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)

Shuffle

class torchdata.dataloader2.adapter.Shuffle(enable=True)

Shuffle DataPipes adapter allows control over all existing Shuffler (shuffle) DataPipes in the graph.

Parameters:

enable –

Optional boolean argument to enable/disable shuffling in the DataPipe graph. True by default.

• True: Enables all previously disabled ShufflerDataPipes. If none exists, it will add a new shuffle at the end of the graph.

• False: Disables all ShufflerDataPipes in the graph.

• None: No-op. Introduced for backward compatibility.

Example:

dp = IterableWrapper(range(size)).shuffle()
dl = DataLoader2(dp, [Shuffle(False)])
assert list(range(size)) == list(dl)