I am building TraceML (open-source) focused on one practical outcome:

Automatically identify why a PyTorch training run is slow and suggest what to change.

Instead of raw traces or yet another dashboard, the goal is:

- highlight the top bottleneck(s) (data stalls, GPU waiting, comm/sync, memory/alloc issues)

- keep it lightweight enough to run regularly, not only during deep debugging

I would love honest feedback from people who run real training workloads:

Would you use something like this, or does it inevitably become another dashboard to maintain?

What would make it clearly useful vs noise?

Repo: https://github.com/traceopt-ai/traceml

Happy to clarify anything.