Knowledge Base Resources
Contributed by cyberinfrastructure professionals (researchers, research computing facilitators, research software engineers and HPC system administrators), these resources are shared through the ConnectCI community platform. Add resources you find helpful!
Framework to help in scaling Machine Learning/Deep Learning/AI/NLP Models to Web Application level
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This framework will help in scaling Machine Learning/Deep Learning/Artificial Intelligence/Natural Language Processing Models to Web Application level almost without any time.
PetIGA, an open-source code for isogeometric analysis
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This documentation provides an overview of the PetIGA framework, an open source code for solving multiphysics problems with isogeometric analysis. The documentation covers some simple tutorials and examples to help users get started with the framework and apply it to solve real-world problems in continuum mechanics, including solid and fluid mechanics.
Samtools Documentation
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Samtools is a suite of programs for interacting with high-throughput sequencing data, especially in the SAM/BAM format. It offers various utilities for processing, analyzing, and managing sequence data generated from next-generation sequencing (NGS) experiments. Samtools is widely used in bioinformatics and genomics research for tasks such as read alignment, variant calling, and data manipulation.
Metadata Systems
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Metadata is a vital topic in libraries and librarianship, encompassing structured information used for accessing digital resources. The definition of metadata varies but is essentially data about data. It has evolved beyond simply describing metadata schemas and now focuses on topics like interoperability, non-descriptive metadata (administrative and preservation metadata), and the effective application of metadata schemas for user discovery. Interoperability, the ability to seamlessly exchange metadata between systems, is a major concern. Different levels of interoperability are examined, including schema-level, record-level, and repository-level. Challenges to interoperability include variations in standards, collaboration barriers, and costs.Metadata management is discussed in terms of the holistic management of metadata across an entire library. Steps include analyzing metadata requirements, adopting schema, creating metadata content, delivery/access, evaluation, and maintenance. Administrative metadata, which encompasses ownership and production information, is becoming more critical, particularly for electronic resource licensing. Preservation metadata is also gaining importance in ensuring the long-term viability of digital objects.
GDAL Multi-threading
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Multi-threading guidance when using GDAL.
InsideHPC
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InsideHPC is an informational site offers videos, research papers, articles, and other resources focused on machine learning and quantum computing among other topics within high performance computing.
Probabilistic Semantic Data Association for Collaborative Human-Robot Sensing
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Humans cannot always be treated as oracles for collaborative sensing. Robots thus need to maintain beliefs over unknown world states when receiving semantic data from humans, as well as account for possible discrepancies between human-provided data and these beliefs. To this end, this paper introduces the problem of semantic data association (SDA) in relation to conventional data association problems for sensor fusion. It then, develops a novel probabilistic semantic data association (PSDA) algorithm to rigorously address SDA in general settings. Simulations of a multi-object search task show that PSDA enables robust collaborative state estimation under a wide range of conditions.
phenoACCESS-24 workshop program materials
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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.
Feed Forward NNs and Gradient Descent
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Feed-forward neural networks are a simple type of network that simply rely on data to be "fed-forward" through a series of layers that makes decisions on how to categorize datum. Gradient descent is a type of optimization tool that is often used to train machines. These two areas in ML are good starting points and are the easiest types of neural network/optimization to understand.
Ask.CI Q&A Platform for Research Computing
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AI Institutes Cyberinfrastructure Documents: SAIL Meeting
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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.
Setting up PyFR flow solver on clusters
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These instructions were executed on the FASTER and Grace cluster computing facilities at Texas A&M University. However, the process can be applied to other clusters with similar environments. For local installation, please refer to the PyFR documentation.
Please note that these instructions were valid at the time of writing. Depending on the time you're executing these, the versions of the modules may need to be updated.
1. Loading Modules
The first step involves loading pre-installed software libraries required for PyFR. Execute the following commands in your terminal to load these modules:
module load foss/2022b
module load libffi/3.4.4
module load OpenSSL/1.1.1k
module load METIS/5.1.0
module load HDF5/1.13.1
2. Python Installation from Source
Choose a location for Python 3.11.1 installation, preferably in a .local directory. Navigate to the directory containing the Python 3.11.1 source code. Then configure and install Python:
cd $INSTALL/Python-3.11.1/
./configure --prefix=$LOCAL --enable-shared --with-system-ffi --with-openssl=/sw/eb/sw/OpenSSL/1.1.1k-GCCcore-11.2.0/ PKG_CONFIG_PATH=$LOCAL/pkgconfig LDFLAGS=/usr/lib64/libffi.so.6.0.2
make clean; make -j20; make install;
3. Virtual Environment Setup
A virtual environment allows you to isolate Python packages for this project from others on your system. Create and activate a virtual environment using:
pip3.11 install virtualenv
python3.11 -m venv pyfr-venv
. pyfr-venv/bin/activate
4. Install PyFR Dependencies
Several Python packages are required for PyFR. Install these packages using the following commands:
pip3 install --upgrade pip
pip3 install --no-cache-dir wheel
pip3 install --no-cache-dir botorch pandas matplotlib pyfr
pip3 uninstall -y pyfr
5. Install PyFR from Source
Finally, navigate to the directory containing the PyFR source code, and then install PyFR:
cd /scratch/user/sambit98/github/PyFR/
python3 setup.py develop
Congratulations! You've successfully set up PyFR on the FASTER and Grace cluster computing facilities. You should now be able to use PyFR for your computational fluid dynamics simulations.
