Installation

Requirements

• Python 3.8 or higher

• PyTorch 2.0+ (provided by cineca-ai module)

• CUDA-capable GPU (optional, for GPU acceleration)

Installation using module load

1. Load required modules:

   This step might need to be ignored in the case that conflict of the environment modules arises. In that case, you can install the dependencies via pip (see next section).

   module load pytorch # This loads PyTorch with CUDA and other dependencies
   

2. Clone the repository:

   git clone https://github.com/kardaneh/rtnn.git
   cd rtnn
   

3. Create virtual environment:

   uv venv --python 3.8
   source .venv/bin/activate
   

4. Install missing dependencies:

   uv pip install xarray mpltex
   

5. Install the package:

   uv pip install -e .
   

6. Verify installation:

   python -c "import rtnn; print(rtnn.__version__)"
   rtnn --version
   

Installation from zero (recommended)

If you have issue on module load and your virtual environment, you can install all dependencies via pip (remember to uncomment the dependencies in pyproject.toml). follow the steps 2-3 from the previous section, then install all dependencies and the package itself:

uv pip install -e .
# This will install all dependencies including PyTorch

Development Installation

For development, install with dev dependencies:

uv pip install -e ".[dev]"
pre-commit install

Building Documentation

To build the HTML documentation locally:

1. Install documentation dependencies:

   uv pip install -e ".[docs]"
   

   This installs Sphinx, the Read the Docs theme, and other required extensions.

2. Navigate to the docs directory:

   cd docs
   

3. Build the HTML documentation:

   make clean
   make html
   

   The HTML files will be generated in docs/build/html/.

4. View the documentation:

   # Open with your browser
   firefox build/html/index.html
   
   # Or serve with Python
   python -m http.server --directory build/html 8000