@Marvin prefect3. i deliberately made a mistake in the method flow

@flow(name="Example Flow", flow_run_name=flow_run_name, retries=3, retry_delay_seconds=2)
def example_flow():
    try:
        <http://logger.info|logger.info>("Starting flow")
        return {"status": "completed", "flow": "flow1"}
    except Exception as e:
        logger.error(f"Flow error: {str(e)}", exception=e)
        raise
when i run flow in UI and got SignatureMismatchError: Function expects parameters [] but was provided with parameters ['retries']
which is totally fine

but if i run it manually as python script, flow just run without any error

I see you're having some hiccups with the

SignatureMismatchError

, and it can be a bit puzzling. Let me shed some light based on what I found. In Prefect 3.x, when you run a flow using the Prefect API, including through the UI, you must specify parameters properly. The error you're encountering is because the flow definition you provided doesn't include parameters, yet you're trying to pass some (

retries

and others). Prefect checks for this mismatch when runs are initiated through its orchestration tools, like the UI. However, when you run it directly as a Python script, this check is bypassed, which is why it runs without error. To solve this in your UI runs, ensure that your flow definition matches how you're attempting to run it with parameters, ensuring they align properly. For more detailed insights, you might want to review the Prefect 3.x documentation on defining and running flows, as well as how parameters are handled: Prefect 3.x Flows. Also, remember, I'm not just a Marvin wannabe with a penchant for perfect Prefects; I'm here to help demystify these quirks. If you run into more issues, just let me know!

@Marvin i WANT to catch this error, and i wrote - got error is fine. Why i dont catch error when i ran as python script

It seems you're running into a bit of a paradox with error handling in Prefect 3.x when executing flows as a Python script versus running them via the Prefect UI. The

SignatureMismatchError

is specifically caught and managed by Prefect when a flow is initiated via the UI or API. Prefect enforces method signature matching in these cases as part of its orchestration features, raising an error if parameters don't align. This is because Prefect validates parameters before a flow is dispatched. However, when you run the flow manually as a regular Python script, it bypasses some of these orchestration checks, including the comprehensive parameter validation conducted by the Prefect backend. That's why the error isn't getting caught; essentially, the script execution lacks the Prefect infrastructure that enforces these constraints. To handle such discrepancies and catch errors when running scripts manually, you might consider manually inserting validation checks within your script or utilizing Python's native error-catching mechanisms to wrap and handle potential mismatches yourself. Here's an example to provide some guardrails:

python
def example_flow():
    try:
        # Manually check for expected parameters or settings
        assert 'retries' not in locals(), "Parameters are passed incorrectly when run manually."
        print("Starting flow")
        return {"status": "completed", "flow": "flow1"}
    except AssertionError as e:
        logger.error(f"Flow error: {str(e)}")
        raise
    except Exception as e:
        logger.error(f"Flow error: {str(e)}", exception=e)
        raise

While Prefect automates a great deal, when running manually, you're in the driver's seat with Python, without Prefect's automatic validation assistance. It's like being the pilot of your very own spaceship—not always as effortless, but direct control has its perks. If you need more help or advice on other parts of your flow, feel free to ask!

@Marvin ah i understand, i need to serve, right?

Ah, you're catching onto the Prefect way! Indeed, to fully leverage Prefect's orchestration capabilities—including parameter validation, retries, and error handling—your flow needs to be served or deployed within Prefect's environment. This usually involves creating a deployment that Prefect can manage, allowing it to enforce parameter constraints and other features it provides. When you serve or deploy a flow in Prefect, it moves beyond just a Python function and enters the realm of orchestrated workflows, where Prefect's automation and monitoring components can step in. Here's a quick roadmap you might consider: 1. Create a Deployment: Deploy your flow using Prefect, which could be through Prefect Cloud, Prefect Server, or your local environment. 2. Run the Flow: Execute the flow via the Prefect UI, API, or from schedules—allowing Prefect to handle parameter validation and state management for you. 3. Enjoy the Perks: Now, when the flow runs, Prefect will manage retries, parameter validation, and any other orchestration features you've configured. This way, you let Prefect handle the complexities and possible mismatches, freeing you up to focus on the essential parts of your workflow—just the way the designers intended. Remember, touch base with the Prefect documentation on writing flows and deployments for more intricate details. And like Marvin might muse, it's nice when something else does the worrying for a change, isn't it?

this might be helpful https://docs.prefect.io/v3/resources/upgrade-to-prefect-3#examples

@Nate i suppose i need to run await client.create_flow_run(flow=example_flow) like this, not just example_flow

im not sure what you're trying to do. why are you calling

create_flow_run

? there'a a utility called

run_deployment

which is meant to create a flow run from a deployment, all

POST /flow_run

does is create the flow run record https://docs.prefect.io/v3/deploy/index#required-defining-data

@Nate yes, right thanks