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ml-project-init

Initialize ML research repositories with PyTorch Lightning and Hydra via Copier.

Updated Feb 6, 2026
Search Tags:#uv#experiment tracking#pytorch lightning#hydra configuration#ml project scaffolding#copier template#gpu cuda presets
One-click install
npx skills add https://github.com/nishide-dev/claude-code-ml-research --skill ml-project-init
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ml-project-init
Source: https://github.com/nishide-dev/claude-code-ml-research/tree/main/skills/ml-project-init
Command: npx skills add https://github.com/nishide-dev/claude-code-ml-research --skill ml-project-init

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Starting an ML research project from scratch takes too long because you must repeatedly set up the same training stack, configuration system, tooling, and project structure.

Core Features & Use Cases

  • Scaffold a full ML research template: Generates a ready-to-train codebase using a Copier template with PyTorch Lightning, Hydra, and modern Python tooling (uv, ruff, type checking, optional tests).
  • Interactive configuration for realistic experiments: Lets you choose CUDA/preset combinations, Lightning/Hydra integration, experiment tracking backends (TensorBoard/W&B/MLflow), and template type (image classification, segmentation, object detection, text classification, GNN, minimal).
  • Post-generation quality and readiness checks: Automatically initializes git, creates/locks dependencies, formats/lints, runs type checks (optional), and runs tests (optional) so the project is immediately usable.

Quick Start

Use ml-project-init to create a new ML research project in a fresh folder using the Copier template with PyTorch Lightning, Hydra, and uv.

Frequently Asked Questions about ml-project-init

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I scaffold a PyTorch Lightning project with Hydra configuration?▼

You can scaffold a PyTorch Lightning project with Hydra configuration by using a Copier template that generates a ready-to-train codebase. The template standardizes the project structure so you can start training immediately.

What ML research templates are available for image classification and segmentation?▼

Available ML research templates include image classification, segmentation, object detection, text classification, GNN, and a minimal option. Each template integrates PyTorch Lightning and Hydra for immediate training readiness.

Can I set up experiment tracking with TensorBoard or W&B when bootstrapping an ML repo?▼

Yes, you can set up experiment tracking with TensorBoard, W&B, or MLflow when bootstrapping an ML repo. The Copier template interactively configures your preferred tracking backend during project generation.

How do I configure CUDA presets for a new PyTorch Lightning research repository?▼

You configure CUDA presets for a new PyTorch Lightning research repository through interactive prompts during Copier template generation. This lets you select the correct CUDA and preset combinations for your specific GPU environment.

What's the best way to initialize an ML project with dependency locking and linting?▼

The best way to initialize an ML project with dependency locking and linting is using a Copier template with modern Python tooling. Post-generation steps automatically lock dependencies, format, and lint the codebase so it is immediately usable.

Do I need uvx copier to bootstrap a machine learning research repository?▼

Yes, you need uvx copier to bootstrap a machine learning research repository using this template. It requires running uvx copier copy with a trusted template source to generate the standardized project files.

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