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Knowledge Base Resources

These resources are contributed by researchers, facilitators, engineers, and HPC admins. Please upvote resources you find useful!
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Topics

  • machine-learning (50)
  • ai (43)
  • training (40)
  • data-analysis (39)
  • deep-learning (28)
  • documentation (28)
  • big-data (26)
  • neural-networks (24)
  • workforce-development (20)
  • visualization (18)
  • professional-development (17)
  • parallelization (16)
  • programming (14)
  • community-outreach (13)
  • image-processing (13)
  • cybersecurity (12)
  • gpu (12)
  • r (12)
  • pytorch (11)
  • slurm (10)
  • c (9)
  • cloud-computing (9)
  • compiling (9)
  • mpi (9)
  • plotting (9)
  • administering-hpc (8)

Programming Language

  • python (48)
  • c++ (15)
  • bash (8)

Science Domain

  • data-science (16)

Skill Level

  • intermediate (191)
  • beginner (189)
  • advanced (111)

Content Type

  • learning (91)
  • website (64)
  • documentation (57)
  • tool (34)
  • video_link (19)
  • presentation (7)
  • mailing_list (2)
  • video (1)
Practical Machine Learning with Python
0
  • 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.
machine-learningprogrammingpython
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Type
video_link
Level
Advanced
ACCESS Campus Champion Example Allocation
0
  • GitHub link to LaTeX source file and compiled PDF
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.
allocations-proposalproposal-requestresearch-facilitation
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Type
learning
Level
Beginner
Discover Data Science
0
  • Discover Data Science
Discover Data Science is all about making connections between prospective students and educational opportunities in an exciting new, hot, and growing field – data science.
data-analysisworkforce-development
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Type
website
Level
Beginner
MATLAB with other Programming Languages
0
  • 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.
cc++fortranjavamatlabpython
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Type
tool
Level
Beginner, Intermediate, Advanced
Paraview UArizona HPC links (advanced)
0
  • Getting started with the paraview terminal
  • Batch headless rendering with Paraview
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.
visualization
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Type
documentation
Level
Intermediate, Advanced
Ultimate guide to Unix
0
  • 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.
bash
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Type
website
Level
Beginner
iOS CoreML + SwiftUI Image Classification Model
0
  • Document Tutorial
This tutorial will teach step-by-step how to create an image classification model using Core ML in XCode and integrate it into an iOS app that will use the user's iPhone camera to scan objects and predict based on the image classification model.
aimachine-learning
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Type
documentation
Level
Beginner
Implementing Markov Processes with Julia
0
  • 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.
aimachine-learningjulia
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Type
tool
Level
Intermediate, Advanced
Vulkan Support Survey across Systems
0
  • Vulkan Support Survey across Systems
  • OSF article link (easier to read)
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.
anvilmatlabdarwinexpansexsedec++
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Type
documentation
Level
Beginner, Intermediate
Samtools Documentation
0
  • 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.
documentationdata-analysisbioinformaticsdata-sciencegenomics
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Type
documentation
Level
Beginner, Intermediate, Advanced
Introduction to Linux CLI for Researchers
0
  • Intro Linux Tutorial for researchers
The goal of this video is to help researchers and students recently given allocations to High Performance Compute resources a basic introduction to Linux commands to help them get started. These are a few of the most fundamental commands for navigating and getting started. If you find this video helpful or would like me to continue this series let me know!
bashsshresearch-facilitationtraining
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Type
learning
Level
Beginner
Charliecloud User Group
0
  • Charliecloud User Group
Announcements for for users and developers of Charliecloud, which provides lightweight user-defined software stacks for high-performance computing.
containers
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Type
mailing_list
Level
Beginner
Jetstream2 Docs Site
0
  • 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.
jetstream
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Type
documentation
Level
Beginner, Intermediate, Advanced
QGIS Processing Executor
0
  • QGIS processing from the command line
Running QGIS tools from the command line
gis
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Type
documentation
Level
Intermediate
TensorFlow for Deep Neural Networks
0
  • TensorFlow Docs
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.
documentationfastertensorflow
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Type
tool
Level
Intermediate, Advanced
Working with Python on HPC Clusters
0
  • Working with Python on HPC Clusters
This tutorial series and documentation covers topics on using Python on HPC clusters. The specific steps are based on the HOPPER cluster at George Mason University in Fairfax, VA. They should be implementable on most HPC clusters that have the SLURM scheduler installed, the Environment Modules system for managing packages and Open onDemand for a web-based GUI to access the cluster resources.
