reference/generated/tensordict.TensorDictBase.memmap_refresh_

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TensorDictBase.memmap_refresh

TensorDictBase.memmap_refresh_(*, allow_pickle: bool | None = None)

Reloads the tensordict from its saved_path, and returns self.

Use it when another process has changed the files: added or removed entries, or written non-tensor data. It calls load_memmap_() on saved_path, then memory-maps the tensordict there again.

Keyword Arguments:

allow_pickle (bool, optional) – whether non-tensor data saved with pickle may be loaded. See load_memmap(). Defaults to None.

Raises:

RuntimeError – if the tensordict is not memory-mapped in a directory, for example a tensordict returned by load_memmap() (for which is_memmap() returns False) or by memmap() without a prefix.

Examples

>>> import tempfile
>>> import torch
>>> from tensordict import TensorDict
>>> with tempfile.TemporaryDirectory() as path:
...     td = TensorDict(a=torch.zeros(3)).memmap(path)
...     _ = TensorDict(a=torch.ones(3), b=torch.ones(3)).memmap(path)
...     _ = td.memmap_refresh_()
...     print(sorted(td.keys()), td["a"])
['a', 'b'] MemoryMappedTensor([1., 1., 1.])

Every tensordict class and every tensorclass has this method. For the other methods, see Methods by task.