Hello everyone,
I am trying to deploy a 3D-UNet model (from here: https://github.com/wolny/pytorch-3dunet 6) on CPU instances and I am running into similar problems. My input size is (96, 96, 96) and I one of the convolution in the decoder uses more than 18 GB of memory…
Is there a way to use less memory (preferably without reducing the complexity of the network or the size of the input) ? I saw on other posts that this was due to a particular implementation of the convolution on CPU; is there anything that can be done in that direction?
In general do you have any tips, tools, frameworks to deploy such models on CPU? I heard about AWS neo, Intel OpenVINO but I am wondering if it will really solve my problem.
Thank you for the help!