Spatial Data Science in the Cloud (Alpine HPC) using Python
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Spatial Data Science is a growing field across a wide range of industries and disciplines. The open-source programming language Python has many libraries that support spatial analysis, but what do you do when your computer is unable to tackle the massive file sizes of high-resolution data and the computing power required in your analysis?
There materials have been prepared to teach you spatial data science and how to execute your analysis using a high-performance computer (HPC).
A visual introduction to Gaussian Belief Propagation
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This website is an interactive introduction to Gaussian Belief Propagation (GBP). A probabilistic inference algorithm that operates by passing messages between the nodes of arbitrarily structured factor graphs. A special case of loopy belief propagation, GBP updates rely only on local information and will converge independently of the message schedule. The key argument is that, given recent trends in computing hardware, GBP has the right computational properties to act as a scalable distributed probabilistic inference framework for future machine learning systems.
Singularity/Apptainer User Manuals
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Singularity/Apptainer is a free and open-source container platform that allows users to build and run containers on high performance computing resources.
SingularityCE is the community edition of Singularity maintained by Sylabs, a company that also offers commercial Singularity products and services.
Apptainer is a fork of Singularity, maintained by the Linux foundation, a community of developers and users who are passionate about open source software.
Info about retiring of R GIS packages rgdal, rgeos, maptools in 2023
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R GIS packages "rgdal", "rgeos", and "maptools" are package set to be archived and no longer supported by end of 2023. Many other R GIS packages are build on top of these packages, including "sp" and "raster". The packages recommended as replacement for "sp" is "sf" and the replacement for "raster" is "terra". Below are links to published articles regarding this transition. Additionally, I am including links to the documentation for the new packages recommended to be used "sf" and "terra".
Examples of code using JSON nlohmann header only Library for C++
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This code showcases how to work with the header-only nlohmann JSON library for C++. In order to compile, change the extensions from json_test.txt to json_test.cpp and test.txt to test.json. You must also download the header files from https://github.com/nlohmann/json. Complilation instructions are at the bottom of json_test. This code is very helpful for creating config files, for example.
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.
Master’s in Cybersecurity Degree Essentials
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Offers comprehensive information on various master's degree options in cybersecurity, including program details, admission requirements, and career opportunities, helping students make informed decisions about pursuing an advanced degree in cybersecurity.
R for Research Scientists
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A book for researchers who contribute code to R projects: This booklet is the result of my work with the Social Cognition for Social Justice lab. It was developed in response to questions I was getting from students; both grad students that were making software design decisions, and undergraduates who were using things like version control for the first time. Although many tutorials and resources exist for these topics, there was not a single source that I thought covered just enough material to build up to the workflow used by the lab without extraneous detail.
Fine-tuning LLMs with PEFT and LoRA
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As LLMs get larger fine-tuning to the full extent can become difficult to train on consumer hardware. Storing and deploying these tuned models can also be quite expensive and difficult to store. With PEFT (parameter -efficent fine tuning), it approaches fine-tune on a smaller scale of model parameters while freezing most parameters of the pretrained LLMs. Basically it is providing full performance that which is similar if not better than full fine tuning while only having a small number of trainable parameters. This source explains that as well as going over LORA diagrams and a code walk through.
FreeSurfer Tutorials
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The official MGH / Harvard tutorial page for FreeSurfer. The FreeSurfer group has provided and designed a series of tutorials for using FreeSurfer and for getting acquainted with the concepts needed to perform its various modes of analysis and processing of MRI data. The tutorials are designed to be followed along in a terminal window where commands can be copy/pasted instead of typed.
Beautiful Soup - Simple Python Web Scraping
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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.
Better Scientific Software (BSSw)
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The Better Scientific Software (BSSw) project provides a community to collaborate and learn about best practices in scientific software development. Software—the foundation of discovery in computational science & engineering—faces increasing complexity in computational models and computer architectures. BSSw provides a central hub for the community to address pressing challenges in software productivity, quality, and sustainability.
marimo | a next generation python notebook
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Introduction seminar for new reactive python notebook from marimo ambassador.