This chapter provides a comprehensive introduction to Data Version Control (DVC), a tool essential for data versioning in machine learning. Learners will explore the motivation behind data versioning, understand its differences from code versioning, and experiment with a simple classification problem. They will review basic Git commands, learn about DVC, and practice setting up a repository. The chapter concludes with an overview of DVC’s features and use cases, including versioning data and models, CI/CD for machine learning, experiment tracking, pipelines, and more.

Data Versioning Motivation

Anatomy of a Machine Learning Model

Differences Between Data and Code Versioning

Understanding Hyperparameters

Introduction to DVC

Working with Git CLI

Review DVC CLI

DVC features and use cases

DVC pipelines

CI/CD for machine learning

This chapter delves into the setup of DVC, encompassing aspects such as installation, initialization of the repository, and the utilization of the .dvcignore file. It further navigates through the exploration of DVC cache and staging files, imparting knowledge on how to add and remove files, manage caches, and comprehend the underlying mechanisms using the MD5 hash. The chapter also elucidates on DVC remotes, distinguishing them from Git remotes, and guides you on how to add, list, and modify them. Lastly, it teaches you how to interact with these remotes by pushing and pulling data, checking out specific versions, and fetching data to the cache.

DVC Setup and Initialization

#!/bin/bash

# Initialize DVC in the workspace
dvc init

# Check DVC version
dvc version

# Commit the changes using Git
git add .
git commit -m "initial commit"

Setting up DVC

# Add patterns of files dvc should ignore, which could improve
# the performance. Learn more at
# https://dvc.org/doc/user-guide/dvcignore

# Ignore all files in the 'dataset' directory
dataset/____

# But don't ignore 'dataset/myData.csv'
!dataset/____

# Ignore all .json files
____

#!/bin/bash

# Check if JSON files are actually ignored in dvcignore
dvc check-ignore -d

# Add patterns of files dvc should ignore, which could improve
# the performance. Learn more at
# https://dvc.org/doc/user-guide/dvcignore

# Ignore all files in the 'dataset' directory
dataset/*

# But don't ignore 'dataset/myData.csv'
!dataset/myData.csv

# Ignore all .json files
*.json

.dvcignore Patterns

DVC Cache and Staging Files

#!/bin/bash

# Add dataset to DVC
dvc add dataset.csv

# Check DVC cache is populated
find .dvc/cache -type f

# Unstage the metadata file
dvc remove dataset.csv.dvc

# Clean the cache
dvc gc -w


Working with DVC Cache

Understanding .dvc Files

Configuring DVC Remotes

Purpose of DVC Remotes

#!/bin/bash

# Set up a local DVC remote
dvc remote add --local localremote /tmp/dvc

# List the DVC remotes
dvc remote list

# Modify the existing remote to a new location `/tmp/dvc-new`
dvc remote modify --local localremote url /tmp/dvc-new

# List the local remotes again
dvc remote list

Setup a DVC Remote

Interacting with DVC Remotes

#!/bin/bash

# Add the `dataset.csv` to DVC cache.
dvc add dataset.csv

# Commit the corresponding `.dvc` file to Git
git commit -m "tracking dataset.csv"

# Push the dataset to the DVC remote
dvc push

# Run pull command
dvc pull

Versioning Data using DVC Remote

#!/bin/bash

# Inspect the Git commit history
git log

# File MD5 value
md5sum dataset.csv

# Roll back the changes made to the dataset metadata file by one commit
git reset --hard HEAD~1
# Alternatively
git revert

# Update the dataset
dvc checkout

Checking out Versioned Data

DVC Configuration and Data Management

This chapter focuses on automating ML pipelines using DVC. Learners create a configuration file containing settings and hyperparameters. They also learn about pipeline visualization using directed acyclic graphs and use commands to describe dependencies, commands, and outputs. Execution of DVC pipelines is covered, including local model training and how Git tracks DVC metadata. Additionally, learners explore metrics and plots tracking in DVC, including how to print metrics, create plot files, and compare metrics and plots across different pipeline stages.

Code organization and refactoring

Understanding parameter files in DVC

Write a parameter file

Writing and visualizing DVC pipelines

Designing a DVC pipeline

Visualizing a DVC pipeline

Executing DVC pipelines

DVC pipeline execution concepts

Execute a ML model training pipeline

Evaluation: Metrics and plots in DVC

Tracking DVC Metrics

Adding plots to dvc.yaml

Congratulations!

Pipelines in DVC

Delve into Data Version Control (DVC), a tool for managing and versioning ML data. Explore its role in the ML lifecycle, differentiate data versioning from code versioning, and examine DVC’s features and use cases. Learn about DVC setup, including cache management and remotes, and discover its applications in CI/CD, experiment tracking, and pipelines. Automate ML pipelines, emphasizing code modularization, and practice executing them efficiently. Conclude with model evaluation, exploring metric tracking in DVC for informed decision-making.


This course offers a comprehensive introduction to Data Version Control (DVC), a tool designed for efficient management and versioning of machine learning data. You will get an understanding of the machine learning product lifecycle, differentiating data versioning from code versioning and exploring DVC’s features and use cases.

<h2>Exploring DVC features</h2>
You will understand the motivations behind data versioning, the machine learning lifecycle, and DVC’s distinct features and use cases. You will also learn about DVC setup, covering installation, repository initialization, and the .dvcignore file. You will explore DVC cache and staging files, learn to add and remove files, manage caches, and understand the underlying mechanisms. You will learn about DVC remotes, explain the distinction between DVC and Git remotes, add remotes, list them, and modify them. You will learn to interact with remotes, push and pull data, check out specific versions, and fetch data to the cache.

<h2>Automate and evaluate</h2>
You will be motivated to automate ML pipelines, emphasizing modularization of code and the creation of a configuration file. You will be introduced to DVC pipelines as directed acyclic graphs, with hands-on experience in adding stages and their inputs and outputs. You will practice executing these pipelines efficiently to enable different use cases in machine learning model training. The course concludes with a focus on evaluation, showcasing how metrics and plots are tracked in DVC.

Supervised Learning with scikit-learn

Introduction to Git

Explore Data Version Control for ML data management. Master setup, automate pipelines, and evaluate models seamlessly.

Purpose of DVC Remotes

Exercice interactif pratique