Feature request: IterableDataset.push_to_hub
+1 +1 +1, should be possible now? :) https://huggingface.co/blog/xethub-joins-hf Haha we're working hard to integrate Xet in the HF back-end, it will enable cool use cases :)

Anyway about `IterableDataset.push_to_hub`, I'd be happy to to provide guidance and answer questions if anyone wants to start a first simple implementation of this +1 +1 +1 +1

Currently running into this when filtering [Common Corpus](https://huggingface.co/datasets/PleIAs/common_corpus) for Dutch entries.

Extra points for somehow making it resumable on error. 11 TB is a lot of data to stream on a home connection without encountering any sort of errors along the way. If it can help, IterableDataset does implement `.state_dict()` and `.load_state_dict()` that you can use to resume a stream already +1 +1 +1 Just added a first implementation for `IterableDataset.push_to_hub()` :)

I'll do a new release soon, in the meantime feel free to install `datasets` from source to try it out !
### Feature request

It'd be great to have a lazy push to hub, similar to the lazy loading we have with `IterableDataset`.

Suppose you'd like to filter [LAION](https://huggingface.co/datasets/laion/laion400m) based on certain conditions, but as LAION doesn't fit into your disk, you'd like to leverage streaming:
```
from datasets import load_dataset

dataset = load_dataset("laion/laion400m", streaming=True, split="train")
```
Then you could filter the dataset based on certain conditions:
```
filtered_dataset = dataset.filter(lambda example: example['HEIGHT'] > 400)
```

In order to persist this dataset and push it back to the hub, one currently needs to first load the entire filtered dataset on disk and then push:

```
from datasets import Dataset

Dataset.from_generator(filtered_dataset.__iter__).push_to_hub(...)
```
It would be great if we can instead lazy push to the data to the hub (basically stream the data to the hub), not being limited by our disk size:
```
filtered_dataset.push_to_hub("my-filtered-dataset")
```

### Motivation

This feature would be very useful for people that want to filter huge datasets without having to load the entire dataset or a filtered version thereof on their local disk.

### Your contribution

Happy to test out a PR :)