Support cloud storage in load_dataset

Would be nice to be able to do

```python
data_files=["s3://..."]  # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```

The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.

This has been requested several times already. Some users want to use their data from private cloud storage to train models

related:

https://github.com/huggingface/datasets/issues/3490
https://github.com/huggingface/datasets/issues/5244
[forum](https://discuss.huggingface.co/t/how-to-use-s3-path-with-load-dataset-with-streaming-true/25739/2)

Comments:
Or for example an archive on GitHub releases!  Before I added support for JXL (locally only, PR still pending) I was considering hosting my files on GitHub instead...

+1 to this. I would like to use 'audiofolder' with a data_dir that's on S3, for example. I don't want to upload my dataset to the Hub, but I would find all the fingerprinting/caching features useful.

Adding to the conversation, Dask also uses `fsspec` for this feature.

[Dask: How to connect to remote data](https://docs.dask.org/en/stable/how-to/connect-to-remote-data.html)

Happy to help on this feature :D 

+1 to this feature request since I think it also tackles my use-case. I am collaborating with a team, working with a loading script which takes some time to generate the dataset artifacts. It would be very handy to use this as a cloud cache to avoid duplicating the effort. 

Currently we could use `builder.download_and_prepare(path_to_cloud_storage, storage_options, ...)`  to cache the artifacts to cloud storage, but then `builder.as_dataset()` yields `NotImplementedError: Loading a dataset cached in SomeCloudFileSystem is not supported`

Makes sense ! If you want to load locally a dataset that you download_and_prepared on a cloud storage, you would use `load_dataset(path_to_cloud_storage)` indeed. It would download the data from the cloud storage, cache them locally, and return a `Dataset`.

It seems currently the `cached_path` function handles all URLs by `get_from_cache` that only supports `ftp` and `http(s)` here:
https://github.com/huggingface/datasets/blob/b5672a956d5de864e6f5550e493527d962d6ae55/src/datasets/utils/file_utils.py#L181

I guess one can add another condition that handles `s3://` or `gs://` URLs via `fsspec` here.

I could use this functionality, so I put together a PR using @kyamagu's suggestion to use `fsspec` in `datasets.utils.file_utils`

https://github.com/huggingface/datasets/pull/5580

Thanks @dwyatte for adding support for fsspec urls

Let me just reopen this since the original issue is not resolved

I'm not yet understanding how to use https://github.com/huggingface/datasets/pull/5580 in order to use `load_dataset(data_files="s3://...")`. Any help/example would be much appreciated :) thanks! 

It's still not officially supported x) But you can try to update `request_etag` in `file_utils.py` to use `fsspec_head` instead of `http_head`. It is responsible of getting the ETags of the remote files for caching. This change may do the trick for S3 urls

Thank you for your guys help on this and merging in #5580. I manually pulled the changes to my local datasets package (datasets.utils.file_utils.py) since it only seemed to be this file that was changed in the PR and I'm getting the error: 
InvalidSchema: No connection adapters were found for 's3://bucket/folder/'. I'm calling load_dataset using the S3 URI. When I use the S3 URL I get HTTPError: 403 Client Error. 
Am I not supposed to use the S3 URI? How do I pull in the changes from this merge? I'm running datasets 2.10.1. 

The current implementation depends on gcsfs/s3fs being able to authenticate through some other means e.g., environmental variables. For AWS, it looks like you can set `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN`

Note that while testing this just now, I did note a discrepancy between gcsfs and  s3fs that we might want to address where gcsfs passes the timeout from `storage_options` [here](https://github.com/huggingface/datasets/blob/3e6269979fc80ae8939294d26298897f0db5b84d/src/datasets/utils/file_utils.py#L333) down into the `aiohttp.ClientSession.request`, but s3fs does not handle this (tries to pass to the `aiobotocore.session.AioSession` constructor raising `TypeError: __init__() got an unexpected keyword argument 'requests_timeout'`).

It seems like some work trying to unify kwargs across different fsspec implementations, so if the plan is to pass down `storage_options`, I wonder if we should just let users control the timeout (and other kwargs) using that and if not specified, use the default?

> Note that while testing this just now, I did note a discrepancy between gcsfs and s3fs that we might want to address where gcsfs passes the timeout from storage_options [here](https://github.com/huggingface/datasets/blob/3e6269979fc80ae8939294d26298897f0db5b84d/src/datasets/utils/file_utils.py#L333) down into the aiohttp.ClientSession.request, but s3fs does not handle this (tries to pass to the aiobotocore.session.AioSession constructor raising TypeError: __init__() got an unexpected keyword argument 'requests_timeout').

> It seems like some work trying to unify kwargs across different fsspec implementations, so if the plan is to pass down storage_options, I wonder if we should just let users control the timeout (and other kwargs) and if not specified, use the default?

@lhoestq here's a small PR for this: https://github.com/huggingface/datasets/pull/5673



@lhoestq sorry for being a little dense here but I am very keen to use fsspec / adlfs for for a larger image dataset I have for object detection. I have to keep it on Azure storage and would also like to avoid a full download or zipping (so use `load_dataset(..., streaming=True)`. So this development is godsend :) only... I am unable to make it work.

Would you expect the setup to work for:
- azure blob storage
- image files (not the standard formats `json`, `parquet`....

? I appreciate that you mostly focus on s3 but it seems that, similar to the [remaining cloud storage functionality](https://huggingface.co/docs/datasets/filesystems#cloud-storage), it should also work for Azure blob storage.


I would imagine that something like (Streaming true or false):
```python
d = load_dataset("new_dataset.py", storage_options=storage_options, split="train")
```
would work with 
```python
# new_dataset.py
....
_URL="abfs://container/image_folder``` 

