Python Data and Viz Training (CCEP Program)
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Containerization Explained
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Containerization is a software development method in which applications are packaged into standard units for development, shipment, and deployment.
MATLAB with other Programming Languages
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MATLAB is a really useful tool for data analysis among other computational work. This tutorial takes you through using MATLAB with other programming languages including C, C++, Fortran, Java, and Python.
OpenMP and Multithreaded Jobs in GRASS
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Techniques and support for multithreaded geospatial data processing in GRASS.
Ultimate guide to Unix
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Unix is incredibly common and useful. This website provides all the common commands and explanations for one to get started with a unix system.
Vulkan Support Survey across Systems
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It's not uncommon to see beautiful visualizations in HPC center galleries, but the majority of these are either rendered off the HPC or created using programs that run on OpenGL or custom rasterization techniques. To put it simply the next generation of graphics provided by OpenGL's successor Vulkan is strangely absent in the super computing world. The aim of this survey of available resources is to determine the systems that can support Vulkan workflows and programs. This will assist users in getting past some of the first hurdles in using Vulkan in HPC contexts.
Awesome Jupyter Widgets (for building interactive scientific workflows or science gateway tools)
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A curated list of awesome Jupyter widget packages and projects for building interactive visualizations for Python code
ACCESS Support Portal
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Samtools Documentation
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Samtools is a suite of programs for interacting with high-throughput sequencing data, especially in the SAM/BAM format. It offers various utilities for processing, analyzing, and managing sequence data generated from next-generation sequencing (NGS) experiments. Samtools is widely used in bioinformatics and genomics research for tasks such as read alignment, variant calling, and data manipulation.
Linux Tutorial from Ryan's Tutorials
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The following pages are intended to give you a solid foundation in how to use the terminal, to get the computer to do useful work for you. You won't be a Unix guru at the end but you will be well on your way and armed with the right knowledge and skills to get you there if that's what you want (which you should because that will make you even more awesome). Here you will learn the Linux command line (Bash) with our 13 part beginners tutorial. It contains clear descriptions, command outlines, examples, shortcuts and best practice. At first, the Linux command line may seem daunting, complex and scary. It is actually quite simple and intuitive (once you understand what is going on that is), and once you work through the following sections you will understand what is going on. Unix likes to take the approach of giving you a set of building blocks and then letting you put them together. This allows us to build things to suit our needs. With a bit of creativity and logical thinking, mixed in with an appreciation of how the blocks work, we can assemble tools to do virtually anything we want. The aim is to be lazy. Why should we do anything we can get the computer to do for us? The only reason I can think of is that you don't know how (but after working through these pages you will know how, so then there won't be a good reason). A question that may have crossed your mind is "Why should I bother learning the command line? The Graphical User Interface is much easier and I can already do most of what I need there." To a certain extent you would be right, and by no means am I suggesting you should ditch the GUI. Some tasks are best suited to a GUI, word processing and video editing are great examples. At the same time, some tasks are more suited to the command line, data manipulation (reporting) and file management are some good examples. Some tasks will be just as easy in either environment. Think of the command line as another tool you can add to your belt. As always, pick the best tool for the job.
Charliecloud User Group
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Announcements for for users and developers of Charliecloud, which provides lightweight user-defined software stacks for high-performance computing.
Regulated Research Community of Practice
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The daily news clearly shows the increasing threat to safety and privacy of data, personal as well as intellectual property. While the requirements such as DFARS 7012, HIPAA, and Cybersecurity Maturity Model Certification (CMMC) improve the consistency of data handling between agencies and contractors and grantees, it leaves academic institutions to figure out how to meet such requirements in a cost-effective way that fits the research and education mission of the institution. Most institutions, agencies, and companies act in isolation with one-off contract language to address data security and safeguarding concerns. Even though cybersecurity has a clear and uniform goal of protecting data, a onesize solution does not fit all academic institutions.
By supporting this community with development of a community strategic roadmap, regular discussions and workshops, and a repository of generalized and specific resources for handling regulated research programs RRCoP lowers the barrier to entry for institutions handling new regulations.
Reinforcement Learning For Beginners with Python
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This course takes through the fundamentals required to get started with reinforcement learning with Python, OpenAI Gym and Stable Baselines. You'll be able to build deep learning powered agents to solve a varying number of RL problems including CartPole, Breakout and CarRacing as well as learning how to build your very own/custom environment!
TensorFlow for Deep Neural Networks
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TensorFlow is a powerful framework for Deep Learning, developed by google. This specifically is their python package, which is easy to use and can be used to train incredibly powerful models.
Language models and using HPC resources
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Documentation and research based on the latest NLP text generation detection methods for 2023.
Neurodesk
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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.
Python
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Python course offered by Texas A&M HPRC
Data Imputation Methods for Climate Data and Mortality Data
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This slices and videos introduced how to use K-Nearest-Neighbors method to impute climate data and how to use Bayesian Spatio-Temporal models in R-INLA to impute mortality data. The demos will be added soon.
Automated Machine Learning Book
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The authoritative book on automated machine learning, which allows practitioners without ML expertise to develop and deploy state-of-the-art machine learning approaches. Describes the background of techniques used in detail, along with tools that are available for free.
Introduction to Parallel Computing Tutorial
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The tutorial is intended to provide a brief overview of the extensive and broad topic of Parallel Computing. It covers the basics of parallel computing, and is intended for someone who is just becoming acquainted with the subject .
Implementing Markov Processes with Julia
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The following link provides an easy method of implementing Markov Decision Processes (MDP) in the Julia computing language. MDPs are a class of algorithms designed to handle stochastic situations where the actor has some level of control. For example, used at a low level, MDPs can be used to control an inverted pendulum, but applied in higher level decision making the can also decide when to take evasive action in air traffic management. MDPs can also be extended to the partially observable domain to form the Partially Observable Markov Decision Process (POMDP). This link contains a wealth of information to show one can easily implement basic POMDP and MDP algorithms and apply well known online and offline solvers.
Installing Rocky Linux Operating System
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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.
Slurm Scheduling Software Documentation
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Slurm is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for large and small Linux clusters. Slurm requires no kernel modifications for its operation and is relatively self-contained. As a cluster workload manager, Slurm has three key functions. First, it allocates exclusive and/or non-exclusive access to resources (compute nodes) to users for some duration of time so they can perform work. Second, it provides a framework for starting, executing, and monitoring work (normally a parallel job) on the set of allocated nodes. Finally, it arbitrates contention for resources by managing a queue of pending work.
Introductory Tutorial to Numpy and Pandas for Data Analysis
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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.
Time-Series LSTMs Python Walkthrough
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A walkthrough (with a Google Colab link) on how to implement your own LSTM to observe time-dependent behavior.