ML Project Initialization

Initialize a new machine learning research project using the ML Research Copier template with PyTorch Lightning, Hydra configuration, and modern Python tooling (uv).

Process

1. Explain the Template System

This command uses uvx copier to create projects from templates. The template supports:

• PyTorch + CUDA: Multiple version presets (2.4-2.9, CUDA 11.8-13.0)
• PyTorch Lightning: For training infrastructure
• Hydra: For configuration management
• Experiment Tracking: TensorBoard, W&B, MLflow
• Modern Tooling: uv (package manager), ruff (linter), ty (type checker)
• Multiple Templates: Image classification, segmentation, GNN, etc.

2. Check Prerequisites

Verify that uvx is available:

uvx --version

If not available, install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

3. Run Copier Template

Execute the copier command pointing to the ML research template:

# From GitHub (recommended)
uvx copier copy --trust gh:nishide-dev/ml-research-template <project-directory>

# From local clone (for development)
uvx copier copy --trust /path/to/ml-research-template <project-directory>

Where:

• gh:nishide-dev/ml-research-template is the GitHub repository URL
• <project-directory> is where the new project will be created

Note: The template is maintained in a separate repository (ml-research-template) for independent versioning and broader reusability.

4. Interactive Configuration

Copier will ask the user to configure:

Project Basics:

• Project name (defaults to directory name)
• Package name (Python import name, auto-generated from project name)
• Description
• Author name and email
• Python version (3.10-3.13)

Development Tools:

• Use ruff? (default: yes)
• Use ty? (default: yes)
• Use pytest? (default: yes)
• Use GitHub Actions? (default: yes)
• Use Nix + direnv? (default: no)

PyTorch/CUDA:

• PyTorch + CUDA preset (interactive dropdown with compatible combinations)
  • PyTorch 2.8.0 + CUDA 12.6 (recommended)
  • PyTorch 2.9.0 + CUDA 12.6 (latest)
  • ... (many presets)
  • Custom (manual version entry)
• Include torchvision? (default: yes)
• Include torchaudio? (default: no)

ML Frameworks:

• Use PyTorch Lightning? (default: yes)
• Lightning version (if enabled)
• Use Hydra? (default: yes)
• Hydra version (if enabled)
• Use PyTorch Geometric? (default: no)

Experiment Tracking:

• Logger choice: TensorBoard / W&B / MLflow / Both / None
• W&B entity (if W&B selected)

Template Type:

• Image Classification (default)
• Segmentation
• Object Detection
• Text Classification
• GNN (Graph Neural Network)
• Minimal (custom template)

Dataset:

• MNIST / CIFAR-10 / CIFAR-100 / Fashion-MNIST / Custom (for image classification)

5. Post-Generation

Copier automatically executes post-generation tasks:

1. git init -b main - Initialize git repository
2. uv venv - Create virtual environment
3. uv lock - Lock dependencies
4. uv sync - Install dependencies
5. ruff check . --fix - Lint and auto-fix
6. ruff format . - Format code
7. ty check - Type check (if enabled)
8. pytest - Run tests (if enabled)

6. Project Structure

The generated project will have:

<project-name>/
├── src/
│   └── <package-name>/
│       ├── __init__.py
│       ├── train.py          # Main training script with Hydra
│       ├── models/           # LightningModule definitions
│       ├── data/             # DataModule and datasets
│       └── utils/            # Utility functions
├── tests/
│   └── test_<package_name>.py
├── configs/                  # Hydra configuration
│   ├── config.yaml
│   ├── model/
│   ├── data/
│   ├── trainer/
│   ├── logger/
│   └── experiment/
├── pyproject.toml           # uv + project config
├── ruff.toml                # Ruff linting config
├── .gitignore
├── README.md
└── .venv/                   # Virtual environment (created)

7. Verify Installation

After project creation, verify the setup:

cd <project-name>

# Verify CUDA availability
uv run python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}')"

# Run a quick test
uv run pytest tests/ -v

# Check code quality
uv run ruff check .

8. Next Steps

Guide the user:

# Start training (with Hydra defaults)
uv run python src/<package-name>/train.py

# Override configuration
uv run python src/<package-name>/train.py trainer.max_epochs=20 data.batch_size=64

# Run specific experiment
uv run python src/<package-name>/train.py experiment=baseline

# Start TensorBoard (if using TensorBoard)
tensorboard --logdir logs/

# Login to W&B (if using W&B)
wandb login

Alternative: Manual Template Selection

If the user wants a specific template variant without interactive prompts, they can use:

# Use defaults with --defaults flag (from GitHub)
uvx copier copy --trust --defaults gh:nishide-dev/ml-research-template <project-directory>

# Use data flags for non-interactive (from GitHub)
uvx copier copy --trust \
  --data project_name="my-project" \
  --data pytorch_cuda_preset="pytorch-2.8.0-cuda-12.6" \
  --data use_lightning=true \
  --data logger_choice="wandb" \
  gh:nishide-dev/ml-research-template <project-directory>

# From local clone
uvx copier copy --trust --defaults /path/to/ml-research-template <project-directory>

Troubleshooting

copier not found

# Install uv first
curl -LsSf https://astral.sh/uv/install.sh | sh

# Then uvx will be available

CUDA version mismatch

Check available CUDA on system:

nvidia-smi

Select matching preset or custom CUDA version during copier prompts.

Import errors after generation

Ensure virtual environment is activated or use uv run:

uv run python src/<package-name>/train.py

Success Criteria

• Copier template executes without errors
• All dependencies installed successfully
• CUDA detection works (if GPU available)
• Tests pass
• Ruff checks pass
• README generated with correct instructions
• Git repository initialized

Project is ready for ML research!

Example Usage

# From GitHub (recommended)
uvx copier copy --trust gh:nishide-dev/ml-research-template ~/projects/my-cifar10-project

# From local clone (for development)
uvx copier copy --trust /path/to/ml-research-template ~/projects/my-cifar10-project

# Follow interactive prompts, then:
cd ~/projects/my-cifar10-project
uv run python src/my_cifar10_project/train.py