Knowledge Base Resources

Rocky Linux is an open-source enterprise operating system. It is compatible with Red Hat Enterprise Linux (RHEL). It is a community-driven project that provides a stable and reliable platform for production workloads. It is one of the best alternatives to Opensource CentOS, since Centos will be on end of life (EoL) soon in 2024 by shifting to CentOS Stream.

In this tutorial, I present an overview with many examples of the use of Numpy and Pandas for data analysis. Beginners in the field of data analysis can find It incredibly helpful, and at the same time, anyone who already has experience in data analysis and needs a refresher can find value in it. I discuss the use of Numpy for analyzing 1D and 2D multidimensional data and an introduction on using Pandas to manipulate CSV files.

Warewulf is an operating system provisioning platform for Linux that is designed to produce secure, scalable, turnkey cluster deployments that maintain flexibility and simplicity. It can be used to setup a stateless provisioning in HPC environment.

This code showcases how to work with the header-only nlohmann JSON library for C++. In order to compile, change the extensions from json_test.txt to json_test.cpp and test.txt to test.json. You must also download the header files from https://github.com/nlohmann/json. Complilation instructions are at the bottom of json_test. This code is very helpful for creating config files, for example.

A walkthrough (with a Google Colab link) on how to implement your own LSTM to observe time-dependent behavior.

Active inference is an emerging study field in machine learning and computational neuroscience. This website in particular introduces "active inference institute", which has established a couple of years ago, and contains a wide variety of resources for understanding the theory of active inference and for participating a worldwide active inference community.

This tutorial explains how to create an Anaconda Navigator Application (app) for JupyterLab. It is intended for users of Windows, macOS, and Linux who want to generate an Anaconda Navigator app conda package from a given recipe. Prior knowledge of conda-build or conda recipes is recommended.

A degree in business analytics looks different in today’s world than it did a decade ago. In its most current application, business analytics uses modern data science and capabilities in machine learning (ML). The magic comes into play when these are leveraged for strategic planning.

Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.

A library of short videos about ACCESS allocations, resources and support.

MOPAC (Molecular Orbital PACkage) is a semi-empirical quantum chemistry package used to compute molecular properties and structures by using approximations of the Schrödinger equation. This tutorial explains the process of using MOPAC for different forms of calculations.

Describes effective mentorship (both ways).

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.

Campus Champions foster a dynamic environment for a diverse community of research computing and data professionals sharing knowledge and experience in digital research infrastructure.

Differential equations, the backbone of countless physical phenomena, have traditionally been solved using numerical methods or analytical techniques. However, the advent of deep learning introduces an intriguing alternative: Physics-Informed Neural Networks (PINNs). By leveraging the representational power of neural networks and integrating physical laws (like differential equations), PINNs offer a novel approach to solving complex problems. This guide walks through an implementation of a PINN to solve DEs such as the logistic equation.

R GIS packages "rgdal", "rgeos", and "maptools" are package set to be archived and no longer supported by end of 2023. Many other R GIS packages are build on top of these packages, including "sp" and "raster". The packages recommended as replacement for "sp" is "sf" and the replacement for "raster" is "terra". Below are links to published articles regarding this transition. Additionally, I am including links to the documentation for the new packages recommended to be used "sf" and "terra".

As developers, we get excited to think about challenging problems. When you ask us what we are working on, our eyes light up like children in a candy store. So why is it that so many of our developer and software origin stories are not told? How did we get to where we are today, and what did we learn along the way? This podcast aims to look “Behind the Scenes of Tech’s Passion Projects and People.” We want to know your developer story, what you have built, and why. We are an inclusive community - whatever kind of institution or country you hail from, if you are passionate about software and technology you are welcome!

Nextflow is an open-source, domain-specific language and workflow manager designed for the execution and coordination of scientific and data-intensive computational workflows. It was specifically created to address the challenges faced by researchers and scientists when dealing with complex and scalable computational pipelines, particularly in fields such as bioinformatics, genomics, and data analysis. Here provided some links to start with.

Slides for a tutorial on Machine Learning applications in Engineering and parameter tuning given at the RMACC conference 2019.

A guide for Duke OIT on how to advise users on using ACCESS and allocation credits to jetstream 2 for Duke University members. This can be used for non Duke members. Assumes the reader has basic knowledge of ACCESS.

Spatial Data Science is a growing field across a wide range of industries and disciplines. The open-source programming language Python has many libraries that support spatial analysis, but what do you do when your computer is unable to tackle the massive file sizes of high-resolution data and the computing power required in your analysis? There materials have been prepared to teach you spatial data science and how to execute your analysis using a high-performance computer (HPC).

The following link elaborates the usage of OpenMP API and its related syntax. There are also several exercises available for learners to help them get familiar with this widely-used tool for multi-threaded realization.

OnShape FeatureScripts allow users to create their own features via OnShape's programming language. The user can make these as simple or complex as they need, and they can save tons of time for heavy OnShape users or complex projects!

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.