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)

BatchAsyncMapper

class torchdata.datapipes.iter.BatchAsyncMapper(source_datapipe, async_fn: Callable, batch_size: int, input_col=None, output_col=None, max_concurrency: int = 32, flatten: bool = True)

Combines elements from the source DataPipe to batches and applies a coroutine function over each element within the batch concurrently, then flattens the outpus to a single, unnested IterDataPipe (functional name: async_map_batches).

Parameters:

• source_datapipe – Source IterDataPipe

• async_fn – The coroutine function to be applied to each batch of data

• batch_size – The size of batch to be aggregated from source_datapipe

• input_col –

  Index or indices of data which fn is applied, such as:

  • None as default to apply fn to the data directly.

  • Integer(s) is used for list/tuple.

  • Key(s) is used for dict.

• output_col –

  Index of data where result of fn is placed. output_col can be specified only when input_col is not None

  • None as default to replace the index that input_col specified; For input_col with multiple indices, the left-most one is used, and other indices will be removed.

  • Integer is used for list/tuple. -1 represents to append result at the end.

  • Key is used for dict. New key is acceptable.

• max_concurrency – Maximum concurrency to call async functions. (Default: 32)

• flatten – Determine if the batches get flatten in the end (Default: True) If False, outputs will be in batches of size batch_size

Example

>>> from torchdata.datapipes.iter import IterableWrapper
>>> async def mul_ten(x):
...     await asyncio.sleep(1)
...     return x * 10
>>> dp = IterableWrapper(range(50))
>>> dp = dp.async_map_batches(mul_ten, 16)
>>> list(dp)
[0, 10, 20, 30, ...]
>>> dp = IterableWrapper([(i, i) for i in range(50)])
>>> dp = dp.async_map_batches(mul_ten, 16, input_col=1)
>>> list(dp)
[(0, 0), (1, 10), (2, 20), (3, 30), ...]
>>> dp = IterableWrapper([(i, i) for i in range(50)])
>>> dp = dp.async_map_batches(mul_ten, 16, input_col=1, output_col=-1)
>>> list(dp)
[(0, 0, 0), (1, 1, 10), (2, 2, 20), (3, 3, 30), ...]
# Async fetching html from remote
>>> from aiohttp import ClientSession
>>> async def fetch_html(url: str, **kwargs):
...     async with ClientSession() as session:
...         resp = await session.request(method="GET", url=url, **kwargs)
...         resp.raise_for_status()
...         html = await resp.text()
...     return html
>>> dp = IterableWrapper(urls)
>>> dp = dp.async_map_batches(fetch_html, 16)