Development of Authenticated Clients and Applications for ICICLE CI Services -- Final Report for the REHS Program, June-August, 2022

• Main Authors: Samar, Sahil, Chen, Mia, Karpinski, Jack, Ray, Michael, Sarin, Archita, Garcia, Christian, Lange, Matthew, Stubbs, Joe, Thomas, Mary
• Format: Journal Article
• Language: English
• Published: 16-04-2023
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Cryptography and Security
• DOI: 10.48550/arxiv.2304.11086

The Artificial Intelligence (AI) institute for Intelligent
Cyberinfrastructure with Computational Learning in the Environment (ICICLE) is
funded by the NSF to build the next generation of Cyberinfrastructure to render
AI more accessible to everyone and drive its further democratization in the
larger society. We describe our efforts to develop Jupyter Notebooks and Python
command line clients that would access these ICICLE resources and services
using ICICLE authentication mechanisms. To connect our clients, we used Tapis,
which is a framework that supports computational research to enable scientists
to access, utilize, and manage multi-institution resources and services. We
used Neo4j to organize data into a knowledge graph (KG). We then hosted the KG
on a Tapis Pod, which offers persistent data storage with a template made
specifically for Neo4j KGs. In order to demonstrate the capabilities of our
software, we developed several clients: Jupyter notebooks authentication,
Neural Networks (NN) notebook, and command line applications that provide a
convenient frontend to the Tapis API. In addition, we developed a data
processing notebook that can manipulate KGs on the Tapis servers, including
creations of a KG, data upload and modification. In this report we present the
software architecture, design and approach, the successfulness of our client
software, and future work.