Memory Management

You can use 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.