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)

RarArchiveLoader

class torchdata.datapipes.iter.RarArchiveLoader(datapipe: IterDataPipe[Tuple[str, BufferedIOBase]], *, length: int = - 1)

Decompresses rar binary streams from input Iterable Datapipes which contains tuples of path name and rar binary stream, and yields a tuple of path name and extracted binary stream (functional name: load_from_rar).

Note

The nested RAR archive is not supported by this DataPipe due to the limitation of the archive type. Please extract outer RAR archive before reading the inner archive.

Parameters:

• datapipe – Iterable DataPipe that provides tuples of path name and rar binary stream

• length – Nominal length of the DataPipe

Example

>>> from torchdata.datapipes.iter import FileLister, FileOpener
>>> datapipe1 = FileLister(".", "*.rar")
>>> datapipe2 = FileOpener(datapipe1, mode="b")
>>> rar_loader_dp = datapipe2.load_from_rar()
>>> for _, stream in rar_loader_dp:
>>>     print(stream.read())
b'0123456789abcdef'