Knowledge Base Resources
These resources have been contributed and “vetted” by the community of cyberinfrastructure professionals (researchers, research computing facilitators, research software engineers and HPC system administrators) that are participating in programs such as this one, that are supported by the ConnectCI community management platform. Additional Knowledge Base Resources are always welcome!
ACCESS Resource Advisor
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A web-based tool to help researchers identify appropriate ACCESS resources for their project.
Regulated Research Community of Practice
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The daily news clearly shows the increasing threat to safety and privacy of data, personal as well as intellectual property. While the requirements such as DFARS 7012, HIPAA, and Cybersecurity Maturity Model Certification (CMMC) improve the consistency of data handling between agencies and contractors and grantees, it leaves academic institutions to figure out how to meet such requirements in a cost-effective way that fits the research and education mission of the institution. Most institutions, agencies, and companies act in isolation with one-off contract language to address data security and safeguarding concerns. Even though cybersecurity has a clear and uniform goal of protecting data, a onesize solution does not fit all academic institutions.
By supporting this community with development of a community strategic roadmap, regular discussions and workshops, and a repository of generalized and specific resources for handling regulated research programs RRCoP lowers the barrier to entry for institutions handling new regulations.
TensorFlow for Deep Neural Networks
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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.
Reinforcement Learning For Beginners with Python
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This course takes through the fundamentals required to get started with reinforcement learning with Python, OpenAI Gym and Stable Baselines. You'll be able to build deep learning powered agents to solve a varying number of RL problems including CartPole, Breakout and CarRacing as well as learning how to build your very own/custom environment!
Introduction to Parallel Computing Tutorial
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The tutorial is intended to provide a brief overview of the extensive and broad topic of Parallel Computing. It covers the basics of parallel computing, and is intended for someone who is just becoming acquainted with the subject .
Neurodesk
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Neurodesk provides a containerised data analysis environment to facilitate reproducible analysis of neuroimaging data. Analysis pipelines for neuroimaging data typically rely on specific versions of packages and software, and are dependent on their native operating system. These dependencies mean that a working analysis pipeline may fail or produce different results on a new computer, or even on the same computer after a software update. Neurodesk provides a platform in which anyone, anywhere, using any computer can reproduce your original research findings given the original data and analysis code.
Data Imputation Methods for Climate Data and Mortality Data
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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.
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.
Automated Machine Learning Book
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The authoritative book on automated machine learning, which allows practitioners without ML expertise to develop and deploy state-of-the-art machine learning approaches. Describes the background of techniques used in detail, along with tools that are available for free.
Installing Rocky Linux Operating System
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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.
NERSC Training and Tutorials
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A comprehensive collection of NERSC developed training and tutorial events, offered on regular schedules. All sessions are archived, including slide decks, video recordings, and software examples as are available. Some examples of past training and tutorial topics are listed below
Deep Learning for Sciences Webinar Series
BerkeleyGW Tutorial Workshop
VASP Trainings
Timemory Software Monitoring Tutorial, April 2021
HPCToolkit to Measure and Analyzing GPU Applications Performance Tutorial
Totalview Tutorial
NVidia HPCSDK - OpenMP Target Offload Training
Parallelware Training Series
ARM Debugging and Profiling Tools Tutorial
Roofline on NVIDIA GPUs
GPUs for Science events
3-part OpenACC Training Series
9-part CUDA Training Series
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.
Globus Documentation
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Globus is a data transfer, sharing, automation, and discovery service used by hundreds of thousands of researchers to manage "big data" at universities, research labs, and national systems such as ACCESS. The Globus documentation website provides how-to guides, reference documentation, and examples for Globus's web application, command-line interface, Python software development kit (SDK), and APIs.
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.
Slurm Scheduling Software Documentation
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Slurm is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for large and small Linux clusters. Slurm requires no kernel modifications for its operation and is relatively self-contained. As a cluster workload manager, Slurm has three key functions. First, it allocates exclusive and/or non-exclusive access to resources (compute nodes) to users for some duration of time so they can perform work. Second, it provides a framework for starting, executing, and monitoring work (normally a parallel job) on the set of allocated nodes. Finally, it arbitrates contention for resources by managing a queue of pending work.
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.
Introductory Tutorial to Numpy and Pandas for Data Analysis
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In this tutorial, I present an overview with many examples of the use of Numpy and Pandas for data analysis. Beginners in the field of data analysis can find It incredibly helpful, and at the same time, anyone who already has experience in data analysis and needs a refresher can find value in it. I discuss the use of Numpy for analyzing 1D and 2D multidimensional data and an introduction on using Pandas to manipulate CSV files.
Building Anaconda Navigator applications
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This tutorial explains how to create an Anaconda Navigator Application (app) for JupyterLab. It is intended for users of Windows, macOS, and Linux who want to generate an Anaconda Navigator app conda package from a given recipe. Prior knowledge of conda-build or conda recipes is recommended.
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.
Developer Stories Podcast
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As developers, we get excited to think about challenging problems. When you ask us what we are working on, our eyes light up like children in a candy store. So why is it that so many of our developer and software origin stories are not told? How did we get to where we are today, and what did we learn along the way? This podcast aims to look “Behind the Scenes of Tech’s Passion Projects and People.” We want to know your developer story, what you have built, and why. We are an inclusive community - whatever kind of institution or country you hail from, if you are passionate about software and technology you are welcome!
Python Tools for Data Science
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Python has become a very popular programming language and software ecosystem for work in Data Science, integrating support for data access, data processing, modeling, machine learning, and visualization. In this webinar, we will describe some of the key Python packages that have been developed to support that work, and highlight some of their capabilities. This webinar will also serve as an introduction and overview of topics addressed in two Cornell Virtual Workshop tutorials, available at https://cvw.cac.cornell.edu/pydatasci1 and https://cvw.cac.cornell.edu/pydatasci2
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 Events and Training
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Listing of upcoming ACCESS related events and training activities.
Solving differential equations with Physics-informed Neural Network
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Differential equations, the backbone of countless physical phenomena, have traditionally been solved using numerical methods or analytical techniques. However, the advent of deep learning introduces an intriguing alternative: Physics-Informed Neural Networks (PINNs). By leveraging the representational power of neural networks and integrating physical laws (like differential equations), PINNs offer a novel approach to solving complex problems. This guide walks through an implementation of a PINN to solve DEs such as the logistic equation.
Warewulf documentation
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Warewulf is an operating system provisioning platform for Linux that is designed to produce secure, scalable, turnkey cluster deployments that maintain flexibility and simplicity. It can be used to setup a stateless provisioning in HPC environment.