@Marvin how do I update flow's run name dynamically in the execution?

It seems that the Prefect documentation and source code do not explicitly provide a direct method for dynamically updating a flow's run name during execution. However, you can manipulate the flow run context to achieve similar behavior. Here's a general approach: 1. Use `FlowRunContext`: Prefect's

FlowRunContext

can be used to access metadata about the current flow run, including the run name. You can modify the context to change the run name. 2. Custom Logic: Implement custom logic within your flow to update the run name based on specific conditions or events during the execution. 3. Prefect Client: You might also consider using the Prefect Client to update metadata about the flow run, although this would typically be done outside the main execution logic. Here's a hypothetical example, assuming you have access to modify the context:

python
from prefect import flow, get_run_logger
from prefect.context import FlowRunContext

@flow
def my_flow():
    logger = get_run_logger()
    
    # Access the current flow run context
    context = FlowRunContext.get()
    
    # Hypothetically update the flow run name
    # This part of the code is illustrative and may not work directly
    context.flow_run.name = "New Flow Run Name"
    
    <http://logger.info|logger.info>(f"Updated flow run name to: {context.flow_run.name}")

my_flow()

Keep in mind that this is a conceptual illustration. You might need to adapt it based on Prefect's actual capabilities and APIs. For more specific implementations, you should refer to Prefect's documentation or community forums for guidance. For further details on managing flow runs, you can explore the Prefect 3.x documentation and the Prefect GitHub repository.

@Marvin how to return the completed flow with Object result that can be accessed in the UI?

To return a completed flow with an object result that can be accessed in the Prefect UI, you can follow these steps: 1. Return Value from Flow: Ensure that your flow returns the result you want to be accessible. Prefect captures the return value of a flow and allows you to view it in the UI. 2. Result Storage: Prefect uses result storage to persist flow results. By default, results are stored in-memory, but you can configure a result storage to persist results outside of the flow execution environment. This is important for accessing results from the UI. 3. Set Up Result Storage: You can configure result storage by setting the

result_storage

in your Prefect configuration or specifying it in your flow definition. Here's a basic example of how you might set this up:

python
from prefect import flow, task
from prefect.filesystems import LocalFileSystem

@task
def generate_data():
    return {"key": "value"}

@flow(result_storage=LocalFileSystem(basepath="/path/to/store/results"))
def my_flow():
    result = generate_data()
    return result

my_flow()

In this example: - The

my_flow

flow returns an object that can be viewed in the Prefect UI. - The

LocalFileSystem

is used as a result storage, which persists the result to a specified path. 4. Accessing in the UI: Once the flow completes, you can navigate to the Prefect UI, find the flow run, and access the result from the run details page. For more details on configuring result storage, refer to the Prefect documentation and explore the flow engine source code.

@Marvin

context.flow_run.name = "New Flow Run Name"

does not work in changing the

flow_run.name

dynamically, is there another approach?

Dynamically changing the flow run name during execution is not directly supported by Prefect's current API. The

flow_run.name

is generally set at the time of creation and is intended to remain constant throughout the execution. However, you can consider these alternatives: 1. Post-execution Update: After the flow run completes, you can manually update the flow run name using the Prefect Client. This would involve writing a script that uses the Prefect Client to change the name of the flow run after it has finished executing. 2. Custom Metadata: Instead of changing the flow run name, you can store additional metadata or tags that can be used to identify or categorize the flow run. This metadata can be updated dynamically during the flow execution. 3. Prefect Client Approach: Use the Prefect Client to interact with the Prefect API and update flow run metadata. This requires a separate script or process to handle the update. Here's an illustrative example using the Prefect Client:

python
from prefect import get_client

async def update_flow_run_name(flow_run_id, new_name):
    async with get_client() as client:
        await client.update_flow_run(flow_run_id, name=new_name)

# Call this function with the appropriate flow run ID and new name

This approach would involve calling the

update_flow_run_name

function after the flow run completes. For more details or updates on this feature, you can follow related discussions or feature requests on the Prefect GitHub issues page.

hi @Tony Lim marvin is likely overcomplicating this / confabulating do you want to set the name based on parameters or something else? if its just based on params, you can do

» ipython

#[1]
from pydantic import BaseModel

#[2]
class Person(BaseModel):
    name: str = "Jim"


#[3]
from prefect import flow

#[4]
@flow(flow_run_name='greeting {person.name}')
def greet_person(person: Person = Person()): print(f"hi {person.name}")

#[5]
greet_person()
16:49:14.013 | INFO    | Flow run 'greeting Jim' - Beginning flow run 'greeting Jim' for flow 'greet-person'
16:49:14.015 | INFO    | Flow run 'greeting Jim' - View at <http://localhost:4200/runs/flow-run/84444c88-99e7-4d26-b1d4-3ad1399bdcfe>
hi Jim
16:49:14.030 | INFO    | Flow run 'greeting Jim' - Finished in state Completed()

I see, I want to set the run_name at some point much later in the execution as there is some non-deterministic information i want to include in the flow_run_name. Not a big issue, but will make our flow runs cleaner