Knowledge Base Resources

This video will walk you through the process of efficiently utilizing and managing your ACCESS project(s). Here, you’ll find instructions on how to request resources, extend the end date of a project, renew a request, and all the other necessary tasks to successfully manage your project.

pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. It lets you store data in easy to manage and display data frames, with column names and datatypes.

These links take you to visualization resources supported by the University of Arizona's HPC visualization consultant (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. Some of the pages linked are very beginner friendly: getting started, working with cameras and keyframes for rendering, visualizing external files (netcdf climate data), graphs and data exporting. Many of the workflows involve using remote desktops via the Open On Demand interface, but if this isn't set up at your university you can use paraview locally on a desktop. Feel free to post on access ci https://ask.cyberinfrastructure.org/ if you need assistance getting a paraview gui open for your work on HPC.

This google colab notebook tutorial demonstrates how to create and train an lstm model in pytorch to be used to predict time series data. An airline passenger dataset is used as an example.

The Stan language is used to specify a (Bayesian) statistical model with an imperative program calculating the log probability density function. Here are some useful links to start your exploration of this statistical programming language, and a Python interface to Stan.

Training materials for using the bash (and zsh) shell.

The official documentation for PyTorch, a machine learning tensor-based framework, and NumPy, which allows for support for ndarrays which is useful to make tensors when implementing NNs. Both libraries can be installed with pip.

A tutorial on the effective use of Dask on HPC resources. The four-hour tutorial will be split into two sections, with early topics focused on novice Dask users and later topics focused on intermediate usage on HPC and associated best practices. The knowledge areas covered include (but are not limited to): Beginner section High-level collections including dask.array and dask.dataframe Distributed Dask clusters using HPC job schedulers Earth Science data analysis using Dask with Xarray Using the Dask dashboard to understand your computation Intermediate section Optimizing the number of workers and memory allocation Choosing appropriate chunk shapes and sizes for Dask collections Querying resource usage and debugging errors

"These notes are part of the UW Experimental College course on Introductory C Programming. They are based on notes prepared (beginning in Spring, 1995) to supplement the book The C Programming Language, by Brian Kernighan and Dennis Ritchie, or K&R as the book and its authors are affectionately known. (The second edition was published in 1988 by Prentice-Hall, ISBN 0-13-110362-8.) These notes are now (as of Winter, 1995-6) intended to be stand-alone, although the sections are still cross-referenced to those of K&R, for the reader who wants to pursue a more in-depth exposition." C is a low-level programming language that provides a deep understanding of how a computer's memory and hardware work. This knowledge can be valuable when optimizing apps for performance or when dealing with resource-constrained environments.C is often used as the foundation for creating cross-platform libraries and frameworks. Learning C can allow you to develop libraries that can be used across different platforms, including iOS, Android, and desktop environments.

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.

Rocky Mountain Advanced Computing Consortium Website

An AWS Tutorial for Beginners is a course that teaches the basics of Amazon Web Services (AWS), a cloud computing platform that offers a wide range of services, including compute, storage, networking, databases, analytics, machine learning, and artificial intelligence.

Multi-threading guidance when using GDAL.

This tutorial will teach step-by-step how to create an image classification model using Core ML in XCode and integrate it into an iOS app that will use the user's iPhone camera to scan objects and predict based on the image classification model.

This tutorial series and documentation covers topics on using Python on HPC clusters. The specific steps are based on the HOPPER cluster at George Mason University in Fairfax, VA. They should be implementable on most HPC clusters that have the SLURM scheduler installed, the Environment Modules system for managing packages and Open onDemand for a web-based GUI to access the cluster resources.

Pandas is one of the most essential Python libraries for data analysis and manipulation. It provides high-performance, easy-to-use data structures, and data analysis tools for the Python programming language. The official documentation serves as an in-depth guide to using this powerful tool including explanations and examples.

Documentation and research based on the latest NLP text generation detection methods for 2023.

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.

The goal of this video is to help researchers and students recently given allocations to High Performance Compute resources a basic introduction to Linux commands to help them get started. These are a few of the most fundamental commands for navigating and getting started. If you find this video helpful or would like me to continue this series let me know!

Raftlib is an open-source C++ Library that provides a framework for implementing parallel and concurrent data processing pipelines. It is designed to simplify the development of high-performance data processing applications by abstracting away the complexities of parallelism, concurrency, and data flow management. It enables stream/data-flow parallel computation by linking parallel compute kernels together using simple right shift operators, similar to C++ streams for string manipulation. RaftLib eliminates the need for explicit usage of traditional threading libraries such as pthreads, std::thread, or OpenMP, which can lead to non-deterministic behavior when misused.

Fastai offers many tools to people working with machine learning and artifical intelligence including tutorials on PyTorch in addition to their own library built on PyTorch, news articles, and other resources to dive into this realm.

Windows Subsystem for Linux (WSL) provides a Linux environment for Windows users to access HPC resources fast and efficiently.

phenoACCESS-24: Workshop on Research Computing and Plant Phenotyping High-throughput plant phenotyping is computationally intensive, requiring data storage, data processing and analysis, research computing expertise, and mechanisms for data sharing. This workshop is aimed at research computing workforce development by addressing questions such as what is plant phenotyping; what types of data are collected; what are the preprocessing and analytical needs; what tools and platforms exist for data capture, management, analysis, and storage; and how best to collaborate and engage with phenotyping researchers. The full-day agenda will include speakers (scientists and research compute staff); panel discussions (how to work with research computing staff and facilities; how to engage with phenotyping scientists), and networking opportunities (meet-and-greet, ice breakers, small group discussions). The videos and slide decks for the talks are included on the linked page.