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How to turn a training config into a source-linked diagram with ML View

Ask your assistant to map data, model, losses and validation as a clickable diagram.

Who it is forAn ML engineer who inherits a training repo and needs to see how the config, data and loss fit together before changing anything.

The problemReading training code and YAML configs across many files makes the flow of data, parameters and losses hard to hold in your head.

The outcomeA *.mlview.json diagram opens in VS Code, and each step links to the cited source lines.

What you need first

  • Python 3.10+
  • Node 20.18.1+
  • VS Code
  • A supported assistant: GitHub Copilot, Codex or Claude Code
  • A target ML project to open in VS Code

Steps

  1. Run cd webview && npm ci && npm run build && cd .. from the ML View checkout.
  2. Run python3 tools/sync-assets.py.
  3. Run cd vscode-extension && npm ci && npm run package && cd .., then install the generated VSIX with VS Code's Extensions: Install from VSIX.
  4. Run python3 tools/install_skill.py /path/to/your/project.
  5. In the target project, invoke mlview in your assistant's skill picker and ask it to explain training with the config, showing data, model, losses, parameter updates, validation and anything unresolved.

Prompt for your agent

Using the MLView repo at <mlview-checkout>, install the mlview skill into <target-project> and ask the assistant to explain the training config in <target-project>. Expect a *.mlview.json file that opens as a diagram. Verify that the diagram lists unresolved items and that its cited files are reported as unchanged.

Caveat. ML View is experimental, and the workflow interpretation comes from the active assistant model; citation checks do not prove the interpretation correct.

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