Title: Memory leak when wrapping datasets into PyTorch Dataset without explicit deletion
Author: iamwangyabin (NONE)
State: closed
Created: 2024-09-28T14:00:47Z

### Describe the bug

I've encountered a memory leak when wrapping the HuggingFace dataset into a PyTorch Dataset. The RAM usage constantly increases during iteration if items are not explicitly deleted after use.








### Steps to reproduce the bug


Steps to reproduce:

Create a PyTorch Dataset wrapper for 'nebula/cc12m':
````
from torch.utils.data import Dataset
from tqdm import tqdm
from datasets import load_dataset
from torchvision import transforms

Image.MAX_IMAGE_PIXELS = None

class CC12M(Dataset):
    def __init__(self, path_or_name='nebula/cc12m', split='train', transform=None, single_caption=True):
        self.raw_dataset = load_dataset(path_or_name)[split]

        if transform is None:
            self.transform = transforms.Compose([
                transforms.Resize((224, 224)),
                transforms.CenterCrop(224),
                transforms.ToTensor(),
                transforms.Normalize(
                    mean=[0.48145466, 0.4578275, 0.40821073],
                    std=[0.26862954, 0.26130258, 0.27577711]
                )
            ])
        else:
            self.transform = transforms.Compose(transform)

        self.single_caption = single_caption
        self.length = len(self.raw_dataset)

    def __len__(self):
        return self.length

    def __getitem__(self, index):
        item = self.raw_dataset[index]
        caption = item['txt']
        with io.BytesIO(item['webp']) as buffer:
            image = Image.open(buffer).convert('RGB')
            if self.transform:
                image = self.transform(image)
        # del item  # Uncomment this line to prevent the memory leak
        return image, caption
````

Iterate through the dataset without the del item line in __getitem__.

Observe RAM usage increasing constantly.

Add del item at the end of __getitem__:

```
def __getitem__(self, index):
    item = self.raw_dataset[index]
    caption = item['txt']
    with io.BytesIO(item['webp']) as buffer:
        image = Image.open(buffer).convert('RGB')
        if self.transform:
            image = self.transform(image)
    del item  # This line prevents the memory leak
    return image, caption
```


Iterate through the dataset again and observe that RAM usage remains stable.



### Expected behavior


Expected behavior:
RAM usage should remain stable during iteration without needing to explicitly delete items.

Actual behavior:
RAM usage constantly increases unless items are explicitly deleted after use

### Environment info

- `datasets` version: 2.21.0
- Platform: Linux-4.18.0-513.5.1.el8_9.x86_64-x86_64-with-glibc2.28
- Python version: 3.12.4
- `huggingface_hub` version: 0.24.6
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1

Comments:
> I've encountered a memory leak when wrapping the HuggingFace dataset into a PyTorch Dataset. The RAM usage constantly increases during iteration if items are not explicitly deleted after use.

Datasets are memory mapped so they work like SWAP memory. In particular as long as you have RAM available the data will stay in RAM, and get paged out once your system needs RAM for something else (no OOM).

related: https://github.com/huggingface/datasets/issues/4883