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.
@<1523701087100473344:profile|SuccessfulKoala55> You are my hero !!! This is EXACTLY what I needed !!!
Is there a place in ClearML that shows Platform Usage? Like, what's actually taking up the API calls?
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
Scary to think how common that might be, could be interesting way to optimize your platform, detect excessive console logging and prompt user to confirm continued usage ( or link to docs on how to disable if they want to stop it )
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.
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.
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 ).
Hi @<1572395184505753600:profile|GleamingSeagull15>
Try adjusting:
None
to 30 sec
It will reduce the number of log reports (i.e. API calls)
Hmm if this is case, you can add some prints in here:
None
the service/action will tell you what you are sending
wdyt?
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
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 .
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.
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
I thought you disabled the stdout log. no?
Maybe ClearML is using
tensorboard
in ways that I can fine tune? I
You can open your TB and see, every report there is logged into clearml
It'd be great if it just posted to clearml after each epoch is completed and the CSV with the results gets updated . I only care about using the dashboard to track completed progress . I can use my local computers terminal window to monitor current epoch training . No need to send that to clearml every second ;) Results once an hour or so is fine after each completes :)
If you do not have a lot of workers, that I would guess console outputs
this one, right ? report_period_sec in ~/clearml.conf correct ?
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 .
But I will try to set the reduce the number of log reports first
I appreciate your help @<1523701205467926528:profile|AgitatedDove14> 🙂
Thanks, will do. Heck, for my use case, I only need like once every 10 minutes.
Actually looking at the counts today, they've barely changed. So I think this actually fixed it, and was just that the counts are only updated daily so I needed to get 48 hours out from when I made the change to see clean results to assure no spill over counts from previous days.
In future collab community videos and sample source for YoloV8, might be worthwhile to call that out as something folks might want to turn off unless they need it :) . Like I mentioned, I had no idea it was going to do that and sent your servers over 1.4M API hits unintentionally : (
My training is on roughly 50 classes as a subset of the Open Images Dataset for Segmentation
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 😉
well from 2 to 30sec is a factor of 15, I think this is a good start 🙂
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.