Knowledge Base Resources

This beginner-friendly guide introduces Retrieval-Augmented Generation (RAG), a technique to enhance Large Language Models (LLMs) by integrating external data sources. It covers the fundamentals of AI, LLMs, and RAG, providing step-by-step instructions, examples, and visual aids. The guide also discusses tools like Milvus, Faiss, and LangChain, offering a practical approach to building smarter AI systems.

This repository offers accessible resources and workshops on AI and high-performance computing (HPC), designed for both STEM and non-STEM majors. The materials are presented in simple language, requiring no prior technical background, making them suitable for a wide range of learners. The focus is on bridging the AI digital gap and enabling participants to harness the power of AI and HPC for research, innovation, and discovery.

This workshop has an introduction to the concepts of visualization followed by hands on exercises. The concepts section has Speaker Notes, and the hands on section has an accompanying Jupyter notebook. The workshop is one in a series of Introduction to HPC

A tutorial on the effective use of Dask on HPC resources. The four-hour tutorial will be split into two sections, with early topics focused on novice Dask users and later topics focused on intermediate usage on HPC and associated best practices. The knowledge areas covered include (but are not limited to): Beginner section High-level collections including dask.array and dask.dataframe Distributed Dask clusters using HPC job schedulers Earth Science data analysis using Dask with Xarray Using the Dask dashboard to understand your computation Intermediate section Optimizing the number of workers and memory allocation Choosing appropriate chunk shapes and sizes for Dask collections Querying resource usage and debugging errors