-
Notifications
You must be signed in to change notification settings - Fork 93
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Mimimum NVIDA chip for training #301
Comments
We didn't use any kind of acceleration, but if you want to, I'd assume anything that Tensorflow supports. |
Anything that Tensorflow supports should work, but note (that at least with how the code is setup currently) that copying the data over to the GPU/doing the training iterations on the GPU/copying everything back over to the CPU takes longer than just doing the training on the CPU, at least when I last tested it. There has been some experimentation with much larger models where acceleration (GPU/TPU) has definitely helped, but none of those models are upstream in this repository. |
So even then a modern mac m1 should work? |
I see, just a modern CPU with 96 HW threads? "for local training, which is currently the only supported mode, we recommend a high-performance workstation (e.g. 96 hardware threads)." |
Yup. The bottleneck is currently compile time. |
Do you know if anyone is using a mac for this? |
There was some experimentation with using a Mac (see patches like #260), but it's not really a platform where all the tooling here is guaranteed to work. The tooling in this repository is almost exclusively developed and run on Linux, although running it on a Mac should theoretically work, maybe minus seem slight issues. |
What kind of NVIDIA chip would I need for training?
TIA
The text was updated successfully, but these errors were encountered: