Hi @<1655744373268156416:profile|StickyShrimp60> , happy to hear you're enjoying ClearML 🙂
To address your points:
Is there any way to lock setting of scalar plots? Especially, I have scalars that are easiest comparable on log scale, but that setting is reverted to default linear scale with any update of the comparison (e.g. adding/removing experiments to the comparison).
I would suggest opening a GitHub feature request for this
Are there plans of implementing a simple feature to ignore outliers in scalar plots?
Can you please elaborate? Screenshots are always welcome 🙂
In the hyperparameter pane of the comparison view I can only see up to 10 experiments. Will more experiments be visible in the paid version or self-hosted version?
I'm afraid not.
Is the hyper-parameter optimization App be available in the self-hosted solution ? (I am thinking about trying out a self-hosted setup on AWS)
It's available only in the PRO and the Scale/Enterprise licenses. However in the self hosted version there is an HPO example - None
Lastly, when preparing reports, is there anyway to include math (e.g. LaTeX mathmode block) ?
I'm not familiar with it, but the reports are based on markdown so anything a markdown editor can do, you can do within the reports.
I see. Sounds like a good idea! Please open a GitHub feature request 🙂
FYI @<1523701070390366208:profile|CostlyOstrich36> after a quick search it seems there is already a request for this 🙂 None
Are there plans of implementing a simple feature to ignore outliers in scalar plots?
Here is a plot that is not readable because of outliers. I will usually just use log-scale on the y axis, and that works fine in most cases, but sometimes you do not want to mess with the scale and just automatically zoom in on the 'typical' range of the data.