The Carpentries
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We teach foundational coding and data science skills to researchers worldwide.
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.
Open OnDemand
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Open OnDemand is an easy-to-use web portal that lets students, researchers, and industry professionals use supercomputers from anywhere. It is installed on supercomputing resources at hundreds of sites. By eliminating the need for client software or command-line interface, Open OnDemand empowers users of all skill levels and significantly speeds up the time to their first computing.
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.
The Chronicle of Evidence-Based Mentoring
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This is a great mentoring resource and has many articles related to mentoring. It is a one-stop shop for mentoring, and at the bottom, there are tags based on topics, and interested users can pick and choose articles and resources on different types of mentorship.
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.
HPC Carpentry
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An HPC focused Carpentry community. Trainings include: HPC fundamentals, python, chapel, LAMMPS, parallelization with python, scaling studies, etc.
CaRCC Data Facing Track
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The Data-Facing Track of the People Network brings together people from research computing groups, libraries, research institutes, and other organizations who support data-enabled research. Many of us are also Researcher-Facing, but this track is an opportunity to discuss the varied challenges of working with data.
What is VPN? How It Works, Types of VPN
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A VPN, or Virtual Private Network, is a technology that creates a secure tunnel between your device and a VPN server. This tunnel encrypts all of your traffic, making it unreadable to anyone who tries to intercept it.
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.
Resource to active inference
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Active inference is an emerging study field in machine learning and computational neuroscience. This website in particular introduces "active inference institute", which has established a couple of years ago, and contains a wide variety of resources for understanding the theory of active inference and for participating a worldwide active inference community.
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.
Tutorial for OpenMP Building up and Utilization
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The following link elaborates the usage of OpenMP API and its related syntax. There are also several exercises available for learners to help them get familiar with this widely-used tool for multi-threaded realization.
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.
Jetstream2 Status
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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.
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.
Time-Series LSTMs Python Walkthrough
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A walkthrough (with a Google Colab link) on how to implement your own LSTM to observe time-dependent behavior.
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.
Bridges-2 Home Page
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Landing Page for Bridges-2 information