Comments (2)
Hi @etudemin
I think there are few reasons for this:
- By default pytorch would use cudnn auto-tuner to find the best algorithm for the task, which I believe might consume more GPU memory(if available) for a faster inference time. You might want to turn it off and also make pytorch deterministic to get the minimum memory usage. You can check this documentation for more details.
- You might want to clean the cuda cache before you record the memory usage by using
torch.cuda.empty_cache()
andtorch.cuda.reset_max_memory_allocated()
. - You might want to use the pytorch provided
torch.cuda.max_memory_allocated()
to record the maximum memory usage.
Cheers,
Jiayu
from cvp-mvsnet.
Hi @JiayuYANG
Thank you very much for the kind replies. I will try that, thanks again!
Sincerely,
etudemin
from cvp-mvsnet.
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