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)

Cycler

class torchdata.datapipes.iter.Cycler(source_datapipe: IterDataPipe[T_co], count: Optional[int] = None)

Cycles the specified input in perpetuity by default, or for the specified number of times (functional name: cycle).

If the ordering does not matter (e.g. because you plan to shuffle later) or if you would like to repeat an element multiple times before moving onto the next element, use Repeater.

Parameters:

• source_datapipe – source DataPipe that will be cycled through

• count – the number of times to read through source_datapipe` (if ``None, it will cycle in perpetuity)

Example

>>> from torchdata.datapipes.iter import IterableWrapper
>>> dp = IterableWrapper(range(3))
>>> dp = dp.cycle(2)
>>> list(dp)
[0, 1, 2, 0, 1, 2]