AI Institutes Cyberinfrastructure Documents: SAIL Meeting
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Materials from the SAIL meeting (https://aiinstitutes.org/2023/06/21/sail-2023-summit-for-ai-leadership/). A space where AI researchers can learn about using ACCESS resources for AI applications and research.
A survey on datasets for fairness-aware machine learning
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The research paper provides an overview of various datasets that have been used to study fairness in machine learning. It discusses the characteristics of these datasets, such as their size, diversity, and the fairness-related challenges they address. The paper also examines the different domains and applications covered by these datasets.
OpenMP Tutorial
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OpenMP (Open Multi-Processing) is an API that supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran on many platforms, instruction-set architectures and operating systems, including Solaris, AIX, FreeBSD, HP-UX, Linux, macOS, and Windows. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior.
Jetstream Home
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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.
Introduction to MP
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Open Multi-Processing, is an API designed to simplify the integration of parallelism in software development, particularly for applications running on multi-core processors and shared-memory systems. It is an important resource as it goes over what openMP and ways to work with it. It is especially important because it provides a straightforward way to express parallelism in code through pragma directives, making it easier to create parallel regions, parallelize loops, and define critical sections. The key benefit of OpenMP lies in its ease of use, automatic thread management, and portability across various compilers and platforms. For app development, especially in the context of mobile or desktop applications, OpenMP can enhance performance by leveraging the capabilities of modern multi-core processors. By parallelizing computationally intensive tasks, such as image processing, data analysis, or simulations, apps can run faster and more efficiently, providing a smoother user experience and taking full advantage of the available hardware resources. OpenMP's scalability allows apps to adapt to different hardware configurations, making it a valuable tool for developers aiming to optimize their software for a range of devices and platforms.
Git Branching Workflow and Maneuvers
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A couple of resources that:
1.) Presents and defends a git branching workflow for stable collaborative git based projects. ("A Successful Git Branching Model")
2.) Maps "What do you want to do?" to the commands necessary to accomplish it. ("Git Flight Rules")
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.
High performance computing 101
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An introductory guide to High Performance Computing.
GPU Computing Workshop Series for the Earth Science Community
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GPU training series for scientists, software engineers, and students, with emphasis on Earth science applications.
The content of this course is coordinated with the 6 month series of GPU Training sessions starting in Februrary 2022. The NVIDIA High Performance Computing Software Development Kit (NVHPC SDK) and CUDA Toolkit will be the primary software requirements for this training which will be already available on NCAR's HPC clusters as modules you may load. This software is free to download from NVIDIA by navigating to the NVHPC SDK Current Release Downloads page and the CUDA Toolkit downloads page. Any provided code is written specifically to build and run on NCAR's Casper HPC system but may be adapted to other systems or personal machines. Material will be updated as appropriate for the future deployment of NCAR's Derecho cluster and as technology progresses.
ACCESS Support Portal
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Contributing cycles to the Open Science Grid
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What are LSTMs?
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This reading will explain what a long short-term memory neural network is. LSTMs are a type of neural networks that rely on both past and present data to make decisions about future data. It relies on loops back to previous data to make such decisions. This makes LSTMs very good for predicting time-dependent behavior.
Metadata Systems
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Metadata is a vital topic in libraries and librarianship, encompassing structured information used for accessing digital resources. The definition of metadata varies but is essentially data about data. It has evolved beyond simply describing metadata schemas and now focuses on topics like interoperability, non-descriptive metadata (administrative and preservation metadata), and the effective application of metadata schemas for user discovery. Interoperability, the ability to seamlessly exchange metadata between systems, is a major concern. Different levels of interoperability are examined, including schema-level, record-level, and repository-level. Challenges to interoperability include variations in standards, collaboration barriers, and costs.Metadata management is discussed in terms of the holistic management of metadata across an entire library. Steps include analyzing metadata requirements, adopting schema, creating metadata content, delivery/access, evaluation, and maintenance. Administrative metadata, which encompasses ownership and production information, is becoming more critical, particularly for electronic resource licensing. Preservation metadata is also gaining importance in ensuring the long-term viability of digital objects.
Rockfish at Johns Hopkins University
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Resources and User Guide available at Rockfish
QGIS Processing Executor
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Running QGIS tools from the command line
Horovod: Distributed deep learning training framework
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Horovod is a distributed deep learning training framework. Using horovod, a single-GPU training script can be scaled to train across many GPUs in parallel. The library supports popular deep learning framework such as TensorFlow, Keras, PyTorch, and Apache MXNet.
Docker Tutorial for Beginners
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A Docker tutorial for beginners is a course that teaches the basics of Docker, a containerization platform that allows you to package your application and its dependencies into a standardized unit for development, shipment, and deployment.
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.
Linux Tutorial from Ryan's Tutorials
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The following pages are intended to give you a solid foundation in how to use the terminal, to get the computer to do useful work for you. You won't be a Unix guru at the end but you will be well on your way and armed with the right knowledge and skills to get you there if that's what you want (which you should because that will make you even more awesome). Here you will learn the Linux command line (Bash) with our 13 part beginners tutorial. It contains clear descriptions, command outlines, examples, shortcuts and best practice. At first, the Linux command line may seem daunting, complex and scary. It is actually quite simple and intuitive (once you understand what is going on that is), and once you work through the following sections you will understand what is going on. Unix likes to take the approach of giving you a set of building blocks and then letting you put them together. This allows us to build things to suit our needs. With a bit of creativity and logical thinking, mixed in with an appreciation of how the blocks work, we can assemble tools to do virtually anything we want. The aim is to be lazy. Why should we do anything we can get the computer to do for us? The only reason I can think of is that you don't know how (but after working through these pages you will know how, so then there won't be a good reason). A question that may have crossed your mind is "Why should I bother learning the command line? The Graphical User Interface is much easier and I can already do most of what I need there." To a certain extent you would be right, and by no means am I suggesting you should ditch the GUI. Some tasks are best suited to a GUI, word processing and video editing are great examples. At the same time, some tasks are more suited to the command line, data manipulation (reporting) and file management are some good examples. Some tasks will be just as easy in either environment. Think of the command line as another tool you can add to your belt. As always, pick the best tool for the job.
MDAnalysis - Python library for the analysis of molecular dynamics simulations
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MDAnalysis is a python based library of tools for the analysis of molecular dynamics simulations. It is able to read and write many popular simulation formats including CHARMM, LAMMPS, GROMACS, and AMBER and more. This link contains the documentation pages of all MDAnalysis functions and has links to tutorials using Jupyter Notebooks.
Python
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Python course offered by Texas A&M HPRC
Astronomy data analysis with astropy
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Astropy is a community-driven package that offers core functionalities needed for astrophysical computations and data analysis. From coordinate transformations to time and date handling, unit conversions, and cosmological calculations, Astropy ensures that astronomers can focus on their research without getting bogged down by the intricacies of programming. This guide walks you through practical usage of astropy from CCD data reduction to computing galactic orbits of stars.
Trusted CI Resources Page
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Very helpful list of external resources from Trusted CI
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.
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.