@inproceedings{fb493516c5d74bef8d1e468999006b11,
title = "HPCFAIR: Enabling FAIR AI for HPC Applications",
abstract = "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.",
keywords = "AI models, datasets, FAIR, HPC, neural networks",
author = "Gaurav Verma and Murali Emani and Chunhua Liao and Lin, \{Pei Hung\} and Tristan Vanderbruggen and Xipeng Shen and Barbara Chapman",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 7th IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments, MLHPC 2021 ; Conference date: 15-11-2021",
year = "2021",
doi = "10.1109/MLHPC54614.2021.00011",
language = "English",
series = "Proceedings of MLHPC 2021: Workshop on Machine Learning in High Performance Computing Environments, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "58--68",
booktitle = "Proceedings of MLHPC 2021",
}