Cross-Platform ML.NET Model Builder Extension for Visual Studio Code
还没有人认领这个 Issue。
评估
- 难度
- 5/5
- 预计耗时
- 一周以上
- 新手友好度
- 25/100
- Issue 类型
- 功能
- 描述清晰度
- 需要澄清
- 活跃度
- 活跃
- 技术栈
- vscode
调研方向
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.
由索引模型根据 Issue 内容生成。
描述
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 mlCLI (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 Interactivenotebooks for visualization.
🧠 Developer Experience
- Works on Windows, macOS, Linux, and Codespaces.
- Uses modern VS Code APIs (WebView2, ES modules).
- Built with
esbuildorvitefor 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:
- Migrate legacy codebase to modern VS Code API.
- Integrate ML.NET CLI 3.x+.
- Build a visual training wizard (dataset → train → evaluate → export).
- Add ONNX / TorchSharp export & inference playground.
- Publish and maintain cross-platform builds.
- 主要语言
- Dockerfile
- 星标
- 288
- 派生
- 66
- PR 合并指标
- 30 天内没有已合并 PR
环境准备
这个项目没有提供开发容器、Dockerfile 或贡献指南，环境需要你自己搭建：先看它的 README，通用步骤见我们的新手贡献指南。
从这里开始
- 先读完整个 Issue，再读项目的贡献指南。
- 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
- Fork 仓库，在一个分支上完成修改。
- 提交 Pull Request，并在描述里引用这个 Issue 编号。
dotnet/machinelearning-modelbuilder 的其他 Issue
-
mylb.未关闭
难度 1/5 1 小时以内 新手友好度 20/100
-
难度 4/5 1-2 天 新手友好度 22/100
-
难度 3/5 1-2 天 新手友好度 42/100
dotnet/machinelearning-modelbuilder#3090 · 2 条评论 · 1 个 reaction ·
-
难度 3/5 1-2 天 新手友好度 35/100
-
难度 5/5 一周以上 新手友好度 10/100
查看 dotnet/machinelearning-modelbuilder 的全部 Issue
相似的 Issue
-
难度 2/5 1-3 小时 新手友好度 75/100
git-ai-project/git-ai#2433 ·
维护者通常 1 天内回复
-
bug raised-by:verifier
难度 2/5 1-3 小时 新手友好度 79/100
medici-finance/assay#4008 ·
维护者通常 1 天内回复
-
难度 2/5 1-3 小时 新手友好度 82/100
kucherenko/jscpd#1239 ·
维护者通常 1 天内回复
-
难度 2/5 1-3 小时 新手友好度 64/100
social-harness/moltzap#1412 ·
维护者通常 1 天内回复
-
难度 2/5 1-3 小时 新手友好度 78/100
gracecraft-software/lupin#280 ·
维护者通常 1 天内回复