Hello! I am seeing this error indicating a 503 response from

get_scheduled_flow_runs

prefect.exceptions.PrefectHTTPStatusError: Server error '503 Service
Unavailable' for url
'<https://api.prefect.cloud/api/accounts/><account_id>/wor
kspaces/<workspace_id>/work_pools/<workpool>/get_scheduled_flow_
runs'
Response: {'exception_message': 'Service Unavailable'}

I don't see any indication of an outage on your status page: https://prefect.status.io/ Is this something y'all are aware of?

Folks, Good evening/afternoon/morning! 🙂 . Does anyone has OpenShift Yaml templates to set up PREFECT ? Due to privacy reason we cant use Prefect Cloud. So need to set up everything on our private OpenShift.. Anyone has Openshift Build , Deploy yaml templates

Hello! How do we change the prefect_api_url to our server host ip in selfhosted prefect. I first ran the command prefect config set PREFECT_API_URL=http://MYHOSTIP:4200/api and then prefect server start, once the prefect server starts, I am seeing it pointed to http://127.0.0.1:4200/api

Hello, Is there any way to implement a dropdown selection on the UI when starting a custom run for a deployment? Update: Solved!

Hi, what is the best way to run "subtasks"? I know i can just use in taskA

taskB.run()

but then it's treated as a normal python function (so no logging, and no other prefect's magic is available for this taskB) I managed to workaround this creating a Flow in a Task, something like this:

@task
def taskA(config: dict):
#...
    with Flow() as flowB:
        taskB()
#...
    a = flow.runB()

it seems to work - i get logs for flowB and taskB, etc I can visualize both flows using flow.visualize() - on two separate diagrams - but it's all right for me. But is this a correct approach, or should I do this some other way?

@Marvin why am I getting TypeError: ‘NoneType’ object is not iterable when using the command

prefect deployment run

@Marvin I want to install self-hosted Prefect server with dashboard(that's Prefect UI ) in my Ubuntu server, could you share me the install document?

What's the correct way to spin up sub flows in parallel (like truly parallel, not concurrent with

asyncio

)? I'm doing the following for now, but it does not preserve flow-subflow relationships (and, it makes sense because the parent process isn't passing any prefect information to the child process in multiprocessing). The alternative here would be to use a Dask runner for all the subflows, in which case every task would be run truly in parallel, but all the subflows would be in the same process as the parent flow. However, it feels more natural to map flows with processes, rather than tasks with processes.

import multiprocessing
import sub_flow

def sub_flow_wrapper(foo):
  sub_main(foo)

@flow
def main():
  with multiprocessing.Pool() as pool:
    pool.starmap(sub_flow_wrapper, [foo1, foo2, foo3])

in the prefect.yaml file I specify the pull step to be a git clone, if I try deploying more than once, on the second time I get an error because the repo already exists in the working directory. Is there a way to specify to not clone but do a git pull from remote branch if the repo already exists?

Hi again 😄 I just got the error

No heartbeat detected from the remote task; marking the run as failed

- this happend for tasks being run in parallel via the dask

KubeCluster

looking at our (internal) logs - it seems like all of them died simultaneously, 20+ minutes into their run there’s a total of 58 tasks being run, with 4 workers the first 4 tasks took approx. 1 hour to run and completed successfully, and then the next 4 that were run all seem to have failed simultaneously any idea / help?