A Visual Studio Code extension that aims to allow users of all technical backgrounds to effectively use DVC.
We want early adopter feedback! Please let us know what you like and don't like about the extension. Feel free to reach out in this repository's issues or via any of the other existing support channels.
- Step 1. Install a supported version of DVC on your system
- Step 2. Install the DVC extension for Visual Studio Code.
- Step 3. See Walkthrough.
Open the Command Palette (F1
or ⇧⌃P on Windows/Linux or ⇧⌘P on macOS) and type
in one of the following commands:
Command | Description |
---|---|
DVC: Get Started |
Open the extension's walkthrough. Which details all of the current features and provides links to extra learning resources. |
View: Show DVC |
Open the extension's view container. |
DVC: Setup The Workspace |
Activate the extension's workspace setup wizard. |
DVC: Show Experiments |
Show an interactive version of DVC's exp show command. |
DVC: Show Plots |
Show an interactive version of DVC's plots diff command. |
- Command Palette
- Source Control Management
- Tracked Resources
- DVC View Container
- Experiments Table
- Plots
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.
See development docs and contributing guidelines in CONTRIBUTING.md
View more resources.
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.