So I see two options:
Reducing the number of image reported (already in our plan) Make on big image per epoch

it's a matter of scale for the query that retrieves the data, not related to the amount of data

I call it like that:
logger.clearml_logger.report_image( self.tag, f"{self.tag}_{iteration:0{pad}d}", epoch, image=image ) `` self.tag is train or valid . iteration is an int for the minibatch in the epoch

What do you use as title and for the series for each image?

I have 6 plots with one or 2 metrics. But I have a lot of debug samples.

Issue open on the clearml-server github https://github.com/allegroai/clearml-server/issues/89 . Thanks for your help.

You're generating a huge amount of variants ( series ) using the iteration number

That's strange... 😕

Reducing the number of image reported (already in our plan)

You don't actually need to reduce the number of images, just make sure the series parameter is consistent, so basically you want to make sure that in every report (i.e. iteration in which you're reporting), you have a fixed set of title/series values

Hi SteadyFox10 , how many unique metrics and variants do you have in this task? We may be hitting some limit here

Thanks a lot I'll check how to do this correctly

Are you using a fixed self.tag ?

Even simpler than a github, this code reproduce the issues I have.

This is a run I made with the changes, As you can see the iteration now go from 0-111 and in each of them I have image with the name train_{001|150}

Sure, let me know if I can help 🙂

That's the issue...

Something like 100 epoch with a least more than 100 images par epoch reported.

Ok fine.

We're planning to optimize the server code for these cases, but I would suggest using a more fixed set of title/series for your debug images

I'll try to make a code that reproduce this behavior and post it on github is it fine ? that way you could check if I'm the problem (which is really likely) 😛

iteration has nothing to do with it

Oh, sorry

SuccessfulKoala55 feel free to roast my errors.

I'd appreciate that 🙂

yes tag is fixed

150 x 100 is still larger than 10,000

That's really hard to support using ES as it inflates the number of buckets in the aggregation used when trying to locate unique debug images

I have made some changes in the code
logger.clearml_logger.report_image( self.tag, f"{self.tag}_{epoch:0{pad}d}", iteration=iteration, image=image ) `` epoch range is 0-150 iteration range is 0-100And the error is still there
General data error (TransportError(503, 'search_phase_execution_exception', 'Trying to create too many buckets. Must be less than or equal to: [10000] but was [10001]. This limit can be set by changing the [search.max_buckets] cluster level setting.'))Could it be because the joint of the scalar graph + debug samples ?
I have 8 scalar graph:
2 :monitor:{gpu|machine}: with 15k iteration 2 training_{metrics|loss} with 15k iteration and the other between 150 and 40 iteration each
SuccessfulKoala55 did you have any other suggestion? did I do something wrong with my changes ?

Is it better on clearml or clearml-server ?

Can I still ask you to open a GitHub issue? stuff tends to get lost here, and I can't get to it today 😞