HPCFAIR: Enabling FAIR AI for HPC Applications

Verma, Gaurav; Emani, Murali; Liao, Chunhua; Lin, Pei-Hung; Vanderbruggen, Tristan; Shen, Xipeng; Chapman, Barbara

doi:10.1109/mlhpc54614.2021.00011

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…The HPC ED federated catalog builds on a foundation of standardized minimal HPC training metadata for publishing and discovering training information and will merge and standardize the HPC learning metadata from among our partnering organizations into a common metadata schema. [22] [15] [19] [20] [21] [9] We propose that our effort begin with two types of metadata for elements in the federated repository: first, metadata that describes the training material, its access methods, and educational characteristics, including Title, Description, Authors, Publisher, Type, Language, Cost, Format, License, Target Group, Expertise Level, Certification details, and very importantly, Persistent Identifiers, Tags, or Keywords; and second, metadata that identifies the publisher and source of the training material so that when an individual selects a specific training item, they can be directed to the source catalog that published that material to browse all available information and to access that training material. Additionally, we will start with the Research Data Alliance (RDA) "Recommendations for a minimal metadata set to aid harmonized discovery of learning resources" that addresses many of the use cases and needs around basic training sharing and discovery and supports FAIR practices" [12].…”

Section: Metadata Taxonomy and Ontologiesmentioning

confidence: 99%

Scaling HPC Education

Mehringer,

Thomas,

Cahill

et al. 2024

JOCSE

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Throughout the cyberinfrastructure community there are a large range of resources available to train faculty and young scholars about successful utilization of computational resources for research. The challenge that the community faces is that training materials abound, but they can be difficult to find, and often have little information about the quality or relevance of offerings. Building on existing software technology, we propose to build a way for the community to better share and find training and education materials through a federated training repository. In this scenario, organizations and authors retain physical and legal ownership of their materials by sharing only catalog information, organizations can refine local portals to use the best and most appropriate materials from both local and remote sources, and learners can take advantage of materials that are reviewed and described more clearly. In this paper, we introduce the HPC ED pilot project, a federated training repository that is designed to allow resource providers, campus portals, schools, and other institutions to both incorporate training from multiple sources into their own familiar interfaces and to publish their local training materials.

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Section: Metadata Taxonomy and Ontologiesmentioning

confidence: 99%

Scaling HPC Education

Mehringer,

Thomas,

Cahill

et al. 2024

JOCSE

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“…Funded by DOE. This multi-institutional project aims to develop a generic High Performance Computing data management framework 9,10 to make both training data and AI models of scientific applications FAIR. • The FAIR Surrogate Benchmarks Initiative 11 .…”

Section: Fair Initiativesmentioning

confidence: 99%

FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective

Huerta¹,

Blaiszik²,

Brinson³

et al. 2022

Preprint

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A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been reinterpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.

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“…To maximize the impact and utility of such AI models, it's recommended to adopt FAIR principles, ensuring they are findable, accessible, interoperable, and reusable. These principles, initially crafted for scientific datasets [5], have been adapted for research software [6][7][8][9] and other areas, including AI tool development [10,11]. However, applying FAIR principles to AI models presents challenges due to the unique nature of AI models.…”

Section: Introductionmentioning

confidence: 99%

FAIR AI Models in High Energy Physics

Li,

Duarte,

Roy

et al. 2024

EPJ Web of Conf.

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The findable, accessible, interoperable, and reusable (FAIR) data principles serve as a framework for examining, evaluating, and improving data sharing to advance scientific endeavors. There is an emerging trend to adapt these principles for machine learning models—algorithms that learn from data without specific coding—and, more generally, AI models, due to AI’s swiftly growing impact on scientific and engineering sectors. In this paper, we propose a practical definition of the FAIR principles for AI models and provide a template program for their adoption. We exemplify this strategy with an implementation from high-energy physics, where a graph neural network is employed to detect Higgs bosons decaying into two bottom quarks.

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HPCFAIR: Enabling FAIR AI for HPC Applications

Cited by 10 publications

References 10 publications

Scaling HPC Education

Scaling HPC Education

FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective

FAIR AI Models in High Energy Physics

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