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)

Multiplexer

class torchdata.datapipes.iter.Multiplexer(*datapipes)

Yields one element at a time from each of the input Iterable DataPipes (functional name: mux).

As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, and so on. It ends when the shortest input DataPipe is exhausted.

Parameters:

datapipes – Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted

Example

>>> # xdoctest: +REQUIRES(module:torchdata)
>>> from torchdata.datapipes.iter import IterableWrapper
>>> dp1, dp2, dp3 = IterableWrapper(range(3)), IterableWrapper(range(10, 15)), IterableWrapper(range(20, 25))
>>> list(dp1.mux(dp2, dp3))
[0, 10, 20, 1, 11, 21, 2, 12, 22]