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DVC Commands for Data Science

Updated: Jan 25

In the fast-paced realm of data science, where collaboration and versioning are pivotal, Data Version Control (DVC) emerges as a game-changer. This guide delves into the essential DVC commands tailored for data scientists. From wrangling large files to orchestrating machine learning pipelines, these commands empower practitioners with efficiency and reproducibility.

DVC commands for Data Science

What are Data Version Control ( DVC ) Commands?

DVC (Data Version Control) is a command-line tool written in Python that complements Git by addressing the unique challenges of versioning and managing large files, datasets, machine learning models, and experiments in data science and machine learning projects.

Benefits of DVC:

  1. Reproducibility: DVC enables the reproducibility of machine learning experiments by versioning not only code but also data, model files, and other artifacts. This ensures that results can be recreated even as datasets and models evolve.

  2. Efficient Versioning: DVC efficiently handles versioning of large files and datasets without duplicating them. Instead, it uses lightweight metafiles (".dvc" files) that reference the actual data, reducing storage requirements.

  3. Collaboration: DVC facilitates collaboration among team members by providing a mechanism to share and manage data and models through remote storage. Multiple team members can work on the same project while keeping their work synchronized.

  4. Integration with Git: DVC seamlessly integrates with Git, leveraging its commands and workflows. This integration allows users to incorporate DVC into their existing Git practices without a steep learning curve.

  5. Large File Handling: DVC is designed to handle large files efficiently. It uses a Git-like approach for versioning, but instead of storing complete copies of files, it stores lightweight references to the actual data, reducing the storage and bandwidth requirements.

  6. Data Sharing: The dvc remote command allows users to share data and models with remote storage, making it easier to collaborate with team members or share results with the wider community. This is particularly useful when dealing with large datasets that are impractical to share through traditional version control systems.

  7. Pipeline Management: DVC provides tools like the dvc run command to define and manage machine learning pipelines. This ensures that data preprocessing, model training, and other pipeline stages are tracked and reproducible.

  8. Flexibility: DVC is agnostic to the underlying storage backend, supporting various remote storage options such as Amazon S3, Google Cloud Storage, and others. This flexibility allows users to choose storage solutions that best fit their needs.

  9. Cache System: DVC incorporates a caching system that optimizes the storage and retrieval of data and models. It avoids redundant downloads and computations, improving overall efficiency.

  10. Open Source and Active Community: DVC is an open-source tool with an active community. This means continuous development, improvements, and support from the community, ensuring that the tool stays relevant and up-to-date with evolving needs in the machine learning and data science domains.

List of DVC Commands

1. init

The init command is used to initialize a new DVC project in a given directory. However, it's important to note that DVC relies on Git for version control, so the initialization process is dependent on Git.

Before initializing DVC, it is recommended to initialize a new Git repository in the directory where you want to set up your data science project. This can be done using the git init command.

git init

The git init command creates a new Git repository, establishing version control for your project.

Once the Git repository is set up, you can then initialize DVC in the same directory using the dvc init command.

dvc init

This command initializes DVC for your project, and it creates a .dvc directory within your project's root directory.

Metadata in .dvc Directory:

The .dvc directory contains all the metadata related to your DVC configuration and files. This includes information about the data files, the structure of your DVC pipeline, and other necessary details for versioning and tracking changes.

The metadata stored in the .dvc directory includes information about the data files that are tracked by DVC, such as their hashes, file sizes, and other relevant details. It also stores information about the configuration settings for DVC in the project.

By organizing this information in the .dvc directory, DVC separates the version control metadata from the actual data files, allowing for efficient tracking of changes without duplicating large datasets.

The purpose of the init command sequence (git init followed by dvc init) is to set up both Git and DVC for version control and data versioning in your project.

This combined initialization ensures that your project is ready for collaboration using Git and can efficiently version and manage large datasets using DVC.