archive_path = dl_manager.download(_URL)
split_metadata_paths = dl_manager.download(_METADATA_URLS)
return [
    datasets.SplitGenerator(
        name=datasets.Split.TRAIN,
        gen_kwargs={
            "annotation_file_path": split_metadata_paths["train"],
            "files": dl_manager.iter_files(archive_path)
},
      ),
...
```
but I get 
```
Traceback (most recent call last):
...        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/load.py", line 1797, in load_dataset
    builder_instance.download_and_prepare(
  File "~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/builder.py", line 890, in download_and_prepare
    self._download_and_prepare(
  File "~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/builder.py", line 1649, in _download_and_prepare
    super()._download_and_prepare(
  File "~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/builder.py", line 963, in _download_and_prepare
    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "~/.cache/huggingface/modules/datasets_modules/datasets/new_dataset/dd26a081eab90074f41fa2c821b458424fde393cc73d3d8241aca956d1fb3aa0/new_dataset_script.py", line 56, in _split_generators
    archive_path = dl_manager.download(_URL)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/download/download_manager.py", line 427, in download
    downloaded_path_or_paths = map_nested(
                               ^^^^^^^^^^^
  File "~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/utils/py_utils.py", line 435, in map_nested
    return function(data_struct)
           ^^^^^^^^^^^^^^^^^^^^^
  File "~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/download/download_manager.py", line 453, in _download
    return cached_path(url_or_filename, download_config=download_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/utils/file_utils.py", line 206, in cached_path
    raise ValueError(f"unable to parse {url_or_filename} as a URL or as a local path")
ValueError: unable to parse abfs://container/image_folder as a URL or as a local path

```


What version of `datasets` are you using ?

@lhoestq 
hello, i still have problem with loading json from S3:


storage_options = {
    "key": xxxx,
    "secret": xxx,
    "endpoint_url": xxxx
}
path = 's3://xxx/xxxxxxx.json'
dataset = load_dataset("json", data_files=path, storage_options=storage_options)

and it throws an error:
TypeError: AioSession.__init__() got an unexpected keyword argument 'hf'
and I use the lastest 2.14.4_dev0 version

Hi @lhoestq, thanks for getting back to me :) you have been busy over the summer I see... I was on `2.12.0`. I have updated to `2.14.4`. 

Now `d = load_dataset("new_dataset.py", storage_options=storage_options, split="train", streaming=True)` works for Azure blob storage (with a local data loader script) when I explicitly list all blobs  (I am struggling to make `fs.ls(<path>)` work in the script to make the list available to the download manager).

 Any chance that it could work out-of-the-box by supplying just the image folder, not the full list of image filenames? It seems that `dl_manager.download(_URL)` always wants one or more (possibly archived) files. In my situation, where I don't want to archive or download, it would be great to just supply the folder (seems reasonably doable with fsspec).

Let me know if there is anything I can do to help.

Thanks,

> Any chance that it could work out-of-the-box by supplying just the image folder, not the full list of image filenames? It seems that dl_manager.download(_URL) always wants one or more (possibly archived) files. In my situation, where I don't want to archive or download, it would be great to just supply the folder (seems reasonably doable with fsspec).

@mayorblock  This is not supported right now, you have to use archives or implement a way to get the list by yourself

> TypeError: AioSession.init() got an unexpected keyword argument 'hf'

@hjq133 Can you update `fsspec` and try again ?

