"memory_allocated()" and "max_memory_allocated()" to monitor memory
occupied by tensors, and use "memory_reserved()" and
"max_memory_reserved()" to monitor the total amount of memory managed
by the caching allocator. Calling "empty_cache()" releases all
unused cached memory from PyTorch so that those can be used by
other GPU applications. However, the occupied GPU memory by tensors
will not be freed so it can not increase the amount of GPU memory
available for PyTorch.
For more advanced users, we offer more comprehensive memory
benchmarking via "memory_stats()". We also offer the capability to
capture a complete snapshot of the memory allocator state via
"memory_snapshot()", which can help you understand the underlying
allocation patterns produced by your code.
To debug memory errors, set "PYTORCH_NO_CUDA_MEMORY_CACHING=1" in your
environment to disable caching.
hipFFT/rocFFT plan cache
Setting the size of the cache for hipFFT/rocFFT plans is not
supported.