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 :)
Hi @<1572395184505753600:profile|GleamingSeagull15>
Try adjusting:
None
to 30 sec
It will reduce the number of log reports (i.e. API calls)
@<1572395184505753600:profile|GleamingSeagull15> see " Can I control what ClearML automatically logs? " in None (specifically the auto_connect_frameworks argument to Task.init() )
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
is number of calls performed, not what those calls were.
oh, yes this is just a measure of how many API calls are sent.
It does not really matter which ones
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
In case of scalars it is easy to see (maximum number of iterations is a good starting point
well from 2 to 30sec is a factor of 15, I think this is a good start 🙂
(Not sure it actually has that information)
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.
I appreciate your help @<1523701205467926528:profile|AgitatedDove14> 🙂
this one, right ? report_period_sec in ~/clearml.conf correct ?
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
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.
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 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 : (
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 ?
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.
I'm not sure on the frequency it updates though
I think we're good now :) Appreciate the help !!!
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.
But I will try to set the reduce the number of log reports first
It was at 1.1M when I shut it down yesterday, and today it's at 1.24M
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 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 ).
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 .
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 .
@<1523701087100473344:profile|SuccessfulKoala55> You are my hero !!! This is EXACTLY what I needed !!!
My training is on roughly 50 classes as a subset of the Open Images Dataset for Segmentation