Fundamentals of Cloud Computing
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An introduction to Cloud Computing
Ask.CI Q&A Platform for Research Computing
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ACCESS Video Learning Center
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A library of short videos about ACCESS allocations, resources and support.
Texas A&M HPRC Training Site
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Training Resources and Courses offered by Texas A&M's Research Computing Group
Set Up VSCode for Python and Github
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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.
Fine-tuning LLMs with PEFT and LoRA
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As LLMs get larger fine-tuning to the full extent can become difficult to train on consumer hardware. Storing and deploying these tuned models can also be quite expensive and difficult to store. With PEFT (parameter -efficent fine tuning), it approaches fine-tune on a smaller scale of model parameters while freezing most parameters of the pretrained LLMs. Basically it is providing full performance that which is similar if not better than full fine tuning while only having a small number of trainable parameters. This source explains that as well as going over LORA diagrams and a code walk through.
Oakridge Leadership Computing Facility (OLCF) Training Events and Archive
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Upcoming training events and archives of training materials detailing general HPC best practices as well as how to use OLCF resources and services.
Master's in Data Science Program Guide - TechGuide
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A master’s degree in data science helps prepare professionals to take the next career step. This article will focus primarily on data science, a graduate degree in this field, and a data scientist or data analyst career. With many employers preferring a master’s degree in data science for those seeking to fill roles as data scientists or analysts, we will discuss the data science master’s degree in detail.
Factor Graphs and the Sum-Product Algorithm
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A tutorial paper that presents a generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph. Following a single, simple computational rule, the sum-product algorithm computes either exactly or approximately various marginal functions derived from the global function. A wide variety of algorithms developed in artificial intelligence, signal processing, and digital communications can be derived as specific instances of the sum-product algorithm, including the forward/backward algorithm, the Viterbi algorithm, the iterative "turbo" decoding algorithm, Pearl's (1988) belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform (FFT) algorithms
marimo | a next generation python notebook
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Introduction seminar for new reactive python notebook from marimo ambassador.
InsideHPC
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InsideHPC is an informational site offers videos, research papers, articles, and other resources focused on machine learning and quantum computing among other topics within high performance computing.
ConnectCI
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Connect.Cybinfrastructure is a family of portals, each representing a program that is serving a segment of the research computing and data community. Each portal provides program-specific information, as well a custom "view" into a common database. The portal was originally developed to support project workflows and a knowledge base of self service learning resources for the Northeast Cyberteam. Subsequently, it was expanded to provide support to multiple cyberteams and other research computing communities of practice. We welcome additional communities, please contact us if you are interested in participating. Central to the Portal is an extensive and ever-evolving tagging infrastructure which informs every aspect of the Portal. The tag taxonomy was initially developed by the Northeast Cyberteam to categorize subject matter relevant to practitioners of Research Computing Facilitation and is ever changing due to the frequent introduction of new technology in domains that characterize the field of research computing.
National Public Radio (NPR)
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Pluses and challenges of mentor selection. Offers tips for acquiring a mentor (finding, asking). And how to be a good mentee. SMART framework mentioned. Discrimination mentioned. Difference between mentor and sponsor underlined. More than one mentor encouraged. Good tips.
AHPCC documentary
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This link is a documentary website to use AHPCC.
Data visualization with Matplotlib
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Data visualization is a critical aspect of data analysis. It allows for a clear and concise representation of data, making it easier for users to understand and interpret complex datasets. One of the most popular libraries for data visualization in Python is Matplotlib. The included website aims to provide a brief overview of Matplotlib, its features, and examples/exercises to dive deeper into its functionalities.
ACES: Charliecloud Containers for Scientific Workflows (Tutorial)
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This tutorial introduces the use of Containers using the Charliecloud software suite. This tutorial will provide participants with background and hands-on experience to use basic Charliecloud containers for HPC applications. We discuss what containers are, why they matter for HPC, and how they work. We'll give an overview of Charliecloud, the unprivileged container solution from Los Alamos National Laboratory's HPC Division. Students will learn how to build toy containers and containerize real HPC applications, and then run them on a cluster. Exercises are demonstrated using the ACES cluster, a composable accelerator testbed at Texas A&M University. Students with an allocation on the ACES cluster can follow along with the ACES-specific exercises.
Active inference textbook
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This textbook is the first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines including computational neurosciences, machine learning, artificial intelligence, and robotics. It was published in 2022 and it's open access at this time. The contents in this textbook should be educational to those who want to understand how the free energy principle is applied to the normative behavior of living organisms and who want to widen their knowledge of sequential decision making under uncertainty.
Python Data and Viz Training (CCEP Program)
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Geocomputation with R (Free Reference Book)
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Below is a link for a book that focuses on how to use "sf" and "terra" packages for GIS computations. As of 5/1/2023, this book is up to date and examples are error free. The book has a lot of information but provides a good overview and example workflows on how to use these tools.
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.
Gaussian 16
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Gaussian 16 is a computational chemistry package that is used in predicting molecular properties and understanding molecular behavior at a quantum mechanical level.
Trusted CI Resources Page
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Very helpful list of external resources from Trusted CI
DELTA Introductory Video
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Introductory video about DELTA. Speaker Tim Boerner, Senior Assistant Director, NCSA
File management of Visual Studio Code on clusters
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Visual Studio Code, commonly known as VSCode, is a popular tool used by programmers worldwide. It serves as a text editor and an Integrated Development Environment (IDE) that supports a wide variety of programming languages. One of its key features is its extensive library of extensions. These extensions add on to the basic functionalities of VSCode, making coding more efficient and convenient.
However, there's a catch. When these extensions are installed and used frequently, they generate a multitude of files. These files are typically stored in a folder named .vscode-extension within your home directory. On a cluster computing facility such as the FASTER and Grace clusters at Texas A&M University, there's a limitation on how many files you can have in your home directory. For instance, the file number limit could be 10000, while the .vscode-extension directory can hold around 4000 temporary files even with just a few extensions. Thus, if the number of files in your home directory surpasses this limit due to VSCode extensions, you might face some issues. This restriction can discourage users from taking full advantage of the extensive features and extensions offered by the VSCode editor.
To overcome this, we can shift the .vscode-extension directory to the scratch space. The scratch space is another area in the cluster where you can store files and it usually has a much higher limit on the number of files compared to the home directory. We can perform this shift smoothly using a feature called symbolic links (or symlinks for short). Think of a symlink as a shortcut or a reference that points to another file or directory located somewhere else.
Here's a step-by-step guide on how to move the .vscode-extension directory to the scratch space and create a symbolic link to it in your home directory:
1. Copy the .vscode-extension directory to the scratch space: Using the cp command, you can copy the .vscode-extension directory (along with all its contents) to the scratch space. Here's how:
cp -r ~/.vscode-extension /scratch/user
Don't forget to replace /scratch/user with the actual path to your scratch directory.
2. Remove the original .vscode-extension directory: Once you've confirmed that the directory has been copied successfully to the scratch space, you can remove the original directory from your home space. You can do this using the rm command:
rm -r ~/.vscode-extension
It's important to make sure that the directory has been copied to the scratch space successfully before deleting the original.
3. Create a symbolic link in the home directory: Lastly, you'll create a symbolic link in your home directory that points to the .vscode-extension directory in the scratch space. You can do this as follows:
ln -s /scratch/user/.vscode-extension ~/.vscode-extension
By following this process, all the files generated by VSCode extensions will be stored in the scratch space. This prevents your home directory from exceeding its file limit. Now, when you access ~/.vscode-extension, the system will automatically redirect you to the directory in the scratch space, thanks to the symlink. This method ensures that you can use VSCode and its various extensions without worrying about hitting the file limit in your home directory.
Official Documentation of VisIt
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VisIt is a prominent open-source, interactive parallel visualization and graphical analysis tool predominantly used for viewing scientific data. Its GitHub repository offers a detailed insight into the software's source code, documentation, and contribution guidelines. In particular, it offers useful examples on how it