• Docs

MediaPipe is Google's open-source framework for building multimodal (e.g., video, audio, etc.) machine learning pipelines. It is highly efficient and versatile, making it perfect for tasks like gesture recognition. This is a tutorial on how to make a custom model for gesture recognition tasks based on the Google MediaPipe API. This tutorial is specifically for video-playback, though could be generalized to image and live-video feed recognition.

• Building the ArduPilot environment for Linux

This article provides instructions for building AirSim, an open-source simulator for autonomous vehicles, on Linux. It outlines the steps to build Unreal Engine, clone and build the AirSim repository, and set up the Unreal environment. It also includes information on how to use AirSim and optional setups such as remote control for manual flight.

• Using ACCESS at Duke

This is a short video on how to exchange ACCESS credits and connect to Jetstream 2 (please note this was created for Duke users but applies to all) .

• Docker Container Library

The Docker container library, commonly known as Docker Hub, is a vast repository that hosts a multitude of pre-configured container images, streamlining the deployment process. It can drastically speed up a workflow, and gives you a consistent starting point each time. Check it out, they might have exactly what you are looking for!

• json_test.txt
• test.txt

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.

• 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.

• PyTorch Tutorial at Stanford University

This is a very barebones introduction to the PyTorch framework used to implement machine learning. This tutorial implements a feed-forward neural network and is taught completely asynchronously through Stanford University. A good start after learning the theory behind feed-forward neural networks.

• RMACC Sys Admin Workshop '24 Slides

A compilation of the slides from this year's RMACC Sys Admin Workshop. RMACC Sys Admin Workhop Schedule: Tuesday 12:00 PM Sign-in 1:00 PM Introductions 1:30 PM Lightning Talk - HPC Survival guide 2:00 PM Node Management - Scott Serr 2:30 PM Lightning Talk - Warewulf 3:00 PM Urgent HPC - Coltran Hophan-Nichols and Alexander Salois Wednesday 9:00 AM Breakfast 10:00 AM Round table Sites - BYU, INL, UMT, ASU, MSU 11:00 AM Open OnDemand setup - Dean Anderson 11:30 AM Lightning talk - Long term hardware support 12:00 PM Lunch 1:00 PM HPC Security - Matt Bidwell 2:00 PM Lightning talk- Security 2:30 PM ACCESS resources - Couso 3:00 PM Easybuild tutorial - Alexander Salois 3:30 PM General Q & A Thursday 9:00 AM Breakfast 10:00 AM Lightning Talk- Containers and Virtual Machines 11:00 AM University of Montana - Hellgate Site Tour 11:30 AM Closing Remarks

• GROMACS - MDTutorials.com
• OpenMM - Making It Rain
• NAMD - UIUC

Links to MD tutorials for beginner's across various simulation platforms.

• Regression forecasting and predicting - Practical Machine Learning Tutorial with Python p.5

This video series provides a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. It covers topics such as linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. Goes over the high level intuitions of the algorithms and how they are logically meant to work. Apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn.

• Data Analysis with R for Educators

This webinar series is an orientation to R. We start with an overview of R’s history and place in the larger data science ecosystem. Next, we introduce the R Studio user interface and how to access R’s excellent documentation. Finally, we present the fundamental concepts you need to use the R environment and language for data analysis. Along the way, we compare R script files (.R) to R Notebook (.Rmd) files and show how the features of R Notebook support better communication and encourage more dynamic engagement with statistical analysis and code. It is helpful to be familiar with tabular data analysis using statistical software, database tools, or spreadsheet programs. Workshop materials, including setup directions and slides are available at https://github.com/CornellCAC/r_for_edu/ The Rstudio Cloud project used in the workshop is https://rstudio.cloud/project/4044219.

• Containerization Explained

Containerization is a software development method in which applications are packaged into standard units for development, shipment, and deployment.

• 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.

• Educause HEISC-800-171 Community Group

The purpose of this group is to provide a forum to discuss NIST 800-171 compliance. Participants are encouraged to collaborate and share effective practices and resources that help higher education institutions prepare for and comply with the NIST 800-171 standard as it relates to Federal Student Aid (FSA), CMMC, DFARS, NIH, and NSF activities.

• unix list of basic commands

Unix is incredibly common and useful. This website provides all the common commands and explanations for one to get started with a unix system.

• GPU Acceleration in Python

This tutorial explains how to use Python for GPU acceleration with libraries like CuPy, PyOpenCL, and PyCUDA. It shows how these libraries can speed up tasks like array operations and matrix multiplication by using the GPU. Examples include replacing NumPy with CuPy for large datasets and using PyOpenCL or PyCUDA for more control with custom GPU kernels. It focuses on practical steps to integrate GPU acceleration into Python programs.

• 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.

• Markov Decision Processes in Julia

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.

• Python Handbook Series

A lecture and notes with the goal of teaching introductory python. Starting by understanding how to download and start using python, then expanding to basic syntax for lists, arrays, loops, and methods.

• Map Projections

In GIS, projections are helpful to take something plotted on a globe and convert it to a flat map that we can print or show on a screen. Unfortunately it also introduces distortions to the objects and features on the map. This not only distorts the objects visually, but the results for any spatial attribute calculations will also reflect this distortion (such as distance and area ). Below is a link to a quick primer on projections, types of distortions that can occur, and suggestions on how to choose a correct projection for your work.

• https://www.htslib.org/doc/

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.

• Big Data Research at the University of Colorado Boulder

Background: Big data, defined as having high volume, complexity or velocity, have the potential to greatly accelerate research discovery. Such data can be challenging to work with and require research support and training to address technical and ethical challenges surrounding big data collection, analysis, and publication. Methods: The present study was conducted via a series of semi-structured interviews to assess big data methodologies employed by CU Boulder researchers across a broad sample of disciplines, with the goal of illuminating how they conduct their research; identifying challenges and needs; and providing recommendations for addressing them. Findings: Key results and conclusions from the study indicate: gaps in awareness of existing big data services provided by CU Boulder; open questions surrounding big data ethics, security and privacy issues; a need for clarity on how to attribute credit for big data research; and a preference for a variety of training options to support big data research.

• Charliecloud User Group

Announcements for for users and developers of Charliecloud, which provides lightweight user-defined software stacks for high-performance computing.

• Framework to help in scaling Machine Learning/Deep Learning/AI/NLP Models to Web Application level

This framework will help in scaling Machine Learning/Deep Learning/Artificial Intelligence/Natural Language Processing Models to Web Application level almost without any time.