Can't think of a reason it will have such an effect ...
Yep GCP. I wonder if it's something to do with Container-Opimized OS, which is how I'm running the agents
Where is the cleamlr-server running? GCP as well?
SuperiorPanda77 I have to admit, not sure what would cause the slowness only on GCP ... (if anything I would expect the network infrastructure would be faster)
Maybe it was the load on the server? meaning dealing with multiple requests at the same time delayed the requests?!
Possibly but I think the server was fine as I could run the same task locally and it took a few seconds (rather than 75) to upload. The egress limit on the agent was 32 Gbps which seems much larger than what I though I was sending but I don't have a good idea of what that limit actually means in practice!
the time taken to upload halved. It is puzzling because as you say it's not that much to upload.
Maybe it was the load on the server? meaning dealing with multiple requests at the same time delayed the requests?!
For now I've whittled down the number of entries to a more select but useful few and that has solved the issue. If it crops up again I will try
connect_configuration
properly.
Thanks for your help!
My pleasure 🙂
I realise I made a mistake and hadn't actually used connect_configuration !
I think the issue is the bandwidth yeah, for example when I doubled the number of CPUs (which doubles the allowed egress) the time taken to upload halved. It is puzzling because as you say it's not that much to upload.
For now I've whittled down the number of entries to a more select but useful few and that has solved the issue. If it crops up again I will try connect_configuration properly.
Thanks for your help!
connect_configuration
seems to take about the same amount of time unfortunately!
I think it is a better solution, that said from your description it sounds the issue is the upload bandwidth (i.e. json-ing the dict itself), could that be it?
(and even 1000 entries seems like something that would end up at 1mb upload, that is not that much)
connect_configuration seems to take about the same amount of time unfortunately!
That said, maybe the connect dict is not the best solution for thousand key dictionary
Seems like it isn't haha!
What is the difference with connect_configuration ? The nice thing about it not being an artifact is that we can use the gui to see which hashes have changed (which admittedly when there are a few thousand is tricky anyway)
. Does
Task.connect
send each element of the dictionary as a separate api request? Has anyone else encountered this issue?
Hi SuperiorPanda77
the task.connect ends up as a single call with all the data being sent on a single request.
That said, maybe the connect dict is not the best solution for thousand key dictionary ...
Maybe artifact, or connect_configuration are better suited ?
wdyt?