MemPool

class torch.cuda.memory.MemPool(*args, **kwargs)

MemPool represents a pool of memory in a caching allocator. Currently, it’s just the ID of the pool object maintained in the CUDACachingAllocator.

Parameters:

• allocator (torch._C._cuda_CUDAAllocator, optional) – a torch._C._cuda_CUDAAllocator object that can be used to define how memory gets allocated in the pool. If allocator is None (default), memory allocation follows the default/ current configuration of the CUDACachingAllocator.

• use_on_oom (bool) – a bool that indicates if this pool can be used as a last resort if a memory allocation outside of the pool fails due to Out Of Memory. This is False by default.

• no_split (bool) – a bool that indicates if this pool should not split a segment. This is False by default.

property id: tuple[int, int]

Returns the ID of this pool as a tuple of two ints.

snapshot(include_traces=True)

Return a snapshot of the CUDA memory allocator pool state across all devices.

Interpreting the output of this function requires familiarity with the memory allocator internals.

Parameters:

include_traces – Whether to include trace entries in the snapshot. If True (default), all trace entries are included. If False, no trace entries are included (lightweight/fast snapshot).

Note

See Memory management for more details about GPU memory management.

use_count()

Returns the reference count of this pool.

Return type:

int