Attention

June 2024 Status Update: Removing DataPipes and DataLoader V2

We are re-focusing the torchdata repo to be an iterative enhancement of torch.utils.data.DataLoader. We do not plan on continuing development or maintaining the [DataPipes] and [DataLoaderV2] solutions, and they will be removed from the torchdata repo. We’ll also be revisiting the DataPipes references in pytorch/pytorch. In release torchdata==0.8.0 (July 2024) they will be marked as deprecated, and in 0.9.0 (Oct 2024) they will be deleted. Existing users are advised to pin to torchdata==0.8.0 or an older version until they are able to migrate away. Subsequent releases will not include DataPipes or DataLoaderV2. Please reach out if you suggestions or comments (please use this issue for feedback)

IterableWrapper

class torchdata.datapipes.iter.IterableWrapper(iterable, deepcopy=True)

Wraps an iterable object to create an IterDataPipe.

Parameters:

• iterable – Iterable object to be wrapped into an IterDataPipe

• deepcopy – Option to deepcopy input iterable object for each iterator. The copy is made when the first element is read in iter().

Note

If deepcopy is explicitly set to False, users should ensure that the data pipeline doesn’t contain any in-place operations over the iterable instance to prevent data inconsistency across iterations.

Example

>>> # xdoctest: +SKIP
>>> from torchdata.datapipes.iter import IterableWrapper
>>> dp = IterableWrapper(range(10))
>>> list(dp)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]