

TITLE
How to train over VERY LARGE dataset?

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### System Info

```shell
I am using transformer trainer while meeting the issue.
The trainer requests torch.utils.data.Dataset as input, which loads the whole dataset into the memory at once. Therefore, when the dataset is too large to load, there's nothing I can do except using IterDataset, which loads samples of data seperately, and results in low efficiency. 
I wonder if there are any tricks like Sharding in huggingface trainer.
Looking forward to your reply.
@sgugger
```


### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

None

### Expected behavior

```shell
some tricks like fairseq Sharding very large datasets https://fairseq.readthedocs.io/en/latest/getting_started.html
```
