Code 2. Start your flow (via

run_deployment

as in your example) 3. When the flow starts, it will pause at

debugpy.wait_for_client()

4. In VS Code, go to the Run and Debug sidebar (Ctrl+Shift+D) 5. Select "Attach to Prefect Flow" configuration 6. Click the green play button or press F5 7. The debugger will attach, and you can step through your code Important Tips: 1. Conditional Debugging: The environment variable check (

ENABLE_DEBUGPY

) ensures you only enable debugging when needed:

python
if "ENABLE_DEBUGPY" in os.environ:
    debugpy.listen(...)

2. Timeout Consideration: Since debugging can take time, make sure your

run_deployment

call has an appropriate timeout:

python
flow_run = await run_deployment(
    name=f"{settings.PREFECT_COMPUTER_VISION_FLOW_NAME}/{settings.PREFECT_DEPLOYMENT_NAME}",
    parameters={...},
    timeout=0  # No timeout while debugging
)

3. Port Accessibility: Make sure port 5678 (or whichever port you choose) is accessible from your VS Code environment to where the flow is running. 4. Multiple Flows: If you have multiple flows running, you can use different ports for each:

python
debugpy.listen(("0.0.0.0", 5678 + flow_instance_number))

Would you like me to elaborate on any of these steps or help you troubleshoot a specific part of the setup?