```python
pip install -U fsspec
```

thanks for your suggestion，it works now ! 

I'm seeing same problem as @hjq133 with following versions:

```
datasets==2.15.0
(venv) ➜  finetuning-llama2 git:(main) ✗ pip freeze | grep s3fs   
s3fs==2023.10.0
(venv) ➜  finetuning-llama2 git:(main) ✗ pip freeze | grep fsspec
fsspec==2023.10.0
```

> @lhoestq hello, i still have problem with loading json from S3:
> 
> storage_options = { "key": xxxx, "secret": xxx, "endpoint_url": xxxx } path = 's3://xxx/xxxxxxx.json' dataset = load_dataset("json", data_files=path, storage_options=storage_options)
> 
> and it throws an error: TypeError: AioSession.**init**() got an unexpected keyword argument 'hf' and I use the lastest 2.14.4_dev0 version

I am trying to do the same thing, but the loading is just hanging, without any error. 
@lhoestq is there any documentation how to load from private s3 buckets?


Hi ! S3 support is still experimental. It seems like there is an extra `hf` field passed to the `s3fs` storage_options that causes this error. I just check the source code of `_prepare_single_hop_path_and_storage_options` and I think you can try passing explicitly your own `storage_options={"s3": {...}}`. Also note that it's generally better to load datasets from HF (we run extensive tests and benchmarks for speed and robustness)

That worked! Thanks
It seems thought that `data_dir=...` doesn't work on s3, only `data_files`.


@lhoestq Would this work either with an Azure Blob Storage Container or its respective Azure Machine Learning Datastore? If yes, what would that look like in code? I've tried a couple of combinations but no success so far, on the latest version of `datasets`. I need to migrate a dataset to the Azure cloud, `load_dataset("path_to_data")` worked perfectly while the files were local only. Thank you!

@mayorblock would you mind sharing how you got it to work? What did you pass as `storage_options`? Would it maybe work without a custom data loader script?

This ticket would be of so much help.

@lhoestq I've been using this feature for the last year on GCS without problem, but I think we need to fix an issue with S3 and then document the supported calling patterns to reduce confusion

It looks like `datasets` uses a default `DownloadConfig` which is where some potentially unintended storage options are getting passed to fsspec

```
DownloadConfig(
	cache_dir=None, 
	force_download=False, 
	resume_download=False, 
	local_files_only=False, 
	proxies=None, 
	user_agent=None, 
	extract_compressed_file=False, 
	force_extract=False, 
	delete_extracted=False, 
	use_etag=True, 
	num_proc=None, 
	max_retries=1, 
	token=None, 
	ignore_url_params=False, 
	storage_options={'hf': {'token': None, 'endpoint': 'https://huggingface.co'}}, 
	download_desc=None
)
```
(specifically the `storage_options={'hf': {'token': None, 'endpoint': 'https://huggingface.co'}}` part)

`gcsfs` is robust to the extra key in storage options for whatever reason, but `s3fs` is not (haven't dug into why). I'm unable to test `adlfs` but it looks like people here got it working

Is this an issue that needs fixed with s3fs? Or can we avoid passing these default storage options in some cases?

Update: I think probably https://github.com/huggingface/datasets/pull/6127 is where these default storage options were introduced

Hmm not sure, maybe it has to do with `_prepare_single_hop_path_and_storage_options` returning the "hf" storage options when it shouldn't

Also running into this issue downloading a parquet dataset from S3 (Upload worked fine using [current main branch](https://github.com/huggingface/datasets/commit/90b896193a0a315789022580eb4c80305d168d4d)).
`dataset = Dataset.from_parquet('s3://path-to-file')`
raises 
`TypeError: AioSession.__init__() got an unexpected keyword argument 'hf'`
Found that the issue is introduced in [#6028](https://github.com/huggingface/datasets/commit/14f6edd9222e577dccb962ed5338b79b73502fa5#diff-8a868b8c1127c5cbe7870bbeec30bd2cbec6696a378e65b66782a6935e3f412e)

When commenting out the __post_init__ part to set 'hf', I am able to download the dataset.

Can someone paste a complete example for getting this to work? Running this:

