I’m working on a shared system, and the admins are making various CUDA versions available via
module load. Unfortunately, the installed modules don’t contain
libcuda.so, but it looks like pretty much everything else is there. Is there a way to install
CuArrays in this case? There is a stub for a
libcuda.so, which fails with
Building against CUDA driver stubs, which is not supported..
and a different version in
/usr/lib64 (even before ‘module load’), which fails with
Initializing CUDA driver failed: no CUDA hardware available (code 100)..
I can see that the other libs are correctly detected, just this
libcuda.so problem prevents me from proceeding. I don’t know enough about CUDA, but is it possible to build
CuArrays without it? Tensorflow and pytorch work with CUDA support on this system.
/usr/lib64/libcuda.so is sym-linked to
libcuda.so.418.39, regardless of the CUDA tool kit version I load with
module load. I tried loading a CUDA 10.1.105, which is the newest, but that fails like all the others.