Distributed data parallel training for streaming datasets
Hi ! According to https://huggingface.co/docs/datasets/use_with_pytorch#stream-data you can use the pytorch DataLoader with `num_workers>0` to distribute the shards across your workers (it uses `torch.utils.data.get_worker_info()` to get the worker ID and select the right subsets of shards to use)
### Feature request

Any documentations for the the `load_dataset(streaming=True)` for (multi-node multi-GPU) DDP training? 

### Motivation

Given a bunch of data files, it is expected to split them onto different GPUs. Is there a guide or documentation?

### Your contribution

Does it requires manually split on data files for each worker in `DatasetBuilder._split_generator()`? What is`IterableDatasetShard` expected to do?