```python
from datasets import load_from_disk, load_dataset
from os import environ
from s3fs import S3FileSystem

storage_options = {
    "key": "AzureDiamond",
    "secret": "hunter2",
    "endpoint_url": "https://fly.storage.tigris.dev"
}

model_name = "Qwen/Qwen2.5-32B"
dataset_name = "mlabonne/FineTome-100k"

dataset = load_dataset(
    f"s3://datnybakfu/model-ready/{model_name}/{dataset_name}",
    storage_options=storage_options,
    filesystem=S3FileSystem(),
    streaming=True,
)
```

I get the following error trace:

<details>
<summary>Stacktrace (folded)</summary

```
---------------------------------------------------------------------------

FileNotFoundError                         Traceback (most recent call last)

[<ipython-input-19-0ae7d5f39c46>](https://localhost:8080/#) in <cell line: 29>()
     27 dataset_name = "mlabonne/FineTome-100k"
     28 
---> 29 dataset = load_dataset(
     30     f"s3://datnybakfu/model-ready/{model_name}/{dataset_name}",
     31     storage_options=storage_options,

2 frames

[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
   2130 
   2131     # Create a dataset builder
-> 2132     builder_instance = load_dataset_builder(
   2133         path=path,
   2134         name=name,

[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
   1851         download_config = download_config.copy() if download_config else DownloadConfig()
   1852         download_config.storage_options.update(storage_options)
-> 1853     dataset_module = dataset_module_factory(
   1854         path,
   1855         revision=revision,

[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)
   1733         )
   1734     else:
-> 1735         raise FileNotFoundError(f"Couldn't find any data file at {relative_to_absolute_path(path)}.")
   1736 
   1737 

FileNotFoundError: Couldn't find any data file at /content/s3:/datnybakfu/model-ready/Qwen/Qwen2.5-32B/mlabonne/FineTome-100k.
```

</details>

Hi, using `s3://` as first argument in load_dataset directly is not supported. You can pass a local path or a HF repository (in the form `username/dataset_name`)

cc @Wauplin if you have a script to move data from S3 to HF ? I know there is this code adapted from https://github.com/fsspec/filesystem_spec/issues/909 that works well (it creates one commit per file)

```python
from multiprocessing.pool import ThreadPool

import fsspec
from tqdm import tqdm

s3_storage_options = {}  # add S3 credentials here if needed, e.g. {"key": aws_access_key_id, "secret": aws_secret_access_key}
a = fsspec.get_mapper("s3://bucket/dataset_folder", **s3_storage_options)
b = fsspec.get_mapper("hf://datasets/username/dataset_name")

def f(k):
 b[k]=a[k]

with ThreadPool(32) as p:
 keys = [key for key in a.keys() if key and not key.endswith("/")]  # ignore root and directories
 for _ in tqdm(p.imap_unordered(f, keys), total=len(keys)):
   pass
```

It's more complex than this. Unfortunately, the best I can advice is to download the data locally (at least partially) and then  use `huggingface-cli upload-large-folder` (see [docs](https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-large-folder)) to make sure the data is properly uploaded. 

The solution suggested above works ok as long as the number of files is not too big. The problem of 1 file == 1 commit is that 1. it can be slow 2. it can lead to concurrency issues (concurrent commits) 3. if can lead to slow repo (once 10000 commits have been made) and 4. it can lead to rate limits (maximum number of commits per hour). All of this is taken care of with the `upload_large_folder` but requires the data to be locally downloaded. If local disk is smaller than the dataset size, you can download and reupload per chunk (folder by folder for instance).