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Allow more granular controls over how data is downsampled when its plotted.
Motivation
Currently, when we compare a run in progress to a completed run, the smoothing is inconsistently applied.
Since both the run in progress and previous run are downsampled to ~5000 steps, the completed run appears much smoother than a run in progress. This prevents the plots from being useful for comparing partially completed runs
Related Discussion
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The text was updated successfully, but these errors were encountered:
tensorpro
changed the title
Greater control in downsampling
Greater control in downsampling used in plots
May 18, 2024
Sounds like this becomes an issue where a run of interest contains significantly less data points than the others. If one were to naively adjust the sampling to match the run with the smallest number of points, this could incur an unnecessary load as significantly more data points would be retrieved from the other runs.
Perhaps a better approach for this use case could be to equalize the x-axis range so that only the points being compared would be retrieved (and as a result, the same sampling would be applied)?
Proposal Summary
Allow more granular controls over how data is downsampled when its plotted.
Motivation
Currently, when we compare a run in progress to a completed run, the smoothing is inconsistently applied.
Since both the run in progress and previous run are downsampled to ~5000 steps, the completed run appears much smoother than a run in progress. This prevents the plots from being useful for comparing partially completed runs
Related Discussion
If this continues a slack thread, please provide a link to the original slack thread.
The text was updated successfully, but these errors were encountered: