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Cross-Platform ML.NET Model Builder Extension for Visual Studio Code

Aperta
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Valutazione

Difficoltà
5/5
Tempo stimato
Più di una settimana
Idoneità per principianti
25/100
Tipo di issue
Funzionalità
Chiarezza
Da chiarire
Stato di attività
Attiva
Stack tecnologico
vscode
Ambito
developer-experience, machine-learning, tooling

Direzione di ricerca

Review the linked MLBuilderVSCode-Source prototype and the repository entry points first, then compare them with the proposed dotnet ml integration and VS Code WebView workflow. The roadmap defines done as a cross-platform extension covering dataset selection, training and evaluation, and model export, but the implementation starting point and scope still need agreement.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

Copy-pasting an issue from @xsa-dev https://github.com/dotnet/machinelearning/issues/7545

🚀 Feature Proposal: Cross-Platform ML.NET Model Builder Extension for Visual Studio Code

Is your feature request related to a problem? Please describe.

Currently, ML.NET Model Builder exists only as a Visual Studio for Windows component.
Developers who use VS Code, macOS, Linux, or GitHub Codespaces have no accessible, GUI-based way to build or train ML.NET models.
This leaves a large part of the .NET developer ecosystem — especially those on non-Windows environments — without first-class tooling for ML.NET.

Describe the solution you'd like

Develop or officially support a VS Code extension (“ML.NET Model Builder for VS Code”) that provides a modern, lightweight, and cross-platform workflow powered by the ML.NET CLI.

🔧 Core Capabilities
  • Integration with dotnet ml CLI (ML.NET 3.x+).
  • Scenario wizard: classification, regression, recommendation, anomaly detection.
  • Dataset selection (CSV, TSV, Parquet).
  • Label & feature selection, ignore columns, training time configuration.
  • Live training progress and evaluation metrics in VS Code WebView.
  • Export models to ONNX or TorchSharp formats.
  • Optional GPU training (CUDA / OneAPI).
  • Integration with .NET Interactive notebooks for visualization.
🧠 Developer Experience
  • Works on Windows, macOS, Linux, and Codespaces.
  • Uses modern VS Code APIs (WebView2, ES modules).
  • Built with esbuild or vite for fast packaging.
  • Published to Visual Studio Marketplace and OpenVSX.
  • Potential starting point: franzsilva/MLBuilderVSCode-Source (early prototype).
Describe alternatives you've considered
  • Using ML.NET CLI directly (functional but non-visual).
  • Running Visual Studio Model Builder in Windows VMs or containers (heavy and impractical).
  • Using Jupyter notebooks with ML.NET (not native to .NET developers’ workflow).
Additional context

A cross-platform Model Builder aligns with the .NET Everywhere strategy and lowers the entry barrier for ML.NET adoption.
It would make ML.NET approachable for students, researchers, and engineers working in containerized or non-Windows environments.
It could also serve as a bridge to AutoML.NET and ONNX Runtime for inference workloads.

Example roadmap:

  1. Migrate legacy codebase to modern VS Code API.
  2. Integrate ML.NET CLI 3.x+.
  3. Build a visual training wizard (dataset → train → evaluate → export).
  4. Add ONNX / TorchSharp export & inference playground.
  5. Publish and maintain cross-platform builds.
Lingua principale
Dockerfile
Stelle
286
Fork
66
Nessuna PR unita negli ultimi 30g

Preparare l'ambiente

Questo progetto non fornisce container di sviluppo, Dockerfile né guida per i contributori, quindi l'ambiente è a tuo carico: parti dal suo README e consulta la nostra guida al primo contributo per i passaggi generali.

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

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