Azzera filtri
Azzera filtri

Overcoming VRAM limitations on Nvidia A100

19 visualizzazioni (ultimi 30 giorni)
I have access to a cluster with several Nvidia A100 40GB GPU's. I am training a deep learning network on these GPU's, however using trainNetwork() only makes use of around 10GB of the GPU's vRAM. I beleive this is a limitation of Nvidia Cuda, see here.
I have two related questions;
  1. Other cluster users are writting in python with the 'DistributedDataParallel' module in PyTorch and are able to load in 40Gb of data (over the cuda limitation) onto the GPU's; is there a similar work around for MATLAB?
  2. If this isn't the case is there any way to use Multi-instance GPU's, so essentially split the physical card into several smaller virtual GPU's and compute in parrellel?
Ideally I would like to speed up computation, so having a 3/4 of the vRAM empty which could otherwise be used for mini-batches is a little heart breaking.

Risposta accettata

Joss Knight
Joss Knight il 14 Mar 2023
Just increase the MiniBatchSize and it'll use more memory.
  6 Commenti
Christopher McCausland
Christopher McCausland il 14 Mar 2023
Hi Joss,
That makes much more sense, thank you for the explination too.
I will be able to eek out the final 10% of GPU utilsation by finding the exact minibatchsize that cases the fail. Regardless, as you mentioned, down-sampling the data should allow for larger minibatchsize size too.
I will wait for an answer to as i'll need partition to be true before I can use DispatchInBackground. Ideally, I would like to distrabute this over multiple GPU workers so hopefully I can get partition working.
In the mean time I will mark this question as answered and will @ you in the next one if I can get partition behaving.
Thank you!
Joss Knight
Joss Knight il 14 Mar 2023
You may never get that 10% so don't get your hopes up! Also, the best utilization is not necessarily at the highest batch size.
Why not ask a new question where you show your code for your datastore and one of us can help you make it partitionable.

Accedi per commentare.

Più risposte (0)

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by