HPCFAIR: Enabling FAIR AI for HPC Applications

Artificial Intelligence (AI) is being adopted in different domains at an unprecedented scale. A significant interest in the scientific community also involves leveraging machine learning (ML) to effectively run high performance computing applications at scale. Given multiple efforts in this arena, there are often duplicated efforts when existing rich data sets and ML models could be leveraged instead. The primary challenge is a lack of an ecosystem to reuse and reproduce the models and datasets. In this work, we propose HPCFAIR, a modular, extensible framework to enable AI models to be Findable, Accessible, Interoperable and Reproducible (FAIR). It enables users with a structured approach to search, load, save and reuse the models in their codes. We present the design, implementation of our framework and highlight how it can be seamlessly integrated to ML-driven applications for high performance computing applications and scientific machine learning workloads.