Step 1: Hello World

Installing Deep Lake and accessing your first Deep Lake Dataset.

PreviousDeep Learning NextStep 2: Creating Deep Lake Datasets

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How to Install Deep Lake and Get Started

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. .

! pip install deeplake

Fetching Your First Deep Lake Dataset

Let's load MNIST, the hello world dataset of machine learning.

First, instantiate a Dataset by pointing to its storage location. Datasets hosted on Activeloop Platform are typically identified by the namespace of the organization followed by the dataset name: activeloop/mnist-train.

import deeplake

dataset_path = 'hub://activeloop/mnist-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 . You can fetch data by calling the .numpy() method, which reads data into a NumPy array.

# Indexing
img = ds.images[0].numpy()              # Fetch the 1st image and return a NumPy array
label = ds.labels[0].numpy(aslist=True) # Fetch the 1st label and store it as a 
                                        # as a list
                              
text_labels = ds.labels[0].data()['text'] # Fetch the first labels and return them as text

# Slicing
imgs = ds.images[0:100].numpy() # Fetch 100 images and return a NumPy array
                                # The method above produces an exception if 
                                # the images are not all the same size

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