screenshot in the PR: None

Thanks for the answers 🙂

@<1523701070390366208:profile|CostlyOstrich36> Any idea please? We could use our 8xA100 as 8 workers, for 8 single-gpu jobs running faster than on a single 1xV100 each.

@<1523701087100473344:profile|SuccessfulKoala55> yes I am 🙂 And thanks, looking forward to it!

Thanks @<1523703436166565888:profile|DeterminedCrab71> . Yes, I've seen the three options to plot different things. What I'm trying to do is for the "Iterations" plot to have the same plot but just change the X label, not the time series. In matplotlib that would be a call to xlabel .

Happy to jump on a call if easier to make sense of it :)

Can the “multiple agents on a single queue” scenario, combined with the autoscaler, spawn multiple agents on a single EC2 instance, by chance, please? (thinking e.g. 8 agents on a 8xGPU machine)

Does that make sense?

Yes, we love the HPO app, and are using it :)

(actually, that might even be feasible without touching the UI, depending how the plot is rendered, but I'll check)

We were just trying to run a plain clearml-session on the AWS autoscaler.

Tagging my colleague @<1529271085315395584:profile|AmusedCat74> who needs this with me 🙂

Dang, so unlike screenshots, reports do not survive task deletion :/

Screenshot incoming

Tagging my colleague @<1529271085315395584:profile|AmusedCat74> who made that report.

Oh? Worth trying!

And yes, I was also referring to tasks ran by the Autoscaler (potentially via the HPO) app, too.

Logging scalars also leverages ClearML automatic logging. One problem is that this automatic logging seems to keep its own internal "iteration" counter for each scalar, as opposed to keeping track of, say, the optimizer's number of steps.
That can be simply fixed on clearML python lib by allowing to set a per-scalar iteration-multiplier.

@<1523701087100473344:profile|SuccessfulKoala55> I think you’ve been tagged in the PR 🙂

From the doc I seemed to find ways to log 2D scatter plots, but not line plots :/ (found)
It also seems simpler to keep the scalar logging structure, but be able to pass a multiplier (reflecting the eval_n_steps in for example Torch Lightning)

(do you welcome PRs?)

(apologies for delay @<1523701087100473344:profile|SuccessfulKoala55> , we got called into meetings. Really appreciate your reactivity!)

What is the best way to achieve that please?

Is the doc on GitHub so we can copy that into a PR?

Thanks. That would be very helpful. Some of our graphs are logged by optimization steps, whereas some by epochs, so having all called "Iterations" is not ideal.

This great tool is worth paying for!

Tagging @<1529271085315395584:profile|AmusedCat74> my colleague with whom we ran into this issue.

We’re on the PRO 🙂

Yes, exactly. Here is the logical sense it makes: I have plots where iterations represent different units: for some these plots iterations (call them A) are optimization steps, while for others (call them B) they are evaluation iterations, occuring every N optimization steps. I would like to either:

• Change the X label so these different plots do not have the same label when they represent different things.
• Or, even better, keep the unique "iterations" label but be able to change how I lo...

The problem with logging as a 2D plot is we lose the streaming: if I understand correctly the documentation, Logger.current_logger().report_scatter2d logs a single, frozen 2D plot when you know the full X and Y data. And you would do that at each evaluation step.
Logging scalars allows to log a growing time series, i.e. add to the existing series/plot at every "iteration", thus being able to monitor the progress over time in one single plot. It's a much more logical setting.