2. remote

The remote command in DVC is a crucial tool for managing and configuring remote storage locations. It allows users to share data with a team, store copies in remote storage, and collaborate effectively. Here's a detailed explanation of the remote command in DVC:

Adding a Remote:

To share data or create a copy in remote storage, you use the dvc remote add command. This command requires specifying a remote name and the URL of the remote storage.

In this example, a remote named dagshub is added, and it points to the specified URL on Dagshub, a collaborative platform for managing and versioning data.

Viewing Remote Storage:

To view the list of configured remote storage locations, you can use the dvc remote list command.

dvc remote list

This command will display a list of configured remotes, in this case, showing the dagshub remote along with its associated URL.

Modifying an Existing Remote:

If there is a need to modify an existing remote, the dvc remote modify command is used. It requires specifying the remote name and the new URL.

This command updates the URL associated with the dagshub remote. It can be useful when changing the storage location or repository associated with a remote.

Renaming or Removing a Remote:

The process of renaming or removing a remote follows a similar pattern. To rename a remote, you can use the dvc remote modify command to change the remote name. To remove a remote, you can use the dvc remote remove command.

# Rename the remote dvc remote modify dagshub new-remote-name # Remove the remote dvc remote remove new-remote-name

This allows users to manage their remote configurations easily, providing flexibility in adapting to changes in collaboration or storage configurations.

3. add

The add command in DVC (Data Version Control) is a fundamental command used to track and version data files within a project. Below is a detailed explanation of the add command:

Tracking Files and Directories:

The primary purpose of the dvc add command is to track files and directories. It allows users to specify which data files or directories should be managed by DVC for versioning.

dvc add ./model ./data

In this example, the add command is used to track the model and data directories. DVC will create corresponding .dvc files for each tracked file or directory, storing metadata and versioning information.

Git Interaction:

When files are added to DVC using dvc add, those files are effectively removed from Git tracking by adding them to the .gitignore file. This is because DVC will manage the versioning and tracking of these files independently.

# .gitignore model data

The .gitignore file ensures that Git doesn't consider these data files as part of its version control, preventing them from being committed to the Git repository. Instead, Git will track pointers to the DVC files (.dvc files) for versioning.

Git Staging Area:

After running the dvc add command, it's necessary to add the generated .dvc files to the Git staging area. This step is crucial for coordinating the versioning of data with the versioning of code and ensuring that the complete state of the project is captured in a Git commit.

git add model.dvc data.dvc .gitignore

By adding the .dvc files to the Git staging area, you signal to Git that these files, along with the associated data files managed by DVC, should be included in the next Git commit.

The add command in DVC is essential for integrating data versioning with the overall version control system (Git). It allows data scientists and developers to manage and version large datasets efficiently without the need to store the actual data in the Git repository.

The separation of data versioning (handled by DVC) from code versioning (handled by Git) is a key feature that enables more efficient collaboration, reduces repository size, and simplifies the tracking of changes in both code and data.

4. remove

The remove command in DVC is used to stop tracking files and directories or to remove a stage from the dvc.yml file.


The primary purpose of the dvc remove command is to stop tracking specific files or directories that were previously added to version control using DVC. Additionally, it can be used to remove a specific stage from the dvc.yml file, which defines the DVC pipeline.

Usage for File/Directory Removal:

To stop tracking a file or directory, the command is used as follows:

dvc remove <file.dvc>

Here, <file.dvc> refers to the DVC file associated with the file or directory that you want to stop tracking.

Usage for Stage Removal (from dvc.yml):

To remove a stage from the dvc.yml file, you would use the dvc remove command with the appropriate stage name:

dvc remove <stage-name>

This removes the specified stage from the dvc.yml file, effectively eliminating it from the DVC pipeline.


It's important to ensure that the file or directory being removed has an extension of .dvc. This is because DVC uses these .dvc files to track and manage metadata related to the actual data files.

Example for File/Directory Removal:

dvc remove model.dvc

This command would stop tracking the data associated with the model.dvc file.

