@Amir_Rosenfeld, the steps are:
get AWS Deep learning AMI ubuntu 16.04 with CUDA 9
Clone pytorch/torchvision from master
Run setup.py 31 for pytorch following the instructions on the pytorch website, but leave out magma
Run setup.py 31 for torchvision but only after giving permisions to easy-install in the anaconda folder AND edit the setup.py 31 file so that torch isn’t a requirement (I noticed that for some reason it didn’t see the pytorch installation from source, and would try to re-install the latest conda version, which doesn’t support cuda 9)
For me, when I used conda or pip directly for torch or vision, it ruined everything. Note that I still use conda for the steps in the github tutorial:
export CMAKE_PREFIX_PATH="$(dirname $(which conda))/…/" # [anaconda root directory]
Install basic dependencies
conda install numpy pyyaml mkl setuptools cmake cffi