pytorchbatch-jobsjob-submissionschedulingslurmmodulesscriptingcondapython
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Type
documentation
Level
Beginner, Intermediate
Installing Rocky Linux Operating System
0
  • Installing Rocky Linux 9
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.
unix-environmentsoftware-installation
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Type
learning
Level
Beginner
phenoACCESS-24 workshop program materials
0
  • phenoACCESS-24: Workshop on Research Computing and Plant Phenotyping
phenoACCESS-24: Workshop on Research Computing and Plant Phenotyping High-throughput plant phenotyping is computationally intensive, requiring data storage, data processing and analysis, research computing expertise, and mechanisms for data sharing. This workshop is aimed at research computing workforce development by addressing questions such as what is plant phenotyping; what types of data are collected; what are the preprocessing and analytical needs; what tools and platforms exist for data capture, management, analysis, and storage; and how best to collaborate and engage with phenotyping researchers. The full-day agenda will include speakers (scientists and research compute staff); panel discussions (how to work with research computing staff and facilities; how to engage with phenotyping scientists), and networking opportunities (meet-and-greet, ice breakers, small group discussions). The videos and slide decks for the talks are included on the linked page.
big-datadata-managementmetadatabiologyprofessional-developmentworkforce-development
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Type
website
Level
Intermediate
Bridges-2 Home Page
0
  • Bridges 2 Home Page
Landing Page for Bridges-2 information
matlab
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Type
website
Level
Beginner, Intermediate, Advanced
Data Imputation Methods for Climate Data and Mortality Data
0
  • Data Imputation Methods for Climate Data and Mortality Data - Slices
  • Github repository
  • Data Imputation Methods for Climate Data and Mortality Data - Full Tutorial
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.
allocation-valuedocumentationaiplottingvisualizationdata-analysismachine-learning
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Type
video_link
Level
Intermediate, Advanced
Trusted CI
0
  • Trusted CI
The mission of Trusted CI is to lead in the development of an NSF Cybersecurity Ecosystem with the workforce, knowledge, processes, and cyberinfrastructure that enables trustworthy science and NSF’s vision of a nation that is a global leader in research and innovation.
cybersecuritytraining
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Type
website
Level
Beginner, Intermediate, Advanced
What is fairness in ML?
0
  • Building ML models for everyone: understanding fairness in machine learning
This article discusses the importance of fairness in machine learning and provides insights into how Google approaches fairness in their ML models. The article covers several key topics: Introduction to fairness in ML: It provides an overview of why fairness is essential in machine learning systems, the potential biases that can arise, and the impact of biased models on different communities. Defining fairness: The article discusses various definitions of fairness, including individual fairness, group fairness, and disparate impact. It explains the challenges in achieving fairness due to trade-offs and the need for thoughtful considerations. Addressing bias in training data: It explores how biases can be present in training data and offers strategies to identify and mitigate these biases. Techniques like data preprocessing, data augmentation, and synthetic data generation are discussed. Fairness in ML algorithms: The article examines the potential biases that can arise from different machine learning algorithms, such as classification and recommendation systems. It highlights the importance of evaluating and monitoring models for fairness throughout their lifecycle. Fairness tools and resources: It showcases various tools and resources available to practitioners and developers to help measure, understand, and mitigate bias in machine learning models. Google's TensorFlow Extended (TFX) and What-If Tool are mentioned as examples. Google's approach to fairness: The article highlights Google's commitment to fairness and the steps they take to address fairness challenges in their ML models. It mentions the use of fairness indicators, ongoing research, and partnerships to advance fairness in AI. Overall, the article provides a comprehensive overview of fairness in machine learning and offers insights into Google's approach to building fair ML models.
aivisualizationdata-analysisdeep-learningmachine-learning
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Type
documentation
Level
Intermediate
Mechanism and Implementation of Various MPI Libraries
0
  • Tutorial for MPI Working Mechanism and Detailed Implementation
  • A Simple Running Case of Open MPI on clusters
There is a detailed explanation about communication routines and managing methods of different MPI libraries, as well as several exercises designed for users to get familiar with the implementation of MPI build process.
compilingmpi
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Type
website
Level
Beginner
Weka
0
  • Weka Homepage
  • Weka Data Mining Tutorials
Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.
big-datadata-analysismachine-learningwekadata-sciencejava
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Type
tool
Level
Intermediate, Advanced

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