Gaussian 16
0
Gaussian 16 is a computational chemistry package that is used in predicting molecular properties and understanding molecular behavior at a quantum mechanical level.
Header-only C++ JSON library
0
JSON is a lightweight format for storing and transporting data, for example in a config file. This library is header-only, and has easy-to-read documentation. It is a C++ library.
FSL Lectures
0
This is the official University of Oxford FSL group lecture page. This includes information on upcoming and past courses (online and in-person), as well as lecture materials. Available lecture materials includes slides and recordings on using FSL, MR physics, and applications of imaging data.
Horovod: Distributed deep learning training framework
0
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.
Awesome Jupyter Widgets (for building interactive scientific workflows or science gateway tools)
0
A curated list of awesome Jupyter widget packages and projects for building interactive visualizations for Python code
Astronomy data analysis with astropy
0
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.
Applications of Machine Learning in Engineering and Parameter Tuning Tutorial
0
Slides for a tutorial on Machine Learning applications in Engineering and parameter tuning given at the RMACC conference 2019.
Beautiful Soup - Simple Python Web Scraping
0
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.
AI Institutes Cyberinfrastructure Documents: SAIL Meeting
0
Materials from the SAIL meeting (https://aiinstitutes.org/2023/06/21/sail-2023-summit-for-ai-leadership/). A space where AI researchers can learn about using ACCESS resources for AI applications and research.
Harnessing the Power of Cloud and Machine Learning for Climate and Ocean Advances
0
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
Neural Networks in Julia
0
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.
Texas A&M HPRC Training Site
0
Training Resources and Courses offered by Texas A&M's Research Computing Group
File management of Visual Studio Code on clusters
0
Visual Studio Code, commonly known as VSCode, is a popular tool used by programmers worldwide. It serves as a text editor and an Integrated Development Environment (IDE) that supports a wide variety of programming languages. One of its key features is its extensive library of extensions. These extensions add on to the basic functionalities of VSCode, making coding more efficient and convenient.
However, there's a catch. When these extensions are installed and used frequently, they generate a multitude of files. These files are typically stored in a folder named .vscode-extension within your home directory. On a cluster computing facility such as the FASTER and Grace clusters at Texas A&M University, there's a limitation on how many files you can have in your home directory. For instance, the file number limit could be 10000, while the .vscode-extension directory can hold around 4000 temporary files even with just a few extensions. Thus, if the number of files in your home directory surpasses this limit due to VSCode extensions, you might face some issues. This restriction can discourage users from taking full advantage of the extensive features and extensions offered by the VSCode editor.
To overcome this, we can shift the .vscode-extension directory to the scratch space. The scratch space is another area in the cluster where you can store files and it usually has a much higher limit on the number of files compared to the home directory. We can perform this shift smoothly using a feature called symbolic links (or symlinks for short). Think of a symlink as a shortcut or a reference that points to another file or directory located somewhere else.
Here's a step-by-step guide on how to move the .vscode-extension directory to the scratch space and create a symbolic link to it in your home directory:
1. Copy the .vscode-extension directory to the scratch space: Using the cp command, you can copy the .vscode-extension directory (along with all its contents) to the scratch space. Here's how:
cp -r ~/.vscode-extension /scratch/user
Don't forget to replace /scratch/user with the actual path to your scratch directory.
2. Remove the original .vscode-extension directory: Once you've confirmed that the directory has been copied successfully to the scratch space, you can remove the original directory from your home space. You can do this using the rm command:
rm -r ~/.vscode-extension
It's important to make sure that the directory has been copied to the scratch space successfully before deleting the original.
3. Create a symbolic link in the home directory: Lastly, you'll create a symbolic link in your home directory that points to the .vscode-extension directory in the scratch space. You can do this as follows:
ln -s /scratch/user/.vscode-extension ~/.vscode-extension
By following this process, all the files generated by VSCode extensions will be stored in the scratch space. This prevents your home directory from exceeding its file limit. Now, when you access ~/.vscode-extension, the system will automatically redirect you to the directory in the scratch space, thanks to the symlink. This method ensures that you can use VSCode and its various extensions without worrying about hitting the file limit in your home directory.
NCSA HPC-Moodle
0
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.
TensorFlow for Deep Neural Networks
0
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.
Biopython Tutorial
0
The Biopython Tutorial and Cookbook website is a dedicated online resource for users in the field of computational biology and bioinformatics. It provides a collection of tutorials and practical examples focused on using the Biopython library.
The website offers a series of tutorials that cover various aspects of Biopython, catering to users with different levels of expertise. It also includes code snippets and examples, and common solutions to common challenges in computational biology.
Understanding LLM Fine-tuning
0
With the recent uprising of LLM's many business are looking at way to adopt these LLMs and fine-tuning these models on specfic data sets to ensure accuracy. These models when fine-tuned can be optimal for fulfilling the specific needs of a company. This site explains explicitly when, how, and why models should be trained. It goes over various strategies for LLM fine -tuning.
NERSC Training and Tutorials
0
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
MOPAC
0
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.
Thrust resources
0
Thrust is a CUDA library that optimizes parallelization on the GPU for you. The Thrust tutorial is great for beginners. The documentation is helpful for anyone using Thrust.
Why 'N How: Martinos Center for Biomedical Imaging:
0
The Why & How seminar series is designed to introduce research assistants, graduate students, and postdoctoral and clinical fellows – really, anyone who is interested – to the many tools used in medical imaging. These include software tools and most of the major imaging modalities wielded by investigators (MRI, PET, EEG, MEG, optical, TMS and others). As the name of the series suggests, the talks cover both the reasons researchers might need a particular tool and the nuts and bolts of how to apply it. You can watch videos of the overviews below.
DAGMan for orchestrating complex workflows on HTC resources (High Throughput Computing)
0
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.
Research Software Development in JupyterLab: A Platform for Collaboration Between Scientists and RSEs
0
Iterative Programming takes place when you can explore your code and play with your objects and functions without needing to save, recompile, or leave your development environment. This has traditionally been achieved with a REPL or an interactive shell. The magic of Jupyter Notebooks is that the interactive shell is saved as a persistant document, so you don't have to flip back and forth between your code files and the shell in order to program iteratively.
There are several editors and IDE's that are intended for notebook development, but JupyterLab is a natural choice because it is free and open source and most closely related to the Jupyter Notebooks/iPython projects. The chief motivation of this repository is to enable an IDE-like development environment through the use of extensions. There are also expositional notebooks to show off the usefulness of these features.
A guide to pip in Python
0
pip stands for "pip installs packages". It's the go-to package manager for Python, allowing developers to install, update, and manage software libraries and dependencies used in Python projects. With just a few commands in your terminal or command prompt, pip makes it effortless to fetch libraries from the Python Package Index (PyPI) and integrate them into your projects. This guide will walk you through the basics of pip, from installation to advanced package management.
Automated Machine Learning Book
0
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.