> For the complete documentation index, see [llms.txt](https://docs-v3.activeloop.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs-v3.activeloop.ai/v3.0.15/performant-dataloader.md).

# Performant Dataloader (Beta)

## How to use Deep Lake's performant Dataloader built and optimized in C++

Deep Lake offers an Alpha version of its dataloader that was build and optimized in C++. The new dataloader is 2-3X faster in many applications, but since it is an experimental state, is not as reliable as the pure-python dataloader described [here](/v3.0.15/getting-started/connecting-to-ml-frameworks.md). 

Both dataloaders can be used interchangeably, and their syntax varies as shown below 

### Pure-Python Dataloader

```python
train_loader = ds_train.pytorch(num_workers = 8,
                                transform = transform, 
                                batch_size = 32,
                                tensors=['images', 'labels'],
                                shuffle = True)
```

### C++ Dataloader

{% hint style="danger" %}
The C++ dataloader is currently available only on Linux machines. It also returns image tensors as PIL images, not numpy arrays (like the python dataloader).

Starting from deeplake 3.1.0, the C++ dataloader will require the dataset's parent organization to be on a Growth of Enterprise plan. Querying Activeloop datasets will be enabled for everyone. 
{% endhint %}

<pre class="language-python"><code class="lang-python">from deeplake.experimental import dataloader
<strong>
</strong><strong>train_loader = dataloader(ds)\
</strong>                .transform(transform)\
                .batch(32)\
                .shuffle()\
                .pytorch(tensors=['images', 'labels'], num_workers = 8)
</code></pre>


---

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