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)

InProcessReadingService

class torchdata.dataloader2.InProcessReadingService(prefetch_cnt: int = 0, init_fn: Optional[Callable[[Union[IterDataPipe, MapDataPipe], WorkerInfo], Union[IterDataPipe, MapDataPipe]]] = None, reset_fn: Optional[Callable[[Union[IterDataPipe, MapDataPipe], WorkerInfo, SeedGenerator], Union[IterDataPipe, MapDataPipe]]] = None)

Default ReadingService to serve the DataPipe graph in the main process, and apply graph settings like determinism control to the graph.

Parameters:

• prefetch_cnt – (int, 0 by default): Number of data will be prefetched in the main process.

• init_fn – (Callable, optional): Custom function to be called when the main process starts to iterate over DataPipe graph.

• reset_fn – (Callable, optional): Custom function to be called at the beginning of each epoch with DataPipe, WorkerInfo and SeedGenerator as the expected arguments.

initialize(datapipe: Union[IterDataPipe, MapDataPipe]) → Union[IterDataPipe, MapDataPipe]

ReadingService takes a DataPipe graph, adapts it into a new DataPipe graph based on the custom need. Called once in creating DataLoader2 iterator at first time. Prior to calling this method, the ReadingService object must be picklable.

Parameters:

datapipe – Original DataPipe graph.

Returns:

An adapted or a new DataPipe graph.

initialize_iteration(seed_generator: SeedGenerator, iter_reset_fn: Optional[Callable[[Union[IterDataPipe, MapDataPipe]], Union[IterDataPipe, MapDataPipe]]] = None) → Optional[Callable[[Union[IterDataPipe, MapDataPipe]], Union[IterDataPipe, MapDataPipe]]]

ReadingService spins up service for an epoch. Called at the beginning of every time getting DataLoader2 iterator.

Parameters:

• seed_generator – SeedGenerator object created and managed by DataLoader2. As the single source of randomness, it will govern the determinism for all of random operations with the graph of DataPipes.

• iter_reset_fn – Optional reset function from the prior ReadingServcie when SequentialReadingService chains multiple ReadingServices

Returns:

A new iter_reset_fn to be used by subseqeuent ReadingService

Example

MultiProcessingReadingService starts setting worker seeds per process and prefetching items from the graph.