Installation

cnbs-predictor is distributed as a conda environment plus a set of Jupyter notebooks. These steps mirror the project README.

Prerequisites

  • Conda (Anaconda or Miniconda)

  • Python 3.11+

On macOS, work from a Terminal window. On Windows, use the Anaconda Prompt. If you don’t already have Anaconda or Miniconda, install it from https://www.anaconda.com/download.

Steps

  1. Clone the repository:

    git clone https://github.com/great-lakes-ai-lab/cnbs-predictor.git
cd cnbs-predictor

  2. Create and activate the conda environment:

    conda env create -f requirements/environment.yml
conda activate nbs_env

  3. Register the environment as a Jupyter kernel:

    python -m ipykernel install --user --name nbs_env --display-name "Python (nbs_env)"

Note

requirements/environment.yml is the full environment, including the TensorFlow stack used for training new models. A lighter requirements/environment-test.yml (no TensorFlow) is used by the automated test suite — see Usage and the project CONTRIBUTING.md.

Building these docs locally

The documentation builds with a small, pip-only set of dependencies (the heavy compiled libraries are mocked, so they are not required just to build docs):

pip install -r docs/sphinx/requirements.txt
cd docs/sphinx
make html

The rendered site lands in docs/sphinx/_build/html/index.html.