Discussion using datasets in offline mode

`datasets.load_dataset("csv", ...)` breaks if you have no connection (There is already this issue https://github.com/huggingface/datasets/issues/761 about it). It seems to be the same for metrics too.

I create this ticket to discuss a bit and gather what you have in mind or other propositions.

Here are some points to open discussion:
- if you want to prepare your code/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.
- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset("MY_PATH/csv.py", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.
- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least "freeze" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.
 
WDYT? (thks)



Comments:
No comments ?

I think it would be very cool. I'm currently working on a cluster from Compute Canada, and I have internet access only when I'm not in the nodes where I run the scripts. So I was expecting to be able to use the wmt14 dataset until I realized I needed internet connection even if I downloaded the data already. I'm going to try option 2 you mention for now though! Thanks ;)

Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.

@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?

here is my way to load a dataset offline, but it **requires** an online machine
1. (online machine)
```
import datasets
data = datasets.load_dataset(...)
data.save_to_disk(/YOUR/DATASET/DIR)
```
2. copy the dir from online to the offline machine
3. (offline machine)
```
import datasets
data = datasets.load_from_disk(/SAVED/DATA/DIR)
```

HTH.

> here is my way to load a dataset offline, but it **requires** an online machine
> 
> 1. (online machine)
> 
> ```
> 
> import datasets
> 
> data = datasets.load_dataset(...)
> 
> data.save_to_disk(/YOUR/DATASET/DIR)
> 
> ```
> 
> 2. copy the dir from online to the offline machine
> 
> 3. (offline machine)
> 
> ```
> 
> import datasets
> 
> data = datasets.load_from_disk(/SAVED/DATA/DIR)
> 
> ```
> 
> 
> 
> HTH.



I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.

Let me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :) 

I already note the "freeze" modules option, to prevent local modules updates. It would be a cool feature.

----------

> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?

Indeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.
For example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do
```python
load_dataset("./my_dataset")
```
and the dataset script will generate your dataset once and for all.

----------

About I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.
cf #1724 

The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)
You can now use them offline
```python
datasets = load_dataset('text', data_files=data_files)
```

We'll do a new release soon

Already fixed by:
- #1726

> 

really helps

@albertvillanova 
datasets version： 2.10.1
I load_dataset and save_to_disk sucessfully on windows10, and I copy the dataset dir
into a ubuntu system, and when I load_from_disk(dir),  something weird happens:


```
load_from_disk('/LLM/data/wiki')
  File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/load.py", line 1874, in load_from_disk
    return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options)
  File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/dataset_dict.py", line 1309, in load_from_disk
    dataset_dict[k] = Dataset.load_from_disk(
  File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1543, in load_from_disk
    fs_token_paths = fsspec.get_fs_token_paths(dataset_path, storage_options=storage_options)
  File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/core.py", line 610, in get_fs_token_paths
    chain = _un_chain(urlpath0, storage_options or {})
  File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/core.py", line 325, in _un_chain
    cls = get_filesystem_class(protocol)
  File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/registry.py", line 232, in get_filesystem_class
    raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: /LLM/data/wiki
```
It seems that something went wrong on the arrow file?
How can I solve this , since currently I can not save_to_disk on ubuntu system

It looks like a bug in `fsspec`, can you try updating `fsspec` (and maybe `datasets` as well) ?