Introduction to DVC

1. Introduction to DVC

Hello again. In this video, we will learn about code and data versioning with Git and DVC.

2. Git as Version Control

Git is a version control system used to track code changes in projects. Git allows users to work independently and make changes locally without needing a constant connection to a central server. Git's distributed nature provides advantages such as offline work, easy branching and merging, and robust version history management. Users can collaborate efficiently and maintain code integrity in a decentralized manner.

3. Git as Version Control

We can interact with Git via its CLI, short for Command Line Interface. CLI commands are issued on terminal, or shell. Git can track contents inside a specific folder, called a repository. It consists of files and folders that are tracked, in addition to Git metadata. The metadata is stored in the .git folder.

4. Data Version Control (DVC)

Now, let's learn about DVC, which is short for Data Version Control. DVC is an open source tool to manage data, similar to Git. One of the strengths of DVC is its integration with Git. While Git is great for code, DVC specializes in data. This means we can manage both code changes and data changes in a unified manner.

5. Git vs DVC CLI

The DVC CLI follows very closely to the Git CLI, the latter of which we are familiar with from our prerequisite courses. Let's review common Git commands and compare them to DVC commands. To start a Git repository inside a folder, we use the 'git init' command. This also creates the .git folder. Similarly, we start by initializing a DVC repository in our working folder using 'dvc init'. Next, we can edit files as needed and use the 'git add' command to stage our changes for commit. To add data files to DVC, we use the 'dvc add' command followed by the path to the data file. This tells DVC to start tracking this data file for changes. To version stamp a state, we can use 'git commit' command followed by an informative commit message. Once we've made changes to our data files, we can update DVC's tracking information using 'dvc commit'. It captures the current state of all tracked data files and records it in DVC. 'dvc commit' doesn't allow usage of commit message like Git.

6. Git vs DVC CLI

To send our committed changes to the remote repository, we use the 'git push' command. Similarly, when we want to send our data changes to a remote data server, we use 'dvc push'. Conversely, to keep our local repository up to date with changes made by others on the remote server, we can use 'git pull'. Similar to Git, to retrieve and synchronize data changes from the remote data server, we use 'dvc pull'. It fetches the latest data updates, keeping our local copies up to date. Finally, if we're starting fresh and want to clone an existing repository from a remote, such as GitHub, we can use the 'git clone' command. We just provide the repository URL, and Git will create a copy of the entire repository on our local machine. If we need to download a specific file or directory tracked by DVC from a remote source, we can use 'dvc get'.

7. Let's practice!

Great work on learning about Git and DVC. Let's review what you have learned.

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Introduction to Data Versioning with DVC

IntermediateSkill Level

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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.

Exercise 1: Data Versioning Motivation Exercise 2: Anatomy of a Machine Learning Model Exercise 3: Differences Between Data and Code Versioning Exercise 4: Understanding Hyperparameters Exercise 5: Introduction to DVC

Current Exercise

Exercise 6: Working with Git CLI Exercise 7: Review DVC CLI Exercise 8: DVC features and use cases Exercise 9: DVC pipelines Exercise 10: 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.

Exercise 1: DVC Setup and Initialization Exercise 2: Setting up DVC Exercise 3: .dvcignore Patterns Exercise 4: DVC Cache and Staging Files Exercise 5: Working with DVC Cache Exercise 6: Understanding .dvc Files Exercise 7: Configuring DVC Remotes Exercise 8: Purpose of DVC Remotes Exercise 9: Setup a DVC Remote Exercise 10: Interacting with DVC Remotes Exercise 11: Versioning Data using DVC Remote Exercise 12: Checking out Versioned Data