# Quickstart

## How to Get Started with Activeloop Deep Lake in Under 5 Minutes

### **Version control, query, and train models while streaming your deep-learning datasets from a cloud of your choice.**

### Installing Deep Lake

Deep Lake can be installed through pip. **By default, Deep Lake does not install dependencies for audio, video, google-cloud, and other features.** [**Details on all installation options are available here**](https://docs.deeplake.ai/en/latest/Installation.html)**.** 

```bash
$ pip3 install deeplake
```

### Fetching Your First Deep Lake Dataset

Let's load the [Visdrone dataset](https://app.activeloop.ai/activeloop/visdrone-det-train), a rich dataset with many object detections per image. [Datasets](https://docs-v3.activeloop.ai/3.1.1/broken-reference) hosted on Activeloop Platform are typically identified by host organization name followed by the dataset name: `activeloop/visdrone-det-train`.

```python
import deeplake

dataset_path = 'hub://activeloop/visdrone-det-train'
ds = deeplake.load(dataset_path) # Returns a Deep Lake Dataset but does not download data locally
```

### Reading Samples From a Deep Lake Dataset

Data is not immediately read into memory because Deep Lake operates [lazily](https://en.wikipedia.org/wiki/Lazy_evaluation). You can fetch data by calling the `.numpy()` or `.data()` methods:

```python
# Indexing
image = ds.images[0].numpy() # Fetch the first image and return a numpy array

labels = ds.labels[0].data() # Fetch the labels in the first image

boxes = ds.boxes[0].numpy() # Fetch the bounding boxes in the first image

# Slicing
img_list = ds.labels[0:100].numpy(aslist=True) # Fetch 100 labels and store 
                                               # them as a list of numpy arrays
```

Other metadata such as the mapping between numerical labels and their text counterparts can be accessed using:

```python
labels_list = ds.labels.info['class_names']
```

### Visualizing a Deep Lake Dataset

Deep Lake enables users to visualize and interpret large datasets. The tensor layout for a dataset can be inspected using:

```python
ds.summary()
```

The dataset can be [visualized in Activeloop Platform](https://app.activeloop.ai/activeloop/mnist-train), or using an iframe in a Jupyter notebook:

```python
ds.visualize()
```

{% hint style="info" %}
Visualizing datasets in [Activeloop Platform](https://app.activeloop.ai/) will unlock more features and faster performance compared to visualization in Jupyter notebooks.
{% endhint %}

### Create your own Datasets

You can perform all of the steps above and more with your own datasets! Please check out the links below to learn more:

{% content-ref url="getting-started" %}
[getting-started](https://docs-v3.activeloop.ai/3.1.1/getting-started)
{% endcontent-ref %}

{% content-ref url="tutorials/creating-datasets" %}
[creating-datasets](https://docs-v3.activeloop.ai/3.1.1/tutorials/creating-datasets)
{% endcontent-ref %}

Congratulations, you've got Deep Lake working on your local machine:nerd: