Scalable Cosmic AI Inference using Cloud Serverless Computing with FMI

• URL: http://arxiv.org/abs/2501.06249v2
• Date: Sun, 09 Feb 2025 14:54:24 GMT
• Title: Scalable Cosmic AI Inference using Cloud Serverless Computing with FMI
• Authors: Mills Staylor, Amirreza Dolatpour Fathkouhi, Md Khairul Islam, Kaleigh O'Hara, Ryan Ghiles Goudjil, Geoffrey Fox, Judy Fox,
• Abstract summary: Large-scale astronomical image data processing and prediction is essential for astronomers.<n>Modern deep learning models offer high predictive accuracy, but they often demand substantial computational resources.<n>We introduce the Cloud-based Astronomy Inference framework to address these challenges.
• Score: 0.35337216626844875
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large-scale astronomical image data processing and prediction is essential for astronomers, providing crucial insights into celestial objects, the universe's history, and its evolution. While modern deep learning models offer high predictive accuracy, they often demand substantial computational resources, making them resource-intensive and limiting accessibility. We introduce the Cloud-based Astronomy Inference (CAI) framework to address these challenges. This scalable solution integrates pre-trained foundation models with serverless cloud infrastructure through a Function-as-a-Service (FaaS) Message Interface (FMI). CAI enables efficient and scalable inference on astronomical images without extensive hardware. Using a foundation model for redshift prediction as a case study, our extensive experiments cover user devices, HPC (High-Performance Computing) servers, and Cloud. CAI's significant scalability improvement on large data sizes provides an accessible and effective tool for the astronomy community. The code is accessible at https://github.com/UVA-MLSys/AI-for-Astronomy.

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