Replies: 2 comments
-
Hi, they don't provide a lot of insight in their benchmarking (the part where it can get up to 2x performance increase) and so it's wise to be a bit sceptical:
Now of course on NVidia cards using ORT is a rather niche thing as it lacks the flexibility you have when using torch+CUDA. As a result it's not going to make a lot of difference as it might at best give those on ORT DirectML the same speed as on ORT GPU. So only those with an AMD 7000-series card have a good reason to believe they can get some speed benefit (On Windows of course, on Linux ROCm 5.5 and the 5.6 prereleases give you great speed with torch). What to expect: You also get a 10% speed boost just by upgrading to ORT 1.15 |
Beta Was this translation helpful? Give feedback.
-
Thanks for the quick reply, I've attempted to modify and use their example scripts on my already converted ONNX (which their documentation says you can do to optimize) which promptly deleted my entire model folder lol. But they also use ver 1.13 of Torch. I think any performance benefits to be had are lost because of this. The documentation is also possibly one of the worst I've seen from Microsoft. I'm updating to nightlies and sticking with this until AMD brings ROCm to windows I'm pretty sure people would port it to old GPUs anyway. |
Beta Was this translation helpful? Give feedback.
-
https://github.com/microsoft/OLive
This is high profile due to nvidia driver release mentioning Microsoft's project.
Would there be any benefit in utilizing Olive for conversion, if so can it be implemented here? The readme mentions that there's hardware-specific optimizations, but I am unable to test and compare. Maybe we can get higher it/s with it.
Beta Was this translation helpful? Give feedback.
All reactions