roanakb

Joined 7 years ago

Karma
61
Submissions
19
Member since
Mar 24
cofounder @ Trainy (yc s23)

prev ML lead @ Hive.ai

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Recent submissions

Comment · 2 years ago
Unfortunately, SM efficiency is not accessible via nvidia-smi. The best methods to track it would be to:

1. Profile your model with Pytorch Profiler 2. Export metrics with Nvidia DCGM

Comment · 2 years ago
oh this looks great, thank you for bringing this up! I'll have to give it a try, but seems like the FSDP limitation on torch.compile might carry over?
Comment · 2 years ago
Comment · 2 years ago
Nice, seems like ML Productivity Goodput is a pretty well thought-out metric to understand the overall efficiency of your cluster. I'll consider adding this into our cluster management platform. Only potential drawbacks I'd guess are it being somewhat difficult to compute since it relies on metrics like MFUs, and not something we can observe layer-by-layer to understand inefficient kernels, but I'll take a deeper look. Thanks!
Comment · 2 years ago
Comment · 2 years ago
Yup, similar to SM efficiency in that sense too. If you aren't seeing >80%, there is certainly time left on the table. But getting a high SM efficiency value doesn't guarantee you're making good use of the hardware as well. (still a better proxy than GPU util though)
Comment · 2 years ago
Comment · 2 years ago
Looks great, you guys made it really easy to integrate!
Comment · 3 years ago
Thanks! Let me know if there are any features you'd like to see added.

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