Issue: Unable to load dataset that was saved with `save_to_disk`
State: closed

### Describe the bug

I get the following error message: You are trying to load a dataset that was saved using `save_to_disk`. Please use `load_from_disk` instead.



### Steps to reproduce the bug

1. Save a dataset with `save_to_disk`
2. Try to load it with `load_datasets`

### Expected behavior

I am able to load the dataset again with `load_datasets` which most packages uses over `load_from_disk`. I want to have a workaround that allows me to create the same indexing that `push_to_hub` creates for you before using `save_to_disk` - how can that be achieved?

### Environment info

datasets 2.17.1, python 3.10

`save_to_disk` uses a special serialization that can only be read using `load_from_disk`.

Contrary to `load_dataset`, `load_from_disk` directly loads Arrow files and uses the dataset directory as cache.

On the other hand `load_dataset` does a conversion step to get Arrow files from the raw data files (could be in JSON, CSV, Parquet etc.) and caches them in the `datasets` cache directory (default is `~/.cache/huggingface/datasets`). We haven't implemented any logic in `load_dataset` to support datasets saved with `save_to_disk` because they don't use the same cache.

EDIT: note that you can save your dataset in Parquet format locally using `.to.parquet()` (make sure to shard in multiple files your dataset if it's multiple GBs - you can use `.shard()` + `.to_parquet()` to do that) and you'll be able to reload it using `load_dataset`

@lhoestq, so is it correctly understood that if I run `to_parquet()` and then `save_to_disk()`, I can load it with `load_dataset`? If yes, then it would resolve this issue (and should probably be documented somewhere 😄)

Here is an example:
```python
ds.to_parquet("my/local/dir/data.parquet")

# later
ds = load_dataset("my/local/dir")
```

and for bigger datasets:
```python
num_shards = 1024  # set number of files to save (e.g. try to have files smaller than 5GB)
for shard_idx in num_shards:
    shard = ds.shard(index=shard_idx, num_shards=num_shards)
    shard.to_parquet(f"my/local/dir/{shard_idx:05d}.parquet")  # 00000.parquet to 01023.parquet

# later
ds = load_dataset("my/local/dir")
```


I hope this helps :)

Thanks for helping out! Does this approach work with `s3fs`? e.g. something like this:

```python
import s3fs
s3 = s3fs.S3FileSystem(anon=True)
with s3.open('mybucket/new-file.parquet', 'w') as f:
    ds.to_parquet(f)
```

This is instead of `save_to_disk` to save to an S3 bucket.

Otherwise, I am not sure how to make this work when saving the dataset to an S3 bucket. Would `dataset.set_format("arrow")` work as a replacement?

`load_dataset` does't support S3 buckets unfortunately :/

> `load_dataset` does't support S3 buckets unfortunately :/

I am aware but I have some code that downloads it to disk before using that method. The most important part is to store it in a format that load_dataset is compatible with. 

Feel free to use Parquet then :)

I ended up with this. Not ideal to save to local disk, but it works and loads via `load_datasets` after downloading from S3 with another method.

```python
with tempfile.TemporaryDirectory() as dir:
    dataset_nbytes = ds._estimate_nbytes()
    max_shard_size_local = convert_file_size_to_int(max_shard_size)
    num_shards = int(dataset_nbytes / max_shard_size_local) + 1

    for shard_idx in range(num_shards):
        shard = ds.shard(index=shard_idx, num_shards=num_shards)
        shard.to_parquet(f"{dir}/{shard_idx:05d}.parquet")
    
    fs.upload(
        lpath=dir,
        rpath=s3_path,
        recursive=True,
    )
```