How-To Guide: Using DVC Extension in Visual Studio Code for Data Management¶
Introduction¶
Getting Started
Enhance your project's data management in VS Code using the DVC extension. This guide walks you through key features to streamline your data version control workflows.
Prerequisites¶
- Visual Studio Code installed.
- DVC extension added to VS Code.
- A DVC-initialized project.
Using the DVC Extension in VS Code¶
Step 1: View DVC Tracked Files¶
- Open the DVC Sidebar:
- Click the DVC icon in the VS Code activity bar to access the DVC sidebar.
- This sidebar shows all DVC-tracked files and stages in your project.
Step 2: Execute DVC Commands¶
Efficient DVC Operations
Interact with DVC-tracked files directly in VS Code to execute common DVC commands.
- Right-click on a DVC-tracked file or stage.
- Select from commands like
dvc pull
,dvc push
, ordvc repro
. - Manage your data versions without switching from the VS Code interface.
Step 3: Monitor Pipeline Status¶
- Utilize Pipeline Visualization:
- The DVC extension provides a graphical view of your data pipelines.
- Monitor progress and status, and easily spot issues.
Step 4: Manage Experiments¶
- Handle Machine Learning Experiments:
- Use the extension to browse, compare, and manage your DVC experiments.
- This is particularly useful for evaluating different model iterations.
Conclusion¶
Enhance Your Workflow
The DVC extension in VS Code is a powerful tool for data version control. It simplifies tracking changes, monitoring pipelines, and managing experiments, leading to a more organized and efficient development process.