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.

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!

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.

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.

Mathematical optimization deals with the problem of finding numerically minimums or maximums of a functions. This tutorial provides the Python solutions for the optimization problems with examples.

Through collaboration and networking, WHPC strives to bring together women in HPC and technical computing while encouraging women to engage in outreach activities and improve the visibility of inspirational role models.

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.

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.

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

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

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

ACCESS requests proposals to be written following NSF proposal guidelines. The link provides an example of an ACCESS proposal using an NSF LaTeX template. The request is at the DISCOVER level appropriate for Campus Champions. The file is 2 pages: the first page details the motivation, approach, and resources requested; and the second page is a 1-page bio.

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.

Discover Data Science is all about making connections between prospective students and educational opportunities in an exciting new, hot, and growing field – data science.

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.

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.

These links take you to visualization resources supported by the University of Arizona's HPC visualization consultant ([rtdatavis.github.io](http://rtdatavis.github.io/)). The following links are specific to the Paraview program and the workflows that have been used my researchers at the U of Arizona. These links are distinct from the others posted in the beginner paraview access ci links from the University of Arizona in that they are for more complex workflows. The links included explain how to use the terminal with paraview (pvpython), and the steps to leverage HPC resources for headless batch rendering. The batch rendering tutorial is significantly more complex than the others so if you find yourself stuck please post on the https://ask.cyberinfrastructure.org/ and I will try to troubleshoot with you.

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.

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.

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.

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.

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