• Plumed
• WESTPA

Tools and plugins to enhance molecular dynamics sampling

• Horovod

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.

• Scikit-learn

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.

• Gaussian 16 HomePage
• About Gaussian 16

Gaussian 16 is a computational chemistry package that is used in predicting molecular properties and understanding molecular behavior at a quantum mechanical level.

• MDAnalysis

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.

• DELTA Youtube Video

Introductory video about DELTA. Speaker Tim Boerner, Senior Assistant Director, NCSA

• https://www.mathworks.com/products/bioinfo.html

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.

• Weka Homepage
• Weka Data Mining Tutorials

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.

• Neural Networks in Julia using Flux.jl

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.

• DAGMan
• DAGMan Workflows

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.

• Docker Documentation

Docker allows for containerization of any task - basically a smaller, scalable version of a virtual machine. This is very useful when transferring work across computing environments, as it ensures reproducibility.

• Examples of I/O Files for Mopac

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.

• HigherEdCUI Slack Channel

Slack channel for the Higher Ed CUI community

• marimo | a next generation python notebook

Introduction seminar for new reactive python notebook from marimo ambassador.

• TUPA - Electric field analyses for molecular simulations

Tool to compute electric fields from molecular simulations

• Beautiful Soup Docs

This package lets you easily scrape websites and extract information based on html tags and various other metadata found in the page. It can be useful for large-scale web analysis and other tasks requiring automated data gathering.

• Markov Decision Processes in Julia

The following link provides an easy method of implementing Markov Decision Processes (MDP) in the Julia computing language. MDPs are a class of algorithms designed to handle stochastic situations where the actor has some level of control. For example, used at a low level, MDPs can be used to control an inverted pendulum, but applied in higher level decision making the can also decide when to take evasive action in air traffic management. MDPs can also be extended to the partially observable domain to form the Partially Observable Markov Decision Process (POMDP). This link contains a wealth of information to show one can easily implement basic POMDP and MDP algorithms and apply well known online and offline solvers.

• PyFR documentation
• PyFR source code from Github
• Discourse channel for discussions and help

What is PyFR and how does it solve fluid flow problems? PyFR is an open-source Computational Fluid Dynamics (CFD) solver that is based on Python and employs the high-order Flux Reconstruction technique. It effectively solves fluid flow problems by utilizing streaming architectures, making it suitable for complex fluid dynamics simulations. How does PyFR achieve scalability on clusters with CPUs and GPUs? PyFR achieves scalability by leveraging distributed memory parallelism through the Message Passing Interface (MPI). It implements persistent, non-blocking MPI requests using point-to-point (P2P) communication and organizes kernel calls to enable local computations while exchanging ghost states. This design approach allows PyFR to efficiently operate on clusters with heterogeneous architectures, combining CPUs and GPUs. Why is PyFR valuable for benchmarking clusters? PyFR's exceptional performance has been recognized by its selection as a finalist in the ACM Gordon Bell Prize for High-Performance Computing. It demonstrates strong-scaling capabilities by effectively utilizing low-latency inter-GPU communication and achieving strong-scaling on unstructured grids. PyFR has been successfully benchmarked with up to 18,000 NVIDIA K20X GPUs on Titan, showcasing its efficiency in handling large-scale simulations.

• NumPY Docs

Numpy is a python package that leverages types and compiled C code to make many math operations in Python efficient. It is especially useful for matrix manipulation and operations.

• Conda Tutorial

Conda is a popular package management system. This tutorial introduces you to Conda and walks you through managing Python, your environment, and packages.

• https://github.com/albumentations-team/albumentations

Data augmentation is a crucial step in the pipeline for image classification with deep learning. Albumentations is an extremely versatile Python library that can be used to easily augment images. Transformations include rotations, flips, downscaling, distortions, blurs, and many more. Citation: Buslaev A, Iglovikov VI, Khvedchenya E, Parinov A, Druzhinin M, Kalinin AA. Albumentations: Fast and Flexible Image Augmentations. Information. 2020; 11(2):125. https://doi.org/10.3390/info11020125

• The Plank Center Mentorship Guide

Backed by collegiate white papers, top industry professionals, and researchers, The Plank Center’s Mentorship Guide offers basic tips and tricks on how to get the most out of a mentorship relationship. This easy-to-follow guide supplements mentorship programs, lesson plans, and professional relationships.

• Docker Container Library

The Docker container library, commonly known as Docker Hub, is a vast repository that hosts a multitude of pre-configured container images, streamlining the deployment process. It can drastically speed up a workflow, and gives you a consistent starting point each time. Check it out, they might have exactly what you are looking for!

• Building Anaconda Navigator applications

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.

• Install Linux on Windows with WSL

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