Differences between Dataset and IterableDataset

There are two types of dataset objects, a [`Dataset`] and an [`IterableDataset`].
Whichever type of dataset you choose to use or create depends on the size of the dataset.
In general, an [`IterableDataset`] is ideal for big datasets (think hundreds of GBs!) due to its lazy behavior and speed advantages, while a [`Dataset`] is great for everything else.
This page will compare the differences between a [`Dataset`] and an [`IterableDataset`] to help you pick the right dataset object for you.

## Downloading and streaming

When you have a regular [`Dataset`], you can access it using `my_dataset[0]`. This provides random access to the rows.
Such datasets are also called "map-style" datasets.
For example you can download ImageNet-1k like this and access any row:

```python
from datasets import load_dataset

imagenet = load_dataset("imagenet-1k", split="train")  # downloads the full dataset
print(imagenet[0])
```

But one caveat is that you must have the entire dataset stored on your disk or in memory, which blocks you from accessing datasets bigger than the disk.
Because it can become inconvenient for big datasets, there exists another type of dataset, the [`IterableDataset`].
When you have an `IterableDataset`, you can access it using a `for` loop to load the data progressively as you iterate over the dataset.
This way, only a small fraction of examples is loaded in memory, and you don't write anything on disk.

For example, you can stream the ImageNet-1k dataset without downloading it on disk:

```python
from datasets import load_dataset

imagenet = load_dataset("imagenet-1k", split="train", streaming=True)  # will start loading the data when iterated over
for example in imagenet:
    print(example)
    break
```
The context clearly explains that the main advantage of using an `IterableDataset` for big datasets is its "lazy behavior and speed advantages." It allows for streaming data without needing to download the entire dataset onto disk, which is particularly beneficial for handling large datasets that exceed disk space. This explanation directly addresses the question about the main advantage, making the answer unambiguous.


The question about the main advantage of using an IterableDataset for big datasets is highly relevant to machine learning developers working with NLP applications in the Hugging Face ecosystem. IterableDatasets are crucial for handling large datasets that cannot fit into memory, which is a common scenario in NLP tasks. Understanding the benefits of IterableDatasets can help developers efficiently manage data loading and processing, leading to more effective model training and evaluation. This question directly addresses a practical concern that developers face, making it very useful.


The question "What is the main advantage of using an IterableDataset for big datasets?" is context-independent and can be understood without additional information. It refers to a specific concept (IterableDataset) that is well-documented and understood in the context of data handling and machine learning. The question is clear and does not rely on any external context to be meaningful.

