• Fine-tuning LLMs with PEFT and LoRA

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.

• FreeSurfer Tutorials

The official MGH / Harvard tutorial page for FreeSurfer. The FreeSurfer group has provided and designed a series of tutorials for using FreeSurfer and for getting acquainted with the concepts needed to perform its various modes of analysis and processing of MRI data. The tutorials are designed to be followed along in a terminal window where commands can be copy/pasted instead of typed.

• solving DE with neural networks

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.

• Master’s in Cybersecurity Degree
• Take the lead: Find Online Cybersecurity Master’s Programs

Offers comprehensive information on various master's degree options in cybersecurity, including program details, admission requirements, and career opportunities, helping students make informed decisions about pursuing an advanced degree in cybersecurity.

• Better Scientific Software (BSSw) Main Site
• BSSw Resources and Blog Posts
• BSSw Tutorial - Github Pages

The Better Scientific Software (BSSw) project provides a community to collaborate and learn about best practices in scientific software development. Software—the foundation of discovery in computational science & engineering—faces increasing complexity in computational models and computer architectures. BSSw provides a central hub for the community to address pressing challenges in software productivity, quality, and sustainability.

• marimo | a next generation python notebook

Introduction seminar for new reactive python notebook from marimo ambassador.

• https://www.nextflow.io/
• https://www.nextflow.io/docs/latest/index.html

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.

• Neural Networks in Julia using Flux.jl

Making a neural network has never been easier! The following link directs users to the Flux.jl package, the easiest way of programming a neural network using the Julia programming language. Julia is the fastest growing software language for AI/ML and this package provides a faster alternative to Python's TensorFlow and PyTorch with a 100% Julia native programming and GPU support.

• Arkansas High Performance Computing Center

This link is a documentary website to use AHPCC.

• Beautiful Soup Docs

This package lets you easily scrape websites and extract information based on html tags and various other metadata found in the page. It can be useful for large-scale web analysis and other tasks requiring automated data gathering.

• Using MATLAB with Other Programming Languages

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.

• Displaying Scientific Data with Tableau

Tableau is a popular and capable software product for creating charts that present data and dashboards that allow you to explore data. It is typically used to present business or statistical data, but can also create compelling visualizations of scientific data. However, scientific data is often generated or stored in formats that are not immediately accessible by Tableau. This seminar will explore the data formats that work best with Tableau and the available mechanisms for generating scientific data in (or converting it to) those formats so that you can apply the full power of Tableau to create the best possible visualizations of your data.

• Easy MPI Tutorial
• Open MPI documentation

Workshop for beginners and intermediate students in MPI which includes helpful exercises. Open MPI documentation.

• Walkthrough Site
• Google Colab

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

• Jetstream2 Docs Site

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.

• OSF hosted knowledge base submission

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.

• DAGMan
• DAGMan Workflows

DAGMan (Directed Acyclic Graph Manager) is a meta-scheduler for HTCondor. It manages dependencies between jobs at a higher level than the HTCondor Scheduler. It is a workflow management system developed by the High-Throughput Computing (HTC) community, specifically for managing large-scale scientific computations and data analysis tasks. It enables users to define complex workflows as directed acyclic graphs (DAGs). In a DAG, nodes represent individual computational tasks, and the directed edges represent dependencies between the tasks. DAGMan manages the execution of these tasks and ensures that they are executed in the correct order based on their dependencies. The primary purpose of DAGMan is to simplify the management of large-scale computations that consist of numerous interdependent tasks. By defining the dependencies between tasks in a DAG, users can easily express the order of execution and allow DAGMan to handle the scheduling and coordination of the tasks. This simplifies the development and execution of complex scientific workflows, making it easier to manage and track the progress of computations.

• Using Jetstream 2 for Duke members (written for Duke OIT)

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.

• Neurostars

A question and answer forum for neuroscience researchers, infrastructure providers and software developers.

• Campus Champions Home

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

• Regulated Research Community of Practice

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.

• ACCESS KB Guide - DELTA

NCSA is the home of Delta, a computing and data resource that balances cutting-edge graphics processor and CPU architectures with a non-POSIX file system with a POSIX-like interface. Delta allows applications to reap the benefits of modern file systems without rewriting code.

• CS224N: NLP with Deep Learning

CS244N is a renowned natural language processing course offered by Stanford University and taught by Christopher Manning. It covers a wide range of topics in NLP, including language modeling, machine translation, sentiment analysis, and more. It teaches both foundational concepts and cutting-edge research to gain a comprehensive understanding of NLP techniques and applications.

• https://www.openmp.org/resources/tutorials-articles/

OpenMP (Open Multi-Processing) is an API that supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran on many platforms, instruction-set architectures and operating systems, including Solaris, AIX, FreeBSD, HP-UX, Linux, macOS, and Windows. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior.

• Application Fundamentals

The provided text discusses various aspects of Android app development fundamentals. It covers key concepts related to app components, the AndroidManifest.xml file, and app resources. Android apps are built using various components, including Activities, Services, Broadcast Receivers, and Content Providers. These components serve different purposes and have distinct lifecycles. Activities are used for user interaction, services for background tasks, broadcast receivers for system-wide event handling, and content providers for managing shared data.The AndroidManifest.xml file is essential for declaring app components, permissions, and other settings. It informs the Android system about the app's components and capabilities. For instance, it specifies the minimum API level, declares hardware and software requirements, and defines intent filters to enable components to respond to specific actions.It's crucial to declare app requirements, such as device features and minimum Android API levels, to ensure compatibility with different devices and configurations. These declarations help in filtering the app's availability on Google Play for users with compatible devices.Android apps rely on resources separate from code, including images, layouts, strings, and more. These resources are stored in various directories and can be tailored for different device configurations. Providing alternative resources allows for optimization across different languages, screen sizes, orientations, and other factors. Understanding these fundamentals is essential for developing Android applications effectively, ensuring compatibility, and providing a consistent user experience across a wide range of devices and configurations.