Knowledge Base Resources

We teach foundational coding and data science skills to researchers worldwide.

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.

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.

This is a great mentoring resource and has many articles related to mentoring. It is a one-stop shop for mentoring, and at the bottom, there are tags based on topics, and interested users can pick and choose articles and resources on different types of mentorship.

This comprehensive workshop is designed to guide participants through the world of cryptography, from foundational concepts to advanced implementations. Starting with the basics of encryption, decryption, and hashing, the workshop discusses real-world applications like SSL, blockchain, and digital signatures. Interactive Python-based coding examples, such as symmetric and asymmetric encryption, will provide hands-on experience. Participants will also learn to identify cryptographic vulnerabilities and perform attacks like length extension. Finally, the workshop also explores future trends such as quantum cryptography and zero-knowledge proofs, providing participants with the knowledge to apply cryptography in securing modern digital systems. Ideal for beginners and intermediate learners alike, this workshop is a step-by-step journey into mastering cryptographic principles and practices.

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.

This is a beginner-friendly tutorial on how to set up your web server using Rust!

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.

DARWIN (Delaware Advanced Research Workforce and Innovation Network) is a big data and high performance computing system designed to catalyze Delaware research and education

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.

This is the main documentation repo for the Open OnDemand Portal which enables researchers to access HPC resources from a familiar web interface.

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.

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.

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.

An HPC focused Carpentry community. Trainings include: HPC fundamentals, python, chapel, LAMMPS, parallelization with python, scaling studies, etc.

The mission of Trusted CI is to lead in the development of an NSF Cybersecurity Ecosystem with the workforce, knowledge, processes, and cyberinfrastructure that enables trustworthy science and NSF’s vision of a nation that is a global leader in research and innovation.

These are the templates for the launch and wrap presentations used by the Cyberinfrastructure Community-Wide Mentorship Network

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.

Monthly workshops sponsored by ACCESS on a variety of HPC topics organized by Pittsburgh Supercomputing Center (PSC). Each workshop will be telecast to multiple satellite sites and workshop materials are archived.

In this presentation, I will explore the recent advancements in AI-driven production of 3D-generative assets and environments, particularly focusing on their application in creating immersive, playful experiences. Platforms such as ChatGPT, Suno, and Speechify have ushered in a new era of digital creativity, facilitating the development of environments that not only entertain but also serve educational purposes. This session will delve into how these technologies are integrated into academic settings, specifically through a case study of the English Department's Digital Media Lab, known as Tech/Tech, which opened in 2022.

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.

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.

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.

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.