DVC (Data Version Control) basics in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is DVC in simple terms?

DVC is a tool that helps you save and track changes in your data and machine learning models, just like how Git tracks code changes.

beginner

How does DVC store large data files without putting them directly in Git?

DVC stores large files outside Git in a special storage called remote storage, and keeps small pointers in Git to track them.

beginner

What command do you use to start tracking a data file with DVC?

You use dvc add <filename> to tell DVC to track a data file.

beginner

Why is DVC useful for machine learning projects?

Because it helps keep track of data versions and model changes, making it easy to reproduce results and collaborate with others.

beginner

What is a DVC remote?

A DVC remote is a storage location (like cloud or server) where DVC saves your large data files safely outside your code repository.

Which command initializes DVC in a project?

A git init

B dvc start

C dvc init

D dvc create

Use dvc init to set up DVC in your project folder.

What does dvc add data.csv do?

A Converts data.csv to a Git file

B Deletes data.csv

C Uploads data.csv to remote storage immediately

D Tracks data.csv with DVC and creates a pointer file

It tells DVC to track data.csv and creates a small file to track it in Git.

Where does DVC store large data files by default?

A In a remote storage configured by the user

B Directly inside the Git repository

C In the system's temp folder

D On GitHub servers

DVC stores large files in remote storage like cloud or network drives, not inside Git.

Which file does DVC create to track data files added with dvc add?

A *.dvc file

B README.md

C .gitignore

D config.yaml

DVC creates a small .dvc file that acts as a pointer to the large data file.

Why should you use DVC with Git in machine learning projects?

A To speed up code execution

B To track both code and data versions easily

C To replace Git completely

D To avoid using any cloud storage

DVC works with Git to track data and code versions together for better project management.

Explain how DVC helps manage large data files in a machine learning project.

Think about how Git handles code and how DVC extends that for data.

Describe the basic steps to start using DVC in a new project.

Start from setting up DVC to saving data safely.

Practice

1. What is the main purpose of using dvc add in a project?

easy

A. To push code changes to a remote Git server

B. To initialize a new Git repository

C. To start tracking a data file or directory with DVC

D. To remove data files from the project

Solution

1. Step 1: Understand the role of dvc add

   dvc add is used to tell DVC to track a data file or directory, creating a pointer file in Git.

2. Step 2: Differentiate from other commands

   Commands like dvc init start DVC, while dvc push syncs data remotely. dvc add specifically tracks data.

3. Final Answer:

   To start tracking a data file or directory with DVC -> Option C

4. Quick Check:

   dvc add tracks data files [OK]

Hint: Remember: add means track data files with DVC [OK]

Common Mistakes:

• Confusing dvc add with dvc init
• Thinking dvc add pushes data remotely
• Assuming dvc add initializes Git

2. Which command correctly initializes DVC in an existing Git repository?

easy

A. dvc start

B. dvc init

C. git dvc init

D. dvc create

Solution

1. Step 1: Identify the DVC initialization command

   The correct command to initialize DVC in a Git repo is dvc init.

2. Step 2: Eliminate incorrect options

   dvc start and dvc create are not valid DVC commands. git dvc init is invalid syntax.

3. Final Answer:

   dvc init -> Option B

4. Quick Check:

   DVC init command = dvc init [OK]

Hint: Use dvc init to start DVC in your repo [OK]

Common Mistakes:

• Typing dvc start instead of dvc init
• Prefixing with git incorrectly
• Using non-existent commands like dvc create

3. Given the following commands run in order:

git init
 dvc init
 dvc add data.csv
 git add data.csv.dvc
 git commit -m "Add data"
 dvc push

What happens after dvc push is executed?

medium

A. The data file is deleted locally after upload

B. Only the data.csv.dvc pointer file is pushed to Git remote

C. The Git repository is cloned to remote storage

D. The actual data file data.csv is uploaded to remote storage

Solution

1. Step 1: Understand dvc push behavior

   dvc push uploads the actual large data files tracked by DVC to the configured remote storage, not just Git files.

2. Step 2: Differentiate Git and DVC storage roles

   Git stores small pointer files like data.csv.dvc, while DVC manages big data files separately in remote storage.

3. Final Answer:

   The actual data file data.csv is uploaded to remote storage -> Option D

4. Quick Check:

   dvc push uploads data files remotely [OK]

Hint: dvc push uploads big data files, not just pointers [OK]

Common Mistakes:

• Thinking dvc push only pushes Git files
• Confusing dvc push with git push
• Assuming data files are deleted after push

4. You ran dvc add dataset.csv but forgot to commit the generated dataset.csv.dvc file to Git. What problem will you face?

medium

A. DVC will not track the data file until the pointer file is committed

B. The data file will be deleted automatically

C. Git will track the data file instead of DVC

D. No problem; DVC tracks data without Git commits

Solution

1. Step 1: Understand the role of the .dvc pointer file

   The dataset.csv.dvc file is a small pointer tracked by Git that tells DVC about the data file version.

2. Step 2: Consequence of not committing the pointer file

   If you don't commit this pointer file, Git and collaborators won't know about the data version, so DVC tracking is incomplete.

3. Final Answer:

   DVC will not track the data file until the pointer file is committed -> Option A

4. Quick Check:

   Pointer file commit = DVC tracking active [OK]

Hint: Always commit .dvc pointer files after dvc add [OK]

Common Mistakes:

• Assuming data files are tracked without pointer commits
• Thinking data files get deleted automatically
• Believing Git tracks large data files directly

5. You have a large dataset tracked by DVC and a remote storage configured. Your teammate cloned the Git repo but the data files are missing locally. Which command should they run to get the data files?

hard

A. dvc pull

B. dvc add

C. git pull

D. git clone

Solution

1. Step 1: Understand what dvc pull does

   dvc pull downloads the actual data files from remote storage to the local machine based on the pointer files in Git.

2. Step 2: Differentiate from Git commands

   git pull updates code and pointer files but does not fetch large data files. dvc add tracks new data, and git clone clones the repo initially.

3. Final Answer:

   dvc pull -> Option A

4. Quick Check:

   Use dvc pull to fetch data files locally [OK]

Hint: Use dvc pull to download data after cloning repo [OK]

Common Mistakes:

• Running only git pull expecting data files
• Trying dvc add to get data files
• Confusing git clone with data download