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.
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 🙂 )
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
I would love to be able to fine tune this as needed, but in my profile I only see a Billings & Usage, and it states at the top that "Usage data is updated once every day" ... and even then, all the shows under "Platform Usage" is number of calls performed, not what those calls were.
But I will try to set the reduce the number of log reports first
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 :)
Is there a place in ClearML that shows Platform Usage? Like, what's actually taking up the API calls?
@<1572395184505753600:profile|GleamingSeagull15> see " Can I control what ClearML automatically logs? " in None (specifically the auto_connect_frameworks argument to Task.init() )
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.
this one, right ? report_period_sec in ~/clearml.conf correct ?
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
I did notice that the last 24 hours I dropped quite a bit, so my theory that the 140K might have some spillover from previous day might have been correct. Last 24 hours went from 1.24M to 1.32M, so about half as much as the day before, with the same training running.
In case of scalars it is easy to see (maximum number of iterations is a good starting point
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
Glad I got that sorted. I was OK being a paying customer, but gettin overage charges for that console stuff would have been a bummer if we had not figured it out. Next month things should be back to normal 😉
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 .
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 .
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 😉
My training is on roughly 50 classes as a subset of the Open Images Dataset for Segmentation
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
It was at 1.1M when I shut it down yesterday, and today it's at 1.24M
@<1523701087100473344:profile|SuccessfulKoala55> You are my hero !!! This is EXACTLY what I needed !!!
I had no idea it was going to do that and sent your servers over 1.4M API hits unintentionally
Yeah, that is way too much, I think relates to the frequency it updates the console 😞
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
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 ).
well, in my case, if I am trying to make sure I do not go over the allotted usage, it matters, as I am already hitting the ceiling and I have no idea what is pushing this volume of data
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
Under your profile you should be able to see it