DVC Extension for Visual Studio Code

Quick start • What you get • Commands • Configuration • Debugging • Contributing • Telemetry

Run, compare, visualize, and track machine learning experiments right in VS Code. This extension uses DVC, an open-source data versioning and ML experiment management tool. No additional services or databases are required.

🔔 Stay up-to-date with the latest product updates and tutorials by subscribing to the DVC YouTube channel!

• Experiment tracking: Record training data, parameters, and metrics on top of Git. Navigate your experiments, compare their results, and find the best ML models.
• Visualization: Plot performance data in a customizable dashboard including one or more overlaid experiments.
• Live tracking: Capture and see metrics changing in real time.
• Reproducibility: Make sure that anyone can recover or confirm previous experiments, and run new experiments based on their results.
• Data Management: Handle and version large datasets, files, and models effectively right from VS Code.

Note: We always welcome feedback! Feel free to reach out via Discord or open issues in GitHub.

Why prefer this extension?

• Enjoy the best developer experience with the first experiment tracking interface for an IDE.
• No external servers, databases, subscriptions, etc. Data stays fully under your control and your existing Git hosting is used to share and collaborate.
• Go beyond a simple metrics dashboard with complete ML experiments that include metrics, code, and data. Powered by [DVC experiment versioning][dvc experiments].
• Implement data versioning on top of your favorite cloud storage, such as Amazon S3, Azure Blob Storage, Google Cloud Storage, NFS, etc.

[dvc experiments]: https://dvc.org/doc/user-guide/experiment-management/experiments-overview

Quick start

1. Install [this extension] in VS Code.
2. Follow the steps set out on the Setup page!

💡 Feel free to try our [example DVC project] first! Opening it [with Github Codespaces] will include this extension automatically.

[this extension]: https://marketplace.visualstudio.com/items?itemName=Iterative.dvc [example dvc project]: https://github.com/iterative/example-get-started-experiments [with github codespaces]: https://docs.github.com/en/codespaces/getting-started/quickstart#creating-your-codespace

Learn more about DVC (Data Version Control)

See the DVC documentation to Get Started with Experiments or [Data Management]. For deeper learning, try our free course! [More resources].

[more resources]: https://github.com/iterative/vscode-dvc/blob/main/extension/resources/walkthrough/more-resources.md

What you get

This extension augments VS Code in the following ways:

• Adds a brand-new new [DVC View] (<img src="https://raw.githubusercontent.com/iterative/vscode-dvc/main/extension/docs/dvc.png" alt="DVC logo" height="16"/> icon in the Activity Bar) with panels to visualize and manage [DVC Experiments].
• Provides special Editors to manage Experiments and display [Plots] in IDE Tabs.
• Adds a [DVC Tracked] panel to the Explorer view. This shows a tree of the data files and directories tracked by DVC, including their state, and options to synchronize them (from/to remote storage).
• Adds a [DVC panel] to the Source Control view to display the [workspace status]. You can restore or reset project versions (based on the current Git HEAD commit) as well as manage new and existing data from here.
• Registers several Commands in the [Command Palette] (see next section).
• Includes a DVC channel for the Output panel (useful for debugging).

[dvc view]: https://github.com/iterative/vscode-dvc/blob/main/extension/resources/walkthrough/view-container.md experiments: https://github.com/iterative/vscode-dvc/blob/main/extension/resources/walkthrough/experiments-table.md [plots]: https://github.com/iterative/vscode-dvc/blob/main/extension/resources/walkthrough/plots.md [command palette]: https://code.visualstudio.com/docs/getstarted/userinterface#_command-palette [dvc tracked]: https://github.com/iterative/vscode-dvc/blob/main/extension/resources/walkthrough/dvc-tracked-files.md

[dvc panel]: https://github.com/iterative/vscode-dvc/blob/main/extension/resources/walkthrough/dvc-tracked-files.md [workspace status]: https://dvc.org/doc/command-reference/status#local-workspace-status

Useful commands

Open the Command Palette (F1 or ⇧⌃P on Windows/Linux or ⇧⌘P on macOS) and type in one of the following commands:

Learn more about the extension's [commands].

[commands]: https://github.com/iterative/vscode-dvc/blob/main/extension/resources/walkthrough/command-palette.md

Configuration

These are the VS Code settings available for the Extension:

Note that the Setup The Workspace command helps you set up the basic ones at the [Workspace level] (saved to .vscode/setting.json).

Python

This extension is integrated with Microsoft's [Python extension]. When possible, the Python extension's selected interpreter will be used to locate DVC. The PYTHONPATH environment variable identified via the [python.envFile] config setting is also respected.

DVC Studio

Studio is a collaboration platform for Machine Learning teams. The extension will help you to connect to Studio by providing guidance and managing the required [DVC config options]. See the DVC Studio section of the Setup page for more information.

[python extension]: https://marketplace.visualstudio.com/items?itemName=ms-python.python [studio.token]: https://dvc.org/doc/user-guide/project-structure/configuration#studio

[workspace level]: https://code.visualstudio.com/docs/getstarted/settings#_workspace-settings [python.envFile]: https://code.visualstudio.com/docs/python/environments#_use-of-the-pythonpath-variable [dvc config options]: https://dvc.org/doc/user-guide/project-structure/configuration#studio

Debugging

The DVC Extension

Please see the DVC [channel] in the IDE's Output panel to see the underlying DVC commands being run, as well as their error output. Feel free to share this with us via Discord or use it to report issues in GitHub.

[channel]: https://code.visualstudio.com/api/extension-capabilities/common-capabilities#output-channel

Your DVC project

Due to the way DVC pipelines run scripts of any language from the command line, users must debug pipeline scripts (e.g. train.py) standalone in whatever way debuggers are run on the base language - this is standard for debugging DVC pipelines, and most scripts are capable of running outside of DVC.

Contributing

See the development and contributing guidelines in CONTRIBUTING.md.

Data and telemetry

The DVC Extension for Visual Studio Code collects usage data and sends it to Azure to help improve our products and services. This extension respects the telemetry.enableTelemetry setting which you can learn more about at https://code.visualstudio.com/docs/supporting/faq#_how-to-disable-telemetry-reporting.