Step 4: Accessing and Updating Data

Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.

How to Access and Load Datasets with Deep Lake

Colab Notebook

Loading Datasets

Deep Lake Datasets can be loaded from a variety of storage locations using:

import deeplake

# Local Filepath
ds = deeplake.load('./my_dataset_path') # Similar functionality to deeplake.dataset(path)

# S3
ds = deeplake.load('s3://my_dataset_bucket', creds={...})

# Public Dataset hosted by Activeloop
## Activeloop Storage - See Step 6
ds = deeplake.load('hub://activeloop/public_dataset_name')

# Dataset in another organization on Activeloop Platform
ds = deeplake.load('hub://org_name/dataset_name')

Since ds = deeplake.dataset(path)can be used to both create and load datasets, you may accidentally create a new dataset if there is a typo in the path you provided while intending to load a dataset. If that occurs, simply use ds.delete() to remove the unintended dataset permanently.

Referencing Tensors

Deep Lake allows you to reference specific tensors using keys or via the "." notation outlined below.

Note: data is still not loaded by these commands.

### NO HIERARCHY ###
ds.images # is equivalent to
ds['images']

ds.labels # is equivalent to
ds['labels']

### WITH HIERARCHY ###
ds.localization.boxes # is equivalent to
ds['localization/boxes']

ds.localization.labels # is equivalent to
ds['localization/labels']

Accessing Data

Data within the tensors is loaded and accessed using the .numpy() , .data() , and .tobytes() commands. When the underlying data can be converted to a numpy array, .data() and .numpy() return equivalent objects.

# 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
                                    
# frame = ds.videos[0][4].numpy()   # Fetch the 5th frame in the 1st video 
                                    # and return a NumPy array
                              
text_labels = ds.labels[0].data()['value'] # 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

The .numpy()method produces an exception if all samples in the requested tensor do not have a uniform shape. If that's the case, running .numpy(aslist=True)returns a list of NumPy arrays, where the indices of the list correspond to different samples.

Updating Data

Existing data in a Deep Lake dataset can be updated using:

ds.images[1] = deeplake.read('https://i.postimg.cc/Yq2SNz9J/photo-1534567110243-8875d64ca8ff.jpg') # If the URI is not public, credentials should be specified using deeplake.read(URI, creds = {...})

ds.labels[1] = 'giraffe' # Tensors of htype = 'class_label' can be updated with either numeric values or text

Image.fromarray(ds.images[1].numpy())