Step 9: Dataset Version Control

Managing changes to your datasets using Version Control.

How to Use Version Control in Deep Lake

Deep Lake dataset version control allows you to manage changes to datasets with commands very similar to Git. It provides critical insights into how your data is evolving, and it works with datasets of any size!

Let's check out how dataset version control works in Deep Lake! If you haven't done so already, please download and unzip the animals dataset from Step 2.

First let's create a Deep Lake dataset in the ./version_control_deeplake folder.

import deeplake
import numpy as np
from PIL import Image

# Set overwrite = True for re-runability
ds = deeplake.dataset('./version_control_deeplake', overwrite = True)

# Create a tensor and add an image
with ds:
    ds.create_tensor('images', htype = 'image', sample_compression = 'jpeg')
    ds.images.append(deeplake.read('./animals/cats/image_1.jpg'))

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

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

Commit

To commit the data added above, simply run ds.commit:

first_commit_id = ds.commit('Added image of a cat')

print('Dataset in commit {} has {} samples'.format(first_commit_id, len(ds)))

Next, let's add another image and commit the update:

with ds:
    ds.images.append(deeplake.read('./animals/dogs/image_3.jpg'))
    
second_commit_id = ds.commit('Added an image of a dog')

print('Dataset in commit {} has {} samples'.format(second_commit_id, len(ds)))

The second image in this dataset is a picture of a dog:

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

Log

The commit history starting from the current commit can be show using ds.log:

log = ds.log()

This command prints the log to the console and also assigns it to the specified variable log. The author of the commit is the username of the Activeloop account that logged in on the machine.

Branch

Branching takes place by running the ds.checkout command with the parameter create = True . Let's create a new branch dog_flipped, flip the second image (dog), and create a new commit on that branch.

ds.checkout('dog_flipped', create = True)

with ds:
    ds.images[1] = np.transpose(ds.images[1], axes=[1,0,2])

flipped_commit_id = ds.commit('Flipped the dog image')

The dog image is now flipped and the log shows a commit on the dog_flipped branch as well as the previous commits on main:

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

ds.log()

Checkout

A previous commit of the branch can be checked out using ds.checkout:

ds.checkout('main')

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

As expected, the dog image on main is not flipped.

Diff

Understanding changes between commits is critical for managing the evolution of datasets. Deep Lake's ds.diff function enables users to determine the number of samples that were added, removed, or updated for each tensor. The function can be used in 3 ways:

ds.diff() # Diff between the current state and the last commit

ds.diff(commit_id) # Diff between the current state and a specific commit

ds.diff(commit_id_1, commit_id_2) # Diff between two specific commits

HEAD Commit

Unlike Git, Deep Lake's dataset version control does not have a local staging area because all dataset updates are immediately synced with the permanent storage location (cloud or local). Therefore, any changes to a dataset are automatically stored in a HEAD commit on the current branch. This means that the uncommitted changes do not appear on other branches. Let's see how this works:

You should currently be on the main branch, which has 2 samples. You can check for uncommited changes using:

ds.has_head_changes

Let's add another image:

print('Dataset on {} branch has {} samples'.format('main', len(ds)))

with ds:
    ds.images.append(deeplake.read('./animals/dogs/image_4.jpg'))
    
print('After updating, the HEAD commit on {} branch has {} samples'.format('main', len(ds)))

The 3rd sample is also an image of a dog:

Image.fromarray(ds.images[2].numpy())

Next, if you checkout dog_flipped branch, the dataset contains 2 samples, which is sample count from when that branch was created. Therefore, the additional uncommitted third sample that was added to the main branch above is not reflected when other branches or commits are checked out.

ds.checkout('dog_flipped')

print('Dataset in {} branch has {} samples'.format('dog_flipped', len(ds)))

Finally, when checking our the main branch again, the prior uncommitted changes and available and they are stored in the HEAD commit on main:

ds.checkout('main')

print('Dataset in {} branch has {} samples'.format('main', len(ds)))

The dataset now contains 3 samples and the uncommitted dog image is visible:

Image.fromarray(ds.images[2].numpy())

You can delete any uncommitted changes using the reset command below, which will bring the main branch back to the state with 2 samples.

ds.reset()

print('Dataset in {} branch has {} samples'.format('main', len(ds)))

Merge

Merging is a critical feature for collaborating on datasets. It enables you to modify data on separate branches before making those changes available on the main branch, thus enabling you to experiment on your data without affecting workflows by other collaborators.

We are currently on the main branch where the picture of the dog is right-side-up.

ds.log()

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

We can merge the dog_flipped branch into main using the command below:

ds.merge('dog_flipped')

After merging the dog_flipped branch, we observe that the image of the dog is flipped. The dataset log now has a commit indicating that a commit from another branch was merged to main.

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

ds.log()

Congrats! You just are now an expert in dataset version control! 🎓