Frequently Asked Questions
Q: I want to run operations on the GPU, but Storch seems to hang?
Depending on your hardware, the CUDA version and capability settings, CUDA might need to do just-in-time compilation of your kernels, which can take a few minutes. The result is cached, so it should load faster on subsequent runs.
If you're unsure, you can watch the size of the cache:
watch -d du -sm ~/.nv/ComputeCacheIf it's still growing, it's very likely that CUDA is doing just-in-time compilation.
You can also increase the cache size to up to 4GB, to avoid recomputation:
export CUDA_CACHE_MAXSIZE=4294967296Q: What about GPU support on my Mac?
Recent PyTorch versions provide a new backend based on Apple’s Metal Performance Shaders (MPS). The MPS backend enables GPU-accelerated training on the M1/M2 architecture. Right now, there's no ARM build of PyTorch in JavaCPP and MPS ist not enabled. If you have an M1/M2 machine and want to help, check the umbrella issue for macosx-aarch64 support.