Getting Started

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

This script will create a Derecho-compatible conda environment with a user-specified name taken from the CREDIT_ENV_NAME environment variable (defaults to credit-derecho).

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.

If you are finding your home directory to be surprisingly full with minimal installation of files, check your ~/.cache directory and delete everything inside it. This directory is where python installers tend to download their files. You can shift the .cache directory to a location with more space by adding export XDG_CACHE_HOME="/glade/work/$USER/.cache" to your ~/.bashrc file.

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.

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