CI Links
Use these links “vetted” by the community. Additional CI links are always welcome.
HPC University
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A comprehensive list of training resources from the HPC University. HPCU is a virtual organization whose primary goal is to provide a cohesive, persistent, and sustainable on-line environment to share educational and training materials for a continuum of high performance computing environments that span desktop computing capabilities to the highest-end of computing facilities offered by HPC centers.
The Carpentries
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We teach foundational coding and data science skills to researchers worldwide.
Cornell Virtual Workshop
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Cornell Virtual Workshop is a comprehensive training resource for high performance computing topics. The Cornell University Center for Advanced Computing (CAC) is a leader in the development and deployment of Web-based training programs. Our Cornell Virtual Workshop learning platform is designed to enhance the computational science skills of researchers, accelerate the adoption of new and emerging technologies, and broaden the participation of underrepresented groups in science and engineering. Over 350,000 unique visitors have accessed Cornell Virtual Workshop training on programming languages, parallel computing, code improvement, and data analysis. The platform supports learning communities around the world, with code examples from national systems such as Frontera, Stampede2, and Jetstream2.
Using Linux commands in a python script (and the difference between the subprocess and os python modules)
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Learn how to use Linux commands in a python script. Specifically, learn how to use the subprocess and os modules in python to run shell commands (which run Linux commands) in a python script that is run on a cluster.
Enhanced Sampling for MD simulations
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Attention, Transformers, and LLMs: a hands-on introduction in Pytorch
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This workshop focuses on developing an understanding of the fundamentals of attention and the transformer architecture so that you can understand how LLMs work and use them in your own projects.
GIS: Geocoding Services
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Geocoding is the process of taking a street address and converting it into coordinates that can be plotted on a map. This conversion typically requires an API call to a remote server hosted by an organization/institution. The remote server will take the address attributes provided by you and the remote server will compare it to the data it contains and return a best estimate on the coordinates for that location.
There are many geocoding services available with different world coverages, quality of result, and set different rate limits for access. For R, a package called "tidygeocoder" provides an easy way to connect to these different services. As an additional benefit, their documentation provides a good summary of geocoding services available and links to their documentation. The link to the documentation for gecoding services accessible by "tidygeocoder" is provided below.
For Python, geopy package is a library that provides connection to various geocoding services. The link to the documentation for this package is also included below.
HPC Carpentry
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An HPC focused Carpentry community. Trainings include: HPC fundamentals, python, chapel, LAMMPS, parallelization with python, scaling studies, etc.
Gentle Introduction to Programming With Python
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This course from MIT OpenCourseWare (OCW) covers very basic information on how to get started with programming using Python. Lectures are available, along with practice assignments, to users at no cost. Python has many applications in tech today, from web frameworks to machine learning. This course will also instruct users on how to get set up with an IDE, which will allow for way more efficient debugging.
PyTorch for Deep Learning and Natural Language Processing
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PyTorch is a Python library that supports accelerated GPU processing for Machine Learning and Deep Learning. In this tutorial, I will teach the basics of PyTorch from scratch. I will then explore how to use it for some ML projects such as Neural Networks, Multi-layer perceptrons (MLPs), Sentiment analysis with RNN, and Image Classification with CNN.
Introduction to Deep Learning in Pytorch
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This workshop series introduces the essential concepts in deep learning and walks through the common steps in a deep learning workflow from data loading and preprocessing to training and model evaluation. Throughout the sessions, students participate in writing and executing simple deep learning programs using Pytorch – a popular Python library for developing, training, and deploying deep learning models.
DeapSECURE – Data-Enabled Advanced Computational Training Platform for Cybersecurity Research and Education
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DeapSECURE is a training program to infuse high-performance computational techniques into cybersecurity research and education. It is an NSF-funded project of the ODU School of Cybersecurity along with the Department of Electrical and Computer Engineering and the Information Technology Services at ODU. The DeapSECURE team has developed six non-degree training modules to expose cybersecurity students to advanced CI platforms and techniques rooted in big data, machine learning, neural networks, and high-performance programming. Techniques taught in DeapSECURE workshops are rather general and transferable to other areas including science, engineering, finance, linguistics, etc. All lesson materials are made available as open-source educational resources.
