Workshop Overview Introduction UVA HPC Multi-GPU Strategies Accelerate DeepSpeed vLLM Best Practices Wrap Up

An Introduction to using and building software containers.

Overview This short course provides a practical introduction to building artificial neural networks using PyTorch, a powerful and flexible deep l earning framework. The course covers the fundamentals of PyTorch, including tensors, automatic differentiation, and model building.

Python, like most interpreted languages, can be very slow. But there are best practices and some programming tricks that can speed it up considerably. This can make the difference between finishing the work in an acceptable time, or being unable to finish a project.

Using VS Code on a remote HPC system may be different from local use. This tutorial introduces best practices for VS Code on HPC.

This tutorial introduces various techniques and strategies to help users more efficiently use UVA's HPC System.

This tutorial is an introduction to running large language models on UVA's HPC system.

This tutorial is an introduction to multi-GPU strategies for large language model (LLM) inference using tools like Accelerate, DeepSpeed, and vLLM.

This tutorial introduces the basics of GPU computing and demonstrates how to run AlphaFold on the HPC cluster to predict protein structures.

In this hands-on workshop, you will be introduced to parallel and distributed computing in MATLAB™ for speeding up your application and offloading work.