Upcoming Training Workshops

Nvidia 5 Day Digital Training Event

Day 1: 1:00 pm – 2:00 pm CST | October 20, 2022
Day 2: 1:00 pm – 2:00 pm CST | October 27, 2022
Day 3: 1:00 pm – 4:00 pm CST | November 3, 2022
Day 4: 1:00 pm – 5:00 pm CST | November 10, 2022
Day 5: 1:00 pm – 5:00 pm CST | November 17, 2022

Day 1: Parabricks – GPU-Accelerated Genome Sequencing Analysis
Genomic sequencing is faster and cheaper than ever. The new bottleneck in the genomics pipeline is in the analysis. It can take upwards of 30 hours to run variant calling on a single sample, it could take months or even years to process thousands of samples. This is where CLARA Parabricks comes in. Using GPU acceleration, we have cut down the variant calling time to below 30 minutes for a 30x human genome. This allows for new genomics projects to be done at a scale that was not previously possible. In this session, we will discuss the capabilities of Parabricks, the performance compared to traditional genomics software packages (such as GATK), and show a demo of what it looks like in action.
Registration: https://ris.wustl.edu/?p=3148

Day 2: Fast Training with MONAI Core
MONAI Core, as a part of the open-source community-led MONAI Project, is a PyTorch-based and GPU-accelerated Deep Learning framework specifically built for medical imaging. With its tremendous momentum, MONAI Core is becoming the `Medical Imaging PyTorch`. In this session, we’ll introduce the powerful features of MONAI Core and how to boost your training performance with it.
Registration: https://ris.wustl.edu/?p=3151

Day 3: Using GPUs with Python
In this workshop, you’ll get hands-on experience accelerating Python codes with NVIDIA GPUs. We will utilize code samples in three main categories to introduce you to Python GPU accelerated computing. First, we will explore drop-in replacements for SciPy and NumPy code through the CuPy library. Next we’ll cover NVIDIA RAPIDS, which provides GPU acceleration for end-to-end data science workloads. Finally we’ll cover Numba, which gives you the flexibility to write custom accelerated code without leaving the Python language. We’ll finish with an end-to-end example that incorporates all the tools introduced to tackle a geospatial problem. By the end of the workshop, you’ll have the skills to start accelerating your own Python codes with NVIDIA GPUs
Registration: https://ris.wustl.edu/?p=3154

Day 4 & 5: Fundamentals of Accelerated Data Science
In this workshop, you’ll learn how to build and execute end-to-end GPU-accelerated data science workflows that enable you to quickly explore, iterate, and get your work into production. Using the RAPIDS-accelerated data science libraries, you’ll apply a wide variety of GPU-accelerated machine learning algorithms, including XGBoost, cuGRAPH’s single-source shortest path, and cuML’s KNN, DBSCAN, and logistic regression to perform data analysis at scale. 
Registration: https://ris.wustl.edu/?p=3157

Computing Basics – Software Applications Needed to Work in High Performance Computing Environments

10:00 am – 11:30 am | September 22, 2022
Zoom link will be provided before the workshop

This virtual workshop is offered in collaboration with WUIT’s Research Infrastructure Services (RIS). The workshop will introduce text editors, shell and batch scripts, Docker container technology, and the ssh protocol. Attendees will learn how to write, edit and run shell scripts, how to create a simple Docker container, and how to connect to the RIS compute platform.

Computing Basics – Submitting Jobs to the RIS Scientific Compute Platform

10:00 am – 11:30 am | September 29, 2022
Zoom link will be provided before the workshop

This virtual workshop is offered in collaboration with WUIT’s Research Infrastructure Services (RIS) and will introduce basic commands for submitting jobs to the queue on RIS compute platform. Attendees will learn how to submit and run jobs/tasks using Docker and queuing system commands.

Previous Workshop Recordings and Resources