Data Visualization tools for Python
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Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It makes analyzing and presenting your data extremely easy and works with Python which many people already know.
ACCESS Pegasus Documentation
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The documentation provides an overview of using Pegasus, a workflow management system, on ACCESS resources for high throughput computing (HTC) workloads, covering logging in, workflow creation, resource configuration, and monitoring options.
Useful R Packages for Data Science and Statistics
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This Udacity article listed the most frequently used R packages for data science and statistics. For each package, the article provided the link to its official documentation. It will be a great start point if you want to start your data science journey in R.
Open OnDemand
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Open OnDemand is an easy-to-use web portal that lets students, researchers, and industry professionals use supercomputers from anywhere. It is installed on supercomputing resources at hundreds of sites. By eliminating the need for client software or command-line interface, Open OnDemand empowers users of all skill levels and significantly speeds up the time to their first computing.
Managing Python Packages on an HPC Cluster
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This workshop will go into the different ways python packages can be managed in a cluster environment using conda and python virtual environments both in batch mode from the command line and with Jupyter Notebooks and Jupyter Lab on the cluster. The examples will be run on the GMU HOPPER Cluster.
Version control with Git
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Understand the benefits of an automated version control system and the basics of how automated version control systems work. Configure git the first time it is used on a computer and understand the meaning of the --global configuration flag. Create a local Git repository and describe the purpose of the .git directory. Go through the modify-add-commit cycle for one or more files, explain where information is stored at each stage of that cycle, and distinguish between descriptive and non-descriptive commit messages.
Introduction to Python for Digital Humanities and Computational Research
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This documentation contains introductory material on Python Programming for Digital Humanities and Computational Research. This can be a go-to material for a beginner trying to learn Python programming and for anyone wanting a Python refresher.
Bash shell tutorial
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Training materials for using the bash (and zsh) shell.
AI/ML TechLab - Accelerating AI/ML Workflows on a Composable Cyberinfrastructure
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This technology lab contains a set of sessions to help a new user start an AI project on the ACES cluster, a composable accelerator testbed at Texas A&M University. You will learn how to create and activate a virtual environment, manipulate and visualize data with Pandas and Matplotlib, use Scikit-learn for linear regression and classification applications, and use Pytorch to create and train a simple image classification model with deep neural networks (DNN).
ACCESS KB Guide - DELTA
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NCSA is the home of Delta, a computing and data resource that balances cutting-edge graphics processor and CPU architectures with a non-POSIX file system with a POSIX-like interface. Delta allows applications to reap the benefits of modern file systems without rewriting code.
Moving-Lid-Driven Flow Simulation by Finite Difference Method
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The listed repository contains code written in C++ to model the flow inside a cavity with a lid moving above from left to right by discretizing incompressible N-S equations with finite difference method. For the governing equations, artificial viscosity has been considered to increase the stability. In terms of solving the resulted algebraic equation system, both the Point Jacobi Method and Symmetric Gauss Seidel methods have been used for the iteration process.
How the Little Jupyter Notebook Became a Web App: Managing Increasing Complexity with nbdev
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A tutorial entitled "How the Little Jupyter Notebook Became a Web App: Managing Increasing Complexity with nbdev" presented at SciPy 2023 in Austin, TX. This tutorial is hosted in a series of Jupyter Notebooks which can be accessed in the click of a button using Binder. See the README for more information.
Performance Engineering Of Software Systems
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A class from MITOpenCourseware that gives a hands on approach to building scalable and high-performance software systems. Topics include performance analysis, algorithmic techniques for high performance, instruction-level optimizations, caching optimizations, parallel programming, and building scalable systems.