Useful R Packages for Data Science and Statistics
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This Udacity article listed the most frequently used R packages for data science and statistics. For each package, the article provided the link to its official documentation. It will be a great start point if you want to start your data science journey in R.
DARWIN Documentation Pages
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DARWIN (Delaware Advanced Research Workforce and Innovation Network) is a big data and high performance computing system designed to catalyze Delaware research and education
PyTorch for Deep Learning and Natural Language Processing
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PyTorch is a Python library that supports accelerated GPU processing for Machine Learning and Deep Learning. In this tutorial, I will teach the basics of PyTorch from scratch. I will then explore how to use it for some ML projects such as Neural Networks, Multi-layer perceptrons (MLPs), Sentiment analysis with RNN, and Image Classification with CNN.
Enhanced Sampling for MD simulations
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GIS: Geocoding Services
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Geocoding is the process of taking a street address and converting it into coordinates that can be plotted on a map. This conversion typically requires an API call to a remote server hosted by an organization/institution. The remote server will take the address attributes provided by you and the remote server will compare it to the data it contains and return a best estimate on the coordinates for that location.
There are many geocoding services available with different world coverages, quality of result, and set different rate limits for access. For R, a package called "tidygeocoder" provides an easy way to connect to these different services. As an additional benefit, their documentation provides a good summary of geocoding services available and links to their documentation. The link to the documentation for gecoding services accessible by "tidygeocoder" is provided below.
For Python, geopy package is a library that provides connection to various geocoding services. The link to the documentation for this package is also included below.
Data Visualization tools for Python
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Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It makes analyzing and presenting your data extremely easy and works with Python which many people already know.
ACCESS Pegasus Documentation
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The documentation provides an overview of using Pegasus, a workflow management system, on ACCESS resources for high throughput computing (HTC) workloads, covering logging in, workflow creation, resource configuration, and monitoring options.
Managing Python Packages on an HPC Cluster
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This workshop will go into the different ways python packages can be managed in a cluster environment using conda and python virtual environments both in batch mode from the command line and with Jupyter Notebooks and Jupyter Lab on the cluster. The examples will be run on the GMU HOPPER Cluster.
Introduction to Python for Digital Humanities and Computational Research
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This documentation contains introductory material on Python Programming for Digital Humanities and Computational Research. This can be a go-to material for a beginner trying to learn Python programming and for anyone wanting a Python refresher.
MATLAB bioinformatics toolbox
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Bioinformatics Toolbox provides algorithms and apps for Next Generation Sequencing (NGS), microarray analysis, mass spectrometry, and gene ontology. Using toolbox functions, you can read genomic and proteomic data from standard file formats such as SAM, FASTA, CEL, and CDF, as well as from online databases such as the NCBI Gene Expression Omnibus and GenBank.
Paraview UArizona HPC links (advanced)
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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.
Weka
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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.
Singularity/Apptainer User Manuals
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Singularity/Apptainer is a free and open-source container platform that allows users to build and run containers on high performance computing resources.
SingularityCE is the community edition of Singularity maintained by Sylabs, a company that also offers commercial Singularity products and services.
Apptainer is a fork of Singularity, maintained by the Linux foundation, a community of developers and users who are passionate about open source software.
What is fairness in ML?
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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.
Neural Networks in Julia
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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.
ACCESS KB Guide - Expanse
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Expanse at SDSC is a cluster designed by Dell and SDSC delivering 5.16 peak petaflops, and offers Composable Systems and Cloud Bursting.
Numba: Compiler for Python
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Numba is a Python compiler designed for accelerating numerical and array operations, enabling users to enhance their application's performance by writing high-performance functions in Python itself. It utilizes LLVM to transform pure Python code into optimized machine code, achieving speeds comparable to languages like C, C++, and Fortran. Noteworthy features include dynamic code generation during import or runtime, support for both CPU and GPU hardware, and seamless integration with the Python scientific software ecosystem, particularly Numpy.
DAGMan for orchestrating complex workflows on HTC resources (High Throughput Computing)
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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.
Docker - Containerized, reproducible workflows
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Docker allows for containerization of any task - basically a smaller, scalable version of a virtual machine. This is very useful when transferring work across computing environments, as it ensures reproducibility.
Running Particle-in-Cell Simulations on HPC
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WarpX is an advanced particle-in-cell code used to model particle accelerators, which needs to be run on HPC. This website contains the tutorial on how to build WarpX on various HPC systems such as NERSC along with examples on how to set up post-processing/visualization tools for different physics cases.
Vulkan Support Survey across Systems
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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.
Guide to building AirSim on Linux machines
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This article provides step-by-step instructions on how to build AirSim, a simulator for autonomous vehicles, on Linux. It includes both Docker and host machine setup options, along with details on building Unreal Engine, AirSim, and the Unreal environment. It also provides guidance on how to use AirSim once it is set up.
MOPAC
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MOPAC (Molecular Orbital PACkage) is a semi-empirical quantum chemistry package used to compute molecular properties and structures by using approximations of the Schrödinger equation. This tutorial explains the process of using MOPAC for different forms of calculations.
ACCESS Guide (originally given at Duke OIT)
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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.