Example for Stage Removal (from dvc.yml):

dvc remove my_stage

This command would remove the stage named my_stage from the dvc.yml file.

Effects on Version Control:

Once a file or directory is removed using dvc remove, DVC will no longer track changes to that file or directory. This means that modifications to those files will not be recorded in subsequent DVC commits.

5. status

The status command in DVC is used to display the changes in the project pipelines and to showcase differences between the cache, workspace, and remote storage.


The primary purpose of the dvc status command is to provide an overview of the modifications made within a DVC project.


Running the dvc status command is simple:

dvc status


The command output typically includes information about changes in the project pipelines, indicating which stages have been modified or need attention.

Pipeline Changes:

The output may include details about changes in the pipeline, such as whether a dependency has been modified, if an output needs to be updated, or if there are any discrepancies between the cache and the workspace.

Cache and Workspace Changes:

It also highlights changes between the cache (where DVC stores the tracked data) and the workspace (the current state of the project).

Example Output:

Pipeline 'example.dvc' is up to date. Nothing to reproduce. Data and pipelines are up to date.

6. commit

The commit command in DVC is used to record changes in files and folders that are tracked by DVC.


The primary purpose of the dvc commit command is to capture changes made to the tracked files and folders within a DVC project.


Running the dvc commit command is straightforward:

dvc commit

Recording Changes:

When changes are made to the data files or pipelines within the DVC project, the commit command is used to record these changes.

Committing Metadata:

The command ensures that any metadata changes, such as modifications to the .dvc files, are committed, allowing for reproducibility of the project.


The commit command plays a key role in versioning data and maintaining a record of changes made over time. This is crucial for collaboration and reproducibility.

Example Output:

Committing 'example.dvc'. [####################] 100% 0.00/0.00 [0.00s]


The commit command is a vital step in the DVC workflow, ensuring that changes to tracked files are officially recorded. It helps in maintaining a clear history of modifications and allows for easy reproduction of previous states of the project.

7. checkout

The checkout command in DVC is used to update tracked files in the workspace based on the information stored in the dvc.lock and .dvc files. This command is analogous to git checkout, but it is specifically designed for DVC-managed files.


The primary purpose of dvc checkout is to synchronize the contents of the working directory with a specific state captured in the DVC files.


To update the tracked files based on the information in dvc.lock and .dvc files, you can use:

dvc checkout

Effect on Workspace:

When you run dvc checkout, DVC retrieves the data associated with the specified state and updates the working directory, reverting it to the condition it was in when the specific DVC commit was made.

Example Scenario:

Imagine you have multiple versions of a dataset tracked by DVC, and you want to switch to a previous version:

dvc checkout -b <branch-name>

Usage with Branches:

The -b option allows you to specify a branch to which you want to switch. This is similar to how git checkout works for branches.

8. push

The push command in DVC is used to push data files from the local workspace to a remote storage location. This is similar to git push, but it is specifically designed for DVC-managed data files.


The primary purpose of dvc push is to share data changes with a remote storage location. This is essential for team collaboration and maintaining multiple copies of data to avoid potential data loss disasters.

Usage for Default Remote:

To push changes to the default remote storage:

dvc push

Usage for Specific Remote:

To push changes to a specific remote storage:

dvc push -r <remote-name>

Example Scenario:

Suppose you've made modifications to your local dataset, and you want to share those changes with your team by pushing them to a shared remote storage:

dvc push

Remote Names:

Remote names are set during the configuration of remote storage using dvc remote add. The -r option allows you to specify the remote name.

Effects on Remote Storage:

The push command uploads the necessary changes to the remote storage, ensuring that the remote version matches the local version.

9. pull

The pull command in DVC is used to update the local workspace with the latest changes from remote storage. It is similar to the git pull command and is an essential part of the DVC workflow for syncing data between local and remote repositories.


The primary purpose of the dvc pull command is to retrieve the latest changes from remote storage and update the local workspace accordingly. This ensures that the local version of data files aligns with the version stored remotely.

