Welp, it's been a day with the new settings, and stats went up 140K for API calls
... going to check again tomorrow to see if any of that was spill over from yesterday
140K calls a day, how often are you sending scalars ? how long is it running? how many experiments are running ?
each epoch runs about 55 minutes, and that screenshot I posted earlier kind of show the logs for the rest of the info being output, if you wanted to check that out None
Maybe ClearML is using tensorboard in ways that I can fine tune? I saw there was a manual way if you were not using tensorboard to send over data, but the videos I saw from your team used this solution when demoing YOLOv8 on YouTube ( there were a few collab videos your team did with theirs, so I just followed their instructions ). But my gut is telling me that might be the issue for the remaining data being sent over that I have no insight into.
In case of scalars it is easy to see (maximum number of iterations is a good starting point
might be a feature request then, as ya, having transparency into something we are charged for would be nice. At this point, I have zero idea what is driving this usage and just want to make sure the costs for training do not bloat too much. I personally am just using ClearML as a central dashboard for a few people. I don't need it to be live data, I just need a rough overview of progress. Even if it only posted updates to ClearML once an hour, that is honestly fine.
So, might be in the minority here, but seems like capturing stdout and sending that over to clearml via API should be disabled by default. Like I get maybe capturing stderr, but stdout? In a training scenario, that's MILLIONS of API calls just in progress bar indicators, right? Like it might actually be better for the ClearML servers just in general to make the user turn that on if they want it, otherwise we're just blasting your servers. In my case, I did not even know it was sending that over until I got into digging where these API calls were coming from, and saw the CONSOLE tab in clearml that had every single line of stdout captured.
If you do not have a lot of workers, that I would guess console outputs
FYI, found log_stdout in that same setting and default for that was true so set that to false so it would not log all stdout & stderr
@<1523701087100473344:profile|SuccessfulKoala55> You are my hero !!! This is EXACTLY what I needed !!!
FYI, I did not even know to look into this until I logged in and saw that I was being throttled because I had hit my monthly limit with API calls ( on my very first use of your platform ), and my last dozen or so epochs were just not even logged ( also a bummer ). I only had that one model in training, and thought there was no way I sent over a million API requests, so had to figure out where those were coming from, and tracked it down to that STDOUT, and was like ... wait, what?!?! Found that console tab, which I did not even use before, and saw that screenshot I posted, and was like ... well, there's your problem, ha ha
Hi @<1572395184505753600:profile|GleamingSeagull15>
Try adjusting:
None
to 30 sec
It will reduce the number of log reports (i.e. API calls)
Is there a place in ClearML that shows Platform Usage? Like, what's actually taking up the API calls?
hmmm, this is just a personal project, honestly was just hoping this would let me take the results of each epoch and put it in a central dashboard. Having this generate 1M+ api calls and only being like 1/4 of the way though training is a bit much. Current pricing is $1/100K API calls at the PRO tear, which I am on ... so it would be like another $50 just in API calls at this pace 😞 Would love to just cap it at a fixed amount for a month for API calls.
I guess last followup question, is there a way to cap costs? Like if this is running at this scale, I am not sure I can use ClearML for my purpose if I am just going to get overage charged repeatedly ( which I am already looking like I will be doing ).
I guess last followup question, is there a way to cap costs?
Scale tier ? (I know it is not per usage, but it is probably more than 15$ per user 🙂 )
But I will try to set the reduce the number of log reports first
(Not sure it actually has that information)
Came to ClearML since it had slick dashboard and showed me the info that mattered. Loved that I could share the results of each epoch so we could make sure things were headed in the correct direction.
Would love to just cap it at a fixed amount for a month for API calls.
Try the timeout configuration, I think this shoud solve all your issues, and will be fairly easy to set for everyone
Just wish I could actually see somewhere what is being sent over API so I could know where to focus my efforts to refine this kind of stuff 😉
I appreciate your help @<1523701205467926528:profile|AgitatedDove14> 🙂
It was at 1.1M when I shut it down yesterday, and today it's at 1.24M
I am running this on a 3090 GPU locally, just been letting it run for about two weeks now I think. Just have the one GPU, ha ha. It's at epoch 368 out of the 1,000 I have it set to cap out on ( if it does not hit the default YOLO "patience" limit of 50 before then and self terminate ).
I think we're good now :) Appreciate the help !!!
Math checks out that if I was generating around 140K a day, and this had been running for 9 days, it had 1.2M when I caught it . So I think the next day after I shut it down I was seeing previous days numbers before shut down added . And another 24 hours it barely changed, so ya, it was 100% the stdout logging .
well from 2 to 30sec is a factor of 15, I think this is a good start 🙂
Ya, sorry, I meant that if you needed more info on what was being run, it was in that screenshot ( showed instances/epochs/batch size, etc ) . But yes, it's since been disabled .
Since it's literally something we have to pay for ( which I signed up to do ) I would love to know what drives this cost
One single experiment using the code above. I have no idea how many scalars I am sending since as far as I can tell, I am not setting anything specific to define what I am sending over to ClearML, literally first time using YoloV8 or ClearML. Just using the super basic python to run.