Knowledge Base Resources

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.

**Cursor: The AI-Powered Code Editor** Cursor is a cutting-edge, AI-first code editor designed to revolutionize the way developers write, debug, and understand code. Built upon the premise of pair-programming with artificial intelligence, Cursor harnesses the capabilities of advanced AI models to offer real-time coding assistance, bug detection, and code generation. **How Cursor Benefits High-Performance Computing (HPC) Work:** 1. **Efficient Code Development:** With AI-assisted code generation, researchers and developers in the HPC realm can quickly write optimized code for simulations, data processing, or modeling tasks, reducing the time to deployment. 2. **Debugging Assistance:** Handling complex datasets and simulations often lead to intricate bugs. Cursor's capability to automatically investigate errors and determine root causes can save crucial time in the HPC workflow. 3. **Tailored Code Suggestions:** Cursor's AI provides context-specific code suggestions by understanding the entire codebase. For HPC applications where performance is paramount, this means receiving recommendations that align with optimization goals. 4. **Improved Code Quality:** With AI-driven bug scanning and linter checks, Cursor ensures that HPC codes are not only fast but also robust and free of common errors. 5. **Easy Integration:** Being a fork of VSCode, Cursor allows seamless migration, ensuring that developers working in HPC can swiftly integrate their existing VSCode setups and extensions. In essence, for HPC tasks that demand speed, precision, and robustness, Cursor acts as an invaluable co-pilot, guiding developers towards efficient and optimized coding solutions. It is free if you provide your own OPEN AI API KEY.

Links to MD tutorials for beginner's across various simulation platforms.

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.

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.

In GIS, projections are helpful to take something plotted on a globe and convert it to a flat map that we can print or show on a screen. Unfortunately it also introduces distortions to the objects and features on the map. This not only distorts the objects visually, but the results for any spatial attribute calculations will also reflect this distortion (such as distance and area ). Below is a link to a quick primer on projections, types of distortions that can occur, and suggestions on how to choose a correct projection for your work.

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.

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.

The purpose of this group is to provide a forum to discuss NIST 800-171 compliance. Participants are encouraged to collaborate and share effective practices and resources that help higher education institutions prepare for and comply with the NIST 800-171 standard as it relates to Federal Student Aid (FSA), CMMC, DFARS, NIH, and NSF activities.

"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.

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.

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.

Self-paced tutorials on high-end computing topics such as parallel computing, multi-core performance, and performance tools. Some of the tutorials also offer digital badges.

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.

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.

This framework will help in scaling Machine Learning/Deep Learning/Artificial Intelligence/Natural Language Processing Models to Web Application level almost without any time.

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!

This website summarizes the notes of Stanford's introductory course on probabilistic graphical models. It starts from the very basics and concludes by explaining from first principles the variational auto-encoder, an important probabilistic model that is also one of the most influential recent results in deep learning.

Landing Page for Bridges-2 information

Bioinformatics Toolbox provides algorithms and apps for Next Generation Sequencing (NGS), microarray analysis, mass spectrometry, and gene ontology. Using toolbox functions, you can read genomic and proteomic data from standard file formats such as SAM, FASTA, CEL, and CDF, as well as from online databases such as the NCBI Gene Expression Omnibus and GenBank.

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.