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

What is the best way to achieve that please?

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...

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.

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

Does that make sense?

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

Screenshot incoming

Thanks @<1523701070390366208:profile|CostlyOstrich36> !

• I hadn’t found the multiple-resources within the same autoscaler. Could you point me to the right place please? Are they all used interexchangeably based upon availability, rather than based on job needs?
• We thought of using separate queues (we do that for CPU vs GPU queues), but having ClearML automatically dispatch to the right based on a job specification would be more flexible. (for example, we could then think to dispath dynami...

@<1523701205467926528:profile|AgitatedDove14> great! (I'm on the Pro version :) ).

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 .

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

This great tool is worth paying for!

screenshot in the PR: None

We’re on the PRO 🙂

(do you welcome PRs?)

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

Great, thanks both! I suspect this might need an extra option to be passed via the SDK, to save the iteration scaling at logging time, which the UI can then use at rendering time.

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)

It was a debugging session. We haven’t yet tried a “Standard” non-debugging clearml session.

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

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

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)

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

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.

Thanks @<1523701070390366208:profile|CostlyOstrich36> ! I'll do - and might even peek under the hood see if I can make a PR. What's the best repo for that? Is it that of the ClearML python package?

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.

Thanks @<1523701087100473344:profile|SuccessfulKoala55> ! Any inkling on how soon? Is it days, weeks, or months please? 🙂

Yes

OK, so no way to have an automatic dispatch to different, correctly-sized instances, it’s only achievable by submitting to different queues?