Harnessing the Power of Cloud and Machine Learning for Climate and Ocean Advances
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Documentation and presentation on how to use machine learning and deep learning framework using TensorFlow, Keras and sci-kit learn for Climate and Ocean Advances
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.
ACCESS KB Guide - Anvil
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Purdue University is the home of Anvil, a powerful supercomputer that provides advanced computing capabilities to support a wide range of computational and data-intensive research spanning from traditional high-performance computing to modern artificial intelligence applications.
Trusted CI
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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.
Machine Learning in R online book
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The free online book for the mlr3 machine learning framework for R. Gives a comprehensive overview of the package and ecosystem, suitable from beginners to experts. You'll learn how to build and evaluate machine learning models, build complex machine learning pipelines, tune their performance automatically, and explain how machine learning models arrive at their predictions.
High performance computing 101
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An introductory guide to High Performance Computing.
An Introduction to the Julia Programming Language
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The Julia Programming Language is one of the fastest growing software languages for AI/ML development. It writes in manner that's similar to Python while being nearly as fast as C++, while being open source, and reproducible across platforms and environments. The following link provide an introduction to using Julia including the basic syntax, data structures, key functions, and a few key packages.
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.
Neurostars
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A question and answer forum for neuroscience researchers, infrastructure providers and software developers.
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.
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.
Oakridge Leadership Computing Facility (OLCF) Training Events and Archive
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Upcoming training events and archives of training materials detailing general HPC best practices as well as how to use OLCF resources and services.
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.
Campus Research Computing Consortium (CaRCC)
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CaRCC – the Campus Research Computing Consortium – is an organization of dedicated professionals developing, advocating for, and advancing campus research computing and data and associated professions.
Vision: CaRCC advances the frontiers of research by improving the effectiveness of research computing and data (RCD) professionals, including their career development and visibility, and their ability to deliver services and resources for researchers. CaRCC connects RCD professionals and organizations around common objectives to increase knowledge sharing and enable continuous innovation in research computing and data capabilities.
Discover Data Science
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Discover Data Science is all about making connections between prospective students and educational opportunities in an exciting new, hot, and growing field – data science.
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.
CUDA Toolkit Documentation
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NVIDIA CUDA Toolkit Documentation: If you are working with GPUs in HPC, the NVIDIA CUDA Toolkit is essential. You can access the CUDA Toolkit documentation, including programming guides and API references, at this provided website
Research Software Engineering Training Materials
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An ongoing collection of RSE training material, workshops, and resources. We are compiling this list as a starting point for future activities. We are especially seeking material that goes beyond basic research computing competency (e.g. what The Carpentries does so well) and is general enough to span multiple domains. Specific tools and technologies used only in one domain, or applicable to only one subset of computing (i.e. HPC) are typically too narrowly focused. When in doubt, submit it to be included or reach out and we’d be happy to discuss.
Active inference textbook
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This textbook is the first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines including computational neurosciences, machine learning, artificial intelligence, and robotics. It was published in 2022 and it's open access at this time. The contents in this textbook should be educational to those who want to understand how the free energy principle is applied to the normative behavior of living organisms and who want to widen their knowledge of sequential decision making under uncertainty.
Conda
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Conda is a popular package management system. This tutorial introduces you to Conda and walks you through managing Python, your environment, and packages.
Women in HPC
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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.
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.
Research Security Operations Center at IU
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The NSF-funded ResearchSOC helps make scientific computing resilient to cyberattacks and capable of supporting trustworthy, productive research through operational cybersecurity services, training, and information sharing necessary to a community as unique and variable as research and education (R&E).
ResearchSOC is a service offering from Indiana University's OmniSOC.
Scikit-Learn: Easy Machine Learning and Modeling
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Scikit-learn is free software machine learning library for Python. It has a variety of features you can use on data, from linear regression classifiers to xg-boost and random forests. It is very useful when you want to analyze small parts of data quickly.