Usage for Default Remote:

To pull changes from the default remote storage:

dvc pull

Usage for Specific Remote:

To pull changes from a specific remote storage:

dvc pull -r <remote-name>

Remote Names:

Remote names are set during the configuration of remote storage using dvc remote add. The -r option allows you to specify the remote name from which you want to pull changes.

Example Scenario:

Suppose you are collaborating on a DVC project with a team, and someone else has pushed changes to the remote storage. You would want to update your local workspace to reflect those changes:

dvc pull

Workflow Similarity to Git:

The pull and push commands in DVC work similarly to their counterparts in Git. While push is used to send changes from the local workspace to remote storage, pull is used to retrieve changes from remote storage and update the local workspace.

Effect on Local Workspace:

The pull command downloads the necessary data files from the remote storage and updates the local workspace, making it consistent with the latest version stored remotely.

Use Case:

The pull command is crucial for team collaboration, where multiple team members may be working on the same DVC project. It ensures that everyone has access to the most up-to-date data, reducing the risk of inconsistencies.

10. run

The run command in DVC is a powerful tool for defining and executing pipeline stages. It is used to create and modify stages in the dvc.yml file, allowing users to assemble complex machine learning and data pipelines.


The primary purpose of the dvc run command is to define and execute pipeline stages. It allows users to specify the dependencies, outputs, and the actual command to be executed as part of the stage.


The basic syntax of the dvc run command is as follows:

  • bashCopy code


  • -n: Specifies the name of the stage.

  • -d: Specifies the dependencies of the stage.

  • -o: Specifies the outputs of the stage.


Let's consider an example where the DVC project has a script called, and a new stage named printer is defined using the dvc run command:

dvc run -n printer -d -o pages ./

In this example:

  • -n printer: Specifies the name of the stage as printer.

  • -d Specifies as a dependency of the stage.

  • -o pages: Specifies pages as the output of the stage.

  • ./ Specifies the actual command to be executed as part of the stage.


The dvc run command automatically generates or updates the dvc.yml file in the project, representing the pipeline. The dvc.yml file contains information about the pipeline stages, their dependencies, and outputs.

Workflow Integration:

The run command is often used to define and organize the workflow of a DVC project. It allows users to structure and automate the execution of commands, ensuring proper handling of dependencies and outputs.


The run command plays a crucial role in ensuring reproducibility. By explicitly defining dependencies and outputs, DVC can track changes in the data, code, or environment, making it easier to reproduce results.

Example Use Case:

Consider a scenario where the script generates a file named pages. The dvc run command ensures that whenever changes, or if pages do not exist, the command is executed to update pages.

Benefits of Using DVC in Data Science Projects:

1.Reproducibility and Versioning:

DVC, or Data Version Control, is pivotal in ensuring the reproducibility of data science projects through meticulous versioning.

  • Comprehensive Versioning: DVC goes beyond traditional version control systems by allowing the versioning of both code and datasets, models, and experiments.

  • Entire Project State: DVC captures the entire project state, including data and model versions, facilitating the recreation of results at any historical point.

  • Consistent Results: Emphasizes the importance of maintaining consistent results across different project stages, where every change is tracked for a reliable history.

2. Efficient Handling of Large Files and Datasets:

DVC efficiently manages challenges posed by large files and datasets, providing solutions that optimize storage usage.

  • Lightweight Metafiles: DVC uses lightweight metafiles instead of duplicating large files, significantly reducing storage requirements.

  • Optimizing Storage: Highlights the significance of optimizing storage when dealing with substantial datasets, with DVC's approach minimizing redundancy.

3. Collaboration and Remote Storage:

DVC serves as a collaborative platform, streamlining teamwork and offering seamless mechanisms for data sharing through remote storage.

  • Team Collaboration: Team Collaboration refers to the collaborative environment fostered by DVC, allowing multiple team members to efficiently work on the same data science project. DVC streamlines collaboration by providing a shared repository for data and models, ensuring that all team members can contribute to and access the project seamlessly.

