How to use the command 'dvc diff' (with examples)

Data Version Control (DVC) is a tool that helps manage machine learning projects consisting of code and data. The dvc diff command is a versatile utility that allows users to efficiently track changes in DVC-managed data files across different versions of a project. This capability is invaluable when it comes to understanding the evolution of data and its impact on model performance.

By using dvc diff, users can observe what has changed in their datasets over time or between two states of a project. This can include any modifications, additions, or deletions in datasets and directories tracked by DVC. This feature aids in maintaining reproducibility and transparency in experiments, making sure that changes in datasets and model inputs are well-documented and understood.

Use case 1: Compare DVC tracked files from different Git commits, tags, and branches with respect to the current workspace

Code:

dvc diff commit_hash/tag/branch

Motivation:
This use case is essential for analyzing what data has changed between a specific point in the project’s history and the current state of your workspace. It helps in verifying if recent changes to datasets or their parameters align with expectations or if further adjustments are needed.

Explanation:

• commit_hash/tag/branch: This argument specifies the Git reference point (commit hash, tag, or branch name) you wish to compare against your current workspace. This flexibility lets you select any state of the repository’s history to compare with your current changes.

Example Output:

Path         Status    Changes
data/train   modified  +5/-3
results.txt  deleted   -1

This output indicates that in the specified Git reference, the ‘data/train’ folder has been modified, with 5 lines added and 3 removed, while ‘results.txt’ has been deleted.

Use case 2: Compare the changes in DVC tracked files from one Git commit to another

Code:

dvc diff revision1 revision2

Motivation:
Comparing two distinct historical states of a project allows users to trace the evolution of data across specific development phases or feature branches. This is particularly useful in collaborative environments where different team members may be working on separate branches.

Explanation:

• revision1: The first Git commit hash or reference point from which to begin the comparison.
• revision2: The second Git commit hash or reference point to compare against revision1.

Example Output:

Path        Status    Changes
dataset.csv renamed   name_change.csv
model.pkl   modified  +2/-2

The output shows that ‘dataset.csv’ has been renamed to ’name_change.csv’, and there are changes in the ‘model.pkl’ file.

Use case 3: Compare DVC tracked files, along with their latest hash

Code:

dvc diff --show-hash commit

Motivation:
This use case is helpful for tracking data versions with precise identifiers by using hashes. Knowing the hash values can be crucial for ensuring the integrity of data files and quickly referencing specific datasets.

Explanation:

• --show-hash: This flag adds hash values of the files to the dvc diff output, giving you a snapshot of the file’s identity at a specific commit.
• commit: The Git commit hash or reference point to compare against your current workspace.

Example Output:

Path              Status    Hash                                    
data/raw_data.csv modified  9f871c37 -> 8b9aecf4
results/summary.txt new      None -> 2b17ab8c

Here, you can see the hash transition for ‘data/raw_data.csv’, indicating a modification, and the new addition of ‘results/summary.txt’ with its hash.

Use case 4: Compare DVC tracked files, displaying the output as JSON

Code:

dvc diff --show-json --show-hash commit

Motivation:
Outputting the differences in JSON format is particularly useful for programmatically parsing change logs. This format is suitable for automated pipelines or when integrating DVC with other data analysis tools or platforms.

Explanation:

• --show-json: This flag instructs dvc diff to format the output as a JSON object, which is ideal for detailed data processing.
• --show-hash: As above, this flag shows hash values for convenient tracking of file versions.
• commit: The Git commit hash or reference point to be compared.

Example Output:

{
  "added": [],
  "modified": [
    {
      "path": "data/train.csv",
      "hash": ["a3d2e4b", "bb81c65"]
    }
  ],
  "deleted": []
}

The JSON output lists the ‘modified’, ‘added’, and ‘deleted’ files, along with respective hash values, offering a succinct and machine-readable record of changes.

Use case 5: Compare DVC tracked files, displaying the output as Markdown

Code:

dvc diff --show-md --show-hash commit

Motivation:
Generating a Markdown report is beneficial for documentation purposes and for teams using project management tools that support Markdown format. It facilitates an easy-to-read representation that can be embedded into documentation or shared in collaborative platforms.

Explanation:

• --show-md: This flag switches the output format to Markdown, suitable for documentation and presentations.
• --show-hash: Displays file hash data along with differences to indicate precise changes.
• commit: Specifies the commit hash or point in history for comparison.

Example Output:

| Path              | Status  | Hash                           |
|-------------------|---------|--------------------------------|
| data/train.csv    | modified| a3d2e4b -> bb81c65             |
| report/results.md | added   | None -> dc1ac3d                |

This Markdown table elegantly shows which files have been modified or added, including their hashes, and is easily incorporated into reports or documentation.

Conclusion:

The dvc diff command is a powerful feature for anyone utilizing DVC in their data science workflows. It provides comprehensive insights into the evolution of data, making it a critical tool for maintaining project integrity, collaboration, and effective tracking of changes over time. By understanding the various ways to leverage dvc diff, users can ensure their machine learning pipelines are transparent, reproducible, and clearly documented, supporting more robust and reliable data-driven decision-making.