Quickstart

A jump-start guide to using Deep Lake.

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.

$ pip3 install deeplake

Fetching Your First Deep Lake Dataset

Let's load the Visdrone dataset, a rich dataset with many object detections per image. Datasets hosted on Activeloop Platform are typically identified by host organization name followed by the dataset name: activeloop/visdrone-det-train.

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. You can fetch data by calling the .numpy() or .data() methods:

# 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:

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:

ds.summary()

The dataset can be visualized in Activeloop Platform, or using an iframe in a Jupyter notebook:

ds.visualize()

Visualizing datasets in Activeloop Platform will unlock more features and faster performance compared to visualization in Jupyter notebooks.

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:

Getting Started

Creating Datasets

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