  • Remote Storage Benefits: Remote Storage Benefits refer to the advantages gained by utilizing remote storage solutions within DVC. Remote storage enhances team-wide accessibility, providing a centralized and efficient mechanism for sharing and managing project data.

4. Integration with Git Workflows:

DVC seamlessly integrates with Git, enhancing version control capabilities and ensuring a smooth transition for users familiar with Git.

  • Smooth Integration: This refers to the seamless blending of DVC functionalities with Git, resulting in a unified and effective version control system. Git is a widely used version control system that is efficient in managing source code changes. However, when it comes to handling large files, datasets, and machine learning models, Git can encounter limitations. DVC steps in to address these challenges by integrating seamlessly with Git.

  • Leveraging Git Commands: How DVC makes use of Git's commands and workflows to provide a user-friendly experience. This involves integrating DVC seamlessly into the familiar Git environment, making it easy for users who are already accustomed to Git.

5. Streamlining Machine Learning Pipelines:

DVC provides essential tools, including the dvc run command, contributing to the efficient organization and tracking of machine learning pipelines.

  • dvc run Command: The dvc run command aids in defining and managing machine learning pipelines. This command allows users to specify dependencies (-d), outputs (-o), and the command to be executed (-n). It contributes to the efficient organization and tracking of machine learning pipelines, ensuring that each stage is well-defined and reproducible. The dvc run command is particularly useful in scenarios where machine learning workflows involve multiple steps, such as data preprocessing, model training, and evaluation. By encapsulating these steps within a single dvc run command, users can ensure that changes to the pipeline are tracked and versioned.

  • Pipeline Organization: DVC facilitates pipeline organization by providing tools like the dvc run command. By utilizing DVC to define and track each stage of the pipeline, users can achieve a well-organized structure. This not only enhances project management but also contributes to reproducibility.

Real-world Use Cases with DVC Commands:

In real-world data science projects, DVC commands play a crucial role in ensuring efficient version control and reproducibility. Let's explore practical examples showcasing the application of DVC commands in diverse scenarios.

Example 1: Managing Large Datasets

  • Scenario: You are working on a project that involves large datasets, making versioning a challenge.

  • DVC Command: dvc add

  • Application: Use dvc add to efficiently manage large datasets by creating lightweight metafiles. This ensures that only references to the actual data are tracked, minimizing storage requirements.

Example 2: Collaborative Model Development

  • Scenario: Your team is collaborating on a machine learning model, and you need a shared repository for efficient collaboration.

  • DVC Command: dvc commit

  • Application: Employ dvc commit to capture changes in both code and model files, ensuring that the entire team has a consistent project state. This facilitates collaboration by tracking modifications and updates.

Example 3: Sharing Results with Remote Teams

  • Scenario: Your data science project involves multiple teams working remotely, and you need a centralized storage solution.

  • DVC Command: dvc push

  • Application: Utilize dvc push to share your project's data, models, and code with remote teams through centralized remote storage. This ensures accessibility and collaboration, even across distributed teams.

How DVC Addresses Common Challenges in Version Control:

Data science projects often face challenges related to version control that can impede collaboration and reproducibility. Let's explore how DVC provides solutions to these challenges.

Challenge 1: Tracking Large Files Efficiently

DVC addresses the challenge of tracking large files by using metafiles instead of duplicating them. This reduces redundancy and minimizes storage requirements, ensuring efficient versioning without overwhelming storage capacity.

Challenge 2: Managing Dependencies in ML Pipelines

DVC excels in managing dependencies by providing tools like the dvc run command. This allows data scientists to define and track machine learning pipelines efficiently, ensuring that dependencies are captured and reproducible.

Challenge 3: Ensuring Reproducibility Across Environments

Reproducibility is a common challenge, especially when moving projects across different environments. DVC addresses this by capturing the entire project state, including code, data, and model versions. This ensures that results can be recreated consistently, regardless of the environment.

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