I'm trying to track metrics for a new model training run. My dataset has a few hundred thousand samples, and logging seems to take forever. The dashboard becomes really unresponsive when I'm looking at the charts.
Is this a common issue? I'm coming from simpler SaaS tools for marketing data, so maybe I'm configuring something wrong. Are there settings to speed things up when dealing with large volumes of data?
Yeah, I've noticed this too. It feels like it gets bogged down when you log every single sample, even as a histogram.
There's a setting for batch logging, right? Like logging metrics per epoch or per a few hundred steps instead. That helped for me.
Is your setup logging at the sample level? Maybe that's where the slowdown is.
That's a common pain point when you're used to real-time marketing dashboards. The tool is built for granular ML metrics, not streaming analytics, so logging every sample can definitely overload it.
The batch logging suggestion from user1413 is spot on. But also check if you're logging images or model checkpoints by accident - those slow things down way more than scalars.
Have you tried reducing the sampling rate in your logging calls? Even setting it to log every 10 or 100 steps makes the dashboard much more responsive while keeping the trend visible.
Automate all the things