fix: loading of datasets from Disk(#7373)
@nepfaff Could you confirm if this fixes the issue for you? I checked Memray, and everything looked good on my end.

Install: `pip install git+https://github.com/sam-hey/datasets.git@fix/concatenate_datasets`
 Will aim to get to this soon. I don't have a rapid testing pipeline setup but need to wait for some AWS nodes to become free I now set up a small experiment:

```python
# Log initial RAM usage
    process = psutil.Process(os.getpid())
    initial_ram = process.memory_info().rss / (1024 * 1024)  # Convert to MB
    logging.info(f"Initial RAM usage: {initial_ram:.2f} MB")

    chunk_datasets = [
        Dataset.load_from_disk(dataset_path, keep_in_memory=False) for _ in range(N)
    ]
    combined_dataset = concatenate_datasets(chunk_datasets)

    # Log final RAM usage
    final_ram = process.memory_info().rss / (1024 * 1024)  # Convert to MB
    ram_diff = final_ram - initial_ram
    logging.info(f"Final RAM usage: {final_ram:.2f} MB")
    logging.info(f"RAM usage increase: {ram_diff:.2f} MB")
```

The RAM usage is linearly correlated with `N` on datasets master!

For my test dataset:
- N=5 => RAM usage increase: 26302.91 MB
- N=10 => RAM usage increase: 52315.18 MB
- N=20 => RAM usage increase: 104510.65 MB
- N=40 => RAM usage increase: 209166.30 MB

Unfortunately, your patch doesn't seem to change this:
```bash
pip install git+https://github.com/sam-hey/datasets.git@fix/concatenate_datasets
pip list | grep datasets
datasets                 3.5.1.dev0
```
Gives exactly the same RAM statistics.

**Edit:** The results are a bit flawed as the memory increase all seems to come from `Dataset.load_from_disk(dataset_path, keep_in_memory=False)` here (which I don't think should happen either?) and not from `concatenate_datasets`. This seems different from my large-scale setup that runs out of memory during `concatenate_datasets` but I don't seem to be able to replicate this here... Thanks a lot, @nepfaff, for taking a look at this! It seems that `concatenate_datasets()` is fixed with this PR. I can also confirm that loading a large number of files requires significant memory. However, as I understand it, this is expected/a bug since the memory consumption stems from `pa.memory_map()`, which returns a memory-mapped file.

This behavior might be related to this bug: https://github.com/apache/arrow/issues/34423 

<img width="1728" alt="Screenshot 2025-04-03 at 16 01 11" src="https://github.com/user-attachments/assets/475691d8-3aba-4d7e-b8ef-5e7552c70b14" />
 Great ! have you tested that it also fixes the memory issue in your case @iamollas ?

Happy to know that it works for you @sam-hey ! Looking forward to merging this The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7489). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Fixes dataset loading from disk by ensuring that memory maps and streams are properly closed. 


For more details, see https://github.com/huggingface/datasets/issues/7373.