adamant-monkey-75912
03/26/2024, 9:20 AMadamant-monkey-75912
03/26/2024, 9:21 AMfull-evening-87657
03/26/2024, 9:27 AMadamant-monkey-75912
03/26/2024, 9:29 AMfull-evening-87657
03/26/2024, 9:36 AMadamant-monkey-75912
03/26/2024, 10:14 AMfull-evening-87657
03/26/2024, 10:27 AM@task(requests=Resources(cpu="1", mem="100Mi"), limits=Resources(cpu="2", mem="150Mi")) # The limits Resources's maximum == helm chart values config
def count_unique_numbers(x: typing.List[int]) -> int:
s = set()
for i in x:
s.add(i)
return len(s)
full-evening-87657
03/26/2024, 10:31 AMadamant-monkey-75912
03/26/2024, 11:17 AMfull-evening-87657
03/26/2024, 11:40 AMaverage-finland-92144
03/26/2024, 12:57 PMadamant-monkey-75912
03/26/2024, 1:12 PMrough-bird-62435
03/26/2024, 4:46 PMwf_executions = sum(tasks_execution)
.
for example, we have one wf with 100 tasks/nodes, we would like to schedule it multiple time during the day, what we don’t want is for one scheduled run to overlap another run, since Flyte doesn’t support this option, we want to at least get an alert if on extreme cases one run gets out of the normal duration to finish.
the most convenient way would be to rely on prom metrics, we got lost there and we are not sure how to get to the right one.
is there any metric (or combination of metrics) to give us the duration of a specific executions including tasks that are failed/finished or still running?
if there are other solutions to the problem, they are also welcome 🙂