Data Processing Using Parallel Computing

Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.

How to use deeplake.compute for parallelizing workflows

This tutorial is also available as a Colab Notebook

Step 8 in the Getting Started Guide highlights how deeplake.compute can be used to rapidly upload datasets. This tutorial expands further and highlights the power of parallel computing for dataset processing.

Transformations on New Datasets

Computer vision applications often require users to process and transform their data. For example, you may perform perspective transforms, resize images, adjust their coloring, or many others. In this example, a flipped version of the MNIST dataset is created, which may be useful for training a model that identifies text in scenes where the camera orientation is unknown.

First, let's define a function that will flip the dataset images.

import deeplake
from PIL import Image
import numpy as np

@deeplake.compute
def flip_vertical(sample_in, sample_out):
    ## First two arguments are always default arguments containing:
    #     1st argument is an element of the input iterable (list, dataset, array,...)
    #     2nd argument is a dataset sample
    
    # Append the label and image to the output sample
    sample_out.labels.append(sample_in.labels.numpy())
    sample_out.images.append(np.flip(sample_in.images.numpy(), axis = 0))
    
    return sample_out

Next, the existing MNIST dataset is loaded, and deeplake.like is used to create an empty dataset with the same tensor structure.

ds_mnist = deeplake.load('deeplake://activeloop/mnist-train')

#We use the overwrite=True to make this code re-runnable
ds_mnist_flipped = deeplake.like('./mnist_flipped', ds_mnist, overwrite = True)

Finally, the flipping operation is evaluated for the 1st 100 elements in the input dataset ds_in, and the result is automatically stored in ds_out.

flip_vertical().eval(ds_mnist[0:100], ds_mnist_flipped, num_workers = 2)

Let's check out the flipped images:

Image.fromarray(ds_mnist.images[0].numpy())

Image.fromarray(ds_mnist_flipped.images[0].numpy())

Transformations on Existing Datasets

In the previous example, a new dataset was created while performing a transformation. In this example, a transformation is used to modify an existing dataset.

First, download and unzip the small classification dataset below called animals.

338KB

animals.zip

archive

Next, use deeplake.ingest to automatically convert this image classification dataset into Deep Lake format and save it in ./animals_deeplake.

ds = deeplake.ingest('./animals', './animals_deeplake') # Creates the dataset

The first image in the dataset is a picture of a cat:

Image.fromarray(ds.images[0].numpy())

The images in the dataset can now be flipped by evaluating the flip_vertical() transformation function from the previous example. If a second dataset is not specified as an input to .eval(), the transformation is applied to the input dataset.

flip_vertical().eval(ds, num_workers = 2)

The picture of the cat is now flipped:

Image.fromarray(ds.images[0].numpy())

Dataset Processing Pipelines

In order to modularize your dataset processing, it is helpful to create functions for specific data processing tasks and combine them in pipelines. In this example, you can create a pipeline using the flip_vertical function from the first example and the resize function below.

@deeplake.compute
def resize(sample_in, sample_out, new_size):
    ## First two arguments are always default arguments containing:
    #     1st argument is an element of the input iterable (list, dataset, array,...)
    #     2nd argument is a dataset sample
    ## Third argument is the required size for the output images
    
    # Append the label and image to the output sample
    sample_out.labels.append(sample_in.labels.numpy())
    sample_out.images.append(np.array(Image.fromarray(sample_in.images.numpy()).resize(new_size)))
    
    return sample_out

Functions decorated using deeplake.compute can be combined into pipelines using deeplake.compose. Required arguments for the functions must be passed into the pipeline in this step:

pipeline = deeplake.compose([flip_vertical(), resize(new_size = (64,64))])

Just like for the single-function example above, the input and output datasets are created first, and the pipeline is evaluated for the 1st 100 elements in the input dataset ds_in. The result is automatically stored in ds_out.

#We use the overwrite=True to make this code re-runnable
ds_mnist_pipe = deeplake.like('./mnist_pipeline', ds_mnist, overwrite = True)

pipeline.eval(ds_mnist[0:100], ds_mnist_pipe, num_workers = 2)

Recovering From Errors

If an error occurs related to a specific sample_in, deplake.compute will throw a TransformError and the error-causing index or sample can be caught using:

# from deeplake.util.exceptions import TransformError

# try:
#     compute_fn.eval(...)
# except TransformError as e:
#     failed_idx = e.index
#     failed_sample = e.sample

The traceback also typically shows information such as the filename of the data that was causing issues. One the problematic sample has been identified, it should be removed from the list of input samples and the deeplake.compute function should be re-executed.

Congrats! You just learned how to make parallelize your computations using Deep Lake! 🎉