---
title: Accessing DataLoaders
description: >-
  These properties will match exactly what was returned in your dataloader hooks
  or passed to the Trainer,
canonical: 'https://lightning.ai/docs/pytorch/stable/reference/data/access'
---
# Accessing DataLoaders {#accessing-dataloaders}

In the case that you require access to the `torch.utils.data.DataLoader` or `torch.utils.data.Dataset` objects, DataLoaders for each step can be accessed
via the trainer properties `train_dataloader`,
`val_dataloaders`,
`test_dataloaders`, and
`predict_dataloaders`.

```python
dataloaders = trainer.train_dataloader
dataloaders = trainer.val_dataloaders
dataloaders = trainer.test_dataloaders
dataloaders = trainer.predict_dataloaders
```

These properties will match exactly what was returned in your `*_dataloader` hooks or passed to the `Trainer`,
meaning that if you returned a dictionary of dataloaders, these will return a dictionary of dataloaders.

## Replacing DataLoaders {#replacing-dataloaders}

If you are using a `CombinedLoader`. A flattened list of DataLoaders can be accessed by doing:

```python
from lightning.pytorch.utilities import CombinedLoader

iterables = {"dl1": dl1, "dl2": dl2}
combined_loader = CombinedLoader(iterables)
# access the original iterables
assert combined_loader.iterables is iterables
# the `.flattened` property can be convenient
assert combined_loader.flattened == [dl1, dl2]
# for example, to do a simple loop
updated = []
for dl in combined_loader.flattened:
    new_dl = apply_some_transformation_to(dl)
    updated.append(new_dl)
# it also allows you to easily replace the dataloaders
combined_loader.flattened = updated
```

---

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- Parent: [Data](/docs/pytorch/2.6.6/reference/data.md)