•
•
1 min read
Distributed AI/ML model training on HPC clusters
This tutorial will cover the basic concepts and fundamentals of High Performance Computing (HPC), Artificial Intelligence (AI), why HPC is important for AI, and distributed training strategies with a focus on PyTorch Distributed Data Parallel (DDP). Through handson exercises, participants will learn step by step how to scale training from a single GPU to multiple GPUs on a single node, and finally extending to multi-node distributed environment.
Related
See all Online AgendaTalk to Your ML Models. Insights from Sensitivity Analysis (XVARS) and Explainable AI (XAI)
Sensitivity Analysis (XVARS) and Explainable AI (XAI)
Written by
Banamali Panigrahi
Large Language Model (LLM) Agent in Hydrology
This tutorial introduces AQUAH, the first language-based agent specifically designed for hydrologic modeling. With simple natural-language prompts (e.g., 'simulate floods for a specific basin'), AQUAH autonomously retrieves data, configures hydrologic models, executes simulations, and produces analyst-ready reports, simplifying complex environmental modeling tasks.
Written by
Zhi Li
Web Application Development for Hydrologic Modeling and Visualization
This session introduces Tethys Platform, an open-source framework for building earth-science- focused web applications. For over a decade, scientists have used Tethys to build web applications for hydrologic modeling, visualization, and decision support. Participants will see two complementary approaches to web app development.
Start the conversation