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Setup Environment

This project uses uv for dependency management and provides a streamlined setup process for deep learning development with PyTorch Lightning.

Installation

The project includes a convenient Makefile for setup:

make install

This command will: - Create a virtual environment using uv - Install all dependencies from pyproject.toml - Set up pre-commit hooks for code quality

Verification

To verify your installation is working correctly:

# Run code quality checks
make check

# Run tests
make test

# Check if PyTorch can detect your GPU (if available)
uv run python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"

# Try your first train with
uv run python -m {{cookiecutter.project_slug}}.scripts.train

Since it create the virtual environment using uv, please use uv run for all your python script like uv run python or just source ./.venv/bin/activate to enter the environment first and then run python command.

Troubleshooting

Common Issues

uv not found: Install uv using curl -LsSf https://astral.sh/uv/install.sh | sh or visit uv installation guide

CUDA version mismatch: This should be handled by UV properly. But If you want to specific version, please check using uv with PyTorch

Pre-commit hooks failing: Run uv run pre-commit install and uv run pre-commit run --all-files to set up and test hooks.

For additional help, see the project's GitHub repository issues section.