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)
Header¶
- class torchdata.datapipes.iter.Header(source_datapipe: IterDataPipe[T_co], limit: Optional[int] = 10)¶
Yields elements from the source DataPipe from the start, up to the specfied limit (functional name:
header).If you would like to manually set the length of a DataPipe to a certain value; we recommend you to use
LengthSetter.- Parameters:
source_datapipe – the DataPipe from which elements will be yielded
limit – the number of elements to yield before stopping
Example
>>> from torchdata.datapipes.iter import IterableWrapper >>> dp = IterableWrapper(range(10)) >>> header_dp = dp.header(3) >>> list(header_dp) [0, 1, 2]
