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)

InMemoryCacheHolder

class torchdata.datapipes.iter.InMemoryCacheHolder(source_dp: IterDataPipe[T_co], size: Optional[int] = None)

Stores elements from the source DataPipe in memory, up to a size limit if specified (functional name: in_memory_cache). This cache is FIFO - once the cache is full, further elements will not be added to the cache until the previous ones are yielded and popped off from the cache.

Parameters:

• source_dp – source DataPipe from which elements are read and stored in memory

• size – The maximum size (in megabytes) that this DataPipe can hold in memory. This defaults to unlimited.

Example

>>> from torchdata.datapipes.iter import IterableWrapper
>>> source_dp = IterableWrapper(range(10))
>>> cache_dp = source_dp.in_memory_cache(size=5)
>>> list(cache_dp)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]