Knowledge Base Resources
These resources have been contributed and “vetted” by the community of cyberinfrastructure professionals (researchers, research computing facilitators, research software engineers and HPC system administrators) that are participating in programs such as this one, that are supported by the ConnectCI community management platform. Additional Knowledge Base Resources are always welcome!
HPC University
3
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.
Cornell Virtual Workshop
1
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.
The Carpentries
2
We teach foundational coding and data science skills to researchers worldwide.
DeapSECURE – Data-Enabled Advanced Computational Training Platform for Cybersecurity Research and Education
1
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.
HPC Carpentry
1
An HPC focused Carpentry community. Trainings include: HPC fundamentals, python, chapel, LAMMPS, parallelization with python, scaling studies, etc.
Gentle Introduction to Programming With Python
1
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.
Version control with Git
1
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.
The Chronicle of Evidence-Based Mentoring
1
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.
Attention, Transformers, and LLMs: a hands-on introduction in Pytorch
1
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.
Using Linux commands in a python script (and the difference between the subprocess and os python modules)
1
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.
Open OnDemand
1
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.
Introduction to Deep Learning in Pytorch
1
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.
C Programming
0
"These notes are part of the UW Experimental College course on Introductory C Programming. They are based on notes prepared (beginning in Spring, 1995) to supplement the book The C Programming Language, by Brian Kernighan and Dennis Ritchie, or K&R as the book and its authors are affectionately known. (The second edition was published in 1988 by Prentice-Hall, ISBN 0-13-110362-8.) These notes are now (as of Winter, 1995-6) intended to be stand-alone, although the sections are still cross-referenced to those of K&R, for the reader who wants to pursue a more in-depth exposition." C is a low-level programming language that provides a deep understanding of how a computer's memory and hardware work. This knowledge can be valuable when optimizing apps for performance or when dealing with resource-constrained environments.C is often used as the foundation for creating cross-platform libraries and frameworks. Learning C can allow you to develop libraries that can be used across different platforms, including iOS, Android, and desktop environments.
Setting up PyFR flow solver on clusters
0
These instructions were executed on the FASTER and Grace cluster computing facilities at Texas A&M University. However, the process can be applied to other clusters with similar environments. For local installation, please refer to the PyFR documentation.
Please note that these instructions were valid at the time of writing. Depending on the time you're executing these, the versions of the modules may need to be updated.
1. Loading Modules
The first step involves loading pre-installed software libraries required for PyFR. Execute the following commands in your terminal to load these modules:
module load foss/2022b
module load libffi/3.4.4
module load OpenSSL/1.1.1k
module load METIS/5.1.0
module load HDF5/1.13.1
2. Python Installation from Source
Choose a location for Python 3.11.1 installation, preferably in a .local directory. Navigate to the directory containing the Python 3.11.1 source code. Then configure and install Python:
cd $INSTALL/Python-3.11.1/
./configure --prefix=$LOCAL --enable-shared --with-system-ffi --with-openssl=/sw/eb/sw/OpenSSL/1.1.1k-GCCcore-11.2.0/ PKG_CONFIG_PATH=$LOCAL/pkgconfig LDFLAGS=/usr/lib64/libffi.so.6.0.2
make clean; make -j20; make install;
3. Virtual Environment Setup
A virtual environment allows you to isolate Python packages for this project from others on your system. Create and activate a virtual environment using:
pip3.11 install virtualenv
python3.11 -m venv pyfr-venv
. pyfr-venv/bin/activate
4. Install PyFR Dependencies
Several Python packages are required for PyFR. Install these packages using the following commands:
pip3 install --upgrade pip
pip3 install --no-cache-dir wheel
pip3 install --no-cache-dir botorch pandas matplotlib pyfr
pip3 uninstall -y pyfr
5. Install PyFR from Source
Finally, navigate to the directory containing the PyFR source code, and then install PyFR:
cd /scratch/user/sambit98/github/PyFR/
python3 setup.py develop
Congratulations! You've successfully set up PyFR on the FASTER and Grace cluster computing facilities. You should now be able to use PyFR for your computational fluid dynamics simulations.
Trusted CI
0
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.
Handwritten Digits Tutorial in PyTorch
0
This tutorial is essentially the "hello world" of image recognition and feed-forward neural network (using PyTorch). Using the MNIST database (filled within images of handwritten digits), the tutorial will instruct how to build a feed-forward neural network that can recognize handwritten digits. A solid understanding of feed-forward and back-propagation is recommended.
Applications of Machine Learning in Engineering and Parameter Tuning Tutorial
0
Slides for a tutorial on Machine Learning applications in Engineering and parameter tuning given at the RMACC conference 2019.
Jetstream Home
0
Jetstream2 makes cutting-edge high-performance computing and software easy to use for your research regardless of your project’s scale—even if you have limited experience with supercomputing systems.Cloud-based and on-demand, the 24/7 system includes discipline-specific apps. You can even create virtual machines that look and feel like your lab workstation or home machine, with thousands of times the computing power.
Regular Expressions
0
Regular expressions (sometimes referred to as RegEx) is an incredibly powerful tool that is used to define string patterns for "find" or "find and replace" operations on strings, or for input validation. Regular Expressions are used in search engines, in search and replace dialogs of word processors and text editors, and text-processing Linux utilities such as sed and awk. They are supported in many programming languages, including Python, R, Perl, Java, and others.
Science Gateway Tool/Web App Template (Jupyter Notebook + ipywidgets)
0
Use this template to turn any science gateway workflow into a web application!
ACCESS HPC Workshop Series
0
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.
Bridges-2 Home Page
0
Landing Page for Bridges-2 information
Neurodesk
0
Neurodesk provides a containerised data analysis environment to facilitate reproducible analysis of neuroimaging data. Analysis pipelines for neuroimaging data typically rely on specific versions of packages and software, and are dependent on their native operating system. These dependencies mean that a working analysis pipeline may fail or produce different results on a new computer, or even on the same computer after a software update. Neurodesk provides a platform in which anyone, anywhere, using any computer can reproduce your original research findings given the original data and analysis code.
Set Up VSCode for Python and Github
0
VSCode is a popular IDE that runs on Windows, MacOS, and Linux. This tutorial will explain how to get set up with VSCode to code in Python. It will also provide a tutorial on how to set up Github integration within VSCode.
FSL Lectures
0
This is the official University of Oxford FSL group lecture page. This includes information on upcoming and past courses (online and in-person), as well as lecture materials. Available lecture materials includes slides and recordings on using FSL, MR physics, and applications of imaging data.