Getting Started

Installation for Single Server/Node Deployment

If you plan to use CREDIT only for running pretrained models or training on a single server/node, then the standard Python install process will install both CREDIT and all necessary dependencies, including the right versions of PyTorch and CUDA, for you. If you are running CREDIT on the Casper system, then the following instructions should work for you.

Create a minimal conda or virtual environment.

conda create -n credit python=3.11
conda activate credit

If you want to install the latest stable release from PyPI:

pip install miles-credit

If you want to install the main development branch

git clone git@github.com:NCAR/miles-credit.git
cd miles-credit
pip install -e .

Important

macOS users will need to ensure that the required compilers are present and properly configured before installing mile-credit for versions requiring pySTEPS (miles-credit > 2025.2.0). See this note in the pySTEPS documentation for details.

Installation on Derecho

If you want to build a conda environment and install a Derecho-compatible version of PyTorch, run the create_derecho_env.sh script.

git clone git@github.com:NCAR/miles-credit.git
cd miles-credit
./create_derecho_env.sh

Important

The credit conda environment requires multiple gigabytes of space. Use the gladequota command to verify that you have sufficient space in your home or work directories before installing. You can specify where to install your conda environments in a .condarc file with the section envs_dirs.

Installation from source

See Installing CREDIT from source for detailed instructions on building CREDIT and its dependencies from source or for building CREDIT on the Derecho supercomputer.

Running a pretrained model

See Prediction Rollouts for more details on how to run one of the pretrained CREDIT models.