- HPCwire0HPCwire is a prominent news and information source for the HPC community. Their website offers articles, analysis, and reports on HPC technologies, applications, and industry trends.
- A Practical Introduction to pymdp: A Two-Part Guide to Active Inference in Python0
- Active Inference ModelStream 007.1 ~ Conor Heins & Daphne Demekas ~ pymdp
- Active Inference ModelStream 007.2 ~ pymdp
Here are descriptions for a two-part video series from the Active Inference Institute. These videos introduce pymdp, a powerful Python package for researchers and developers working with active inference and the Free Energy Principle. 🧠The first video provides a high-level overview of the package, while the second dives into a practical coding demonstration. Together, they offer a comprehensive guide from theory to application.
Active Inference ModelStream 007.1 ~ Conor Heins & Daphne Demekas ~ pymdp
This video provides an overview of pymdp, a Python package designed for active inference. Active inference is a framework for modeling decision-making and planning based on the Free Energy Principle, which posits that intelligent agents act to minimize surprise or uncertainty about their world. The pymdp package allows researchers to simulate how agents achieve this through both perception and action. It was created as a more accessible, user-friendly alternative to traditional MATLAB tools. The package is modular, integrates with other Python libraries like PyTorch, and has future plans to incorporate JAX for deep learning applications, making it a powerful tool for neuroscience and AI researchers studying the Free Energy Principle.
Active Inference ModelStream 007.2 ~ pymdp
This video, the second part of a series, demonstrates how to implement an active inference agent using the pymdp library. The presentation focuses on building a generative model for a contextual multi-armed bandit task. Key concepts explained include the four main components of a POMDP—the A, B, C, and D matrices—which represent the agent's beliefs. The video also introduces the concept of factorized state spaces to manage computational complexity and details how an agent can learn by updating its model parameters based on experience. The ultimate goal is to show how these agents, following the Free Energy Principle, can engage in both optimal and information-seeking behavior.
- Docker Container Library0The 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!
- GIS: What is a Geodetic Datums?0Often when working with GIS, or spatial data, one encounters the word "datum" and it may require that you choose a "datum" when doing GIS computation tasks. Below is a short video on what are datums from NOAA and UCAR.
- Gesture Classifier Model using MediaPipe0MediaPipe is Google's open-source framework for building multimodal (e.g., video, audio, etc.) machine learning pipelines. It is highly efficient and versatile, making it perfect for tasks like gesture recognition. This is a tutorial on how to make a custom model for gesture recognition tasks based on the Google MediaPipe API. This tutorial is specifically for video-playback, though could be generalized to image and live-video feed recognition.
- Advanced Mathematical Optimization Techniques0Mathematical optimization deals with the problem of finding numerically minimums or maximums of a functions. This tutorial provides the Python solutions for the optimization problems with examples.
- Research Security Operations Center at IU0The NSF-funded ResearchSOC helps make scientific computing resilient to cyberattacks and capable of supporting trustworthy, productive research through operational cybersecurity services, training, and information sharing necessary to a community as unique and variable as research and education (R&E). ResearchSOC is a service offering from Indiana University's OmniSOC.
- ACCESS - Video for new ACCESS users0This is a short video on how to exchange ACCESS credits and connect to Jetstream 2 (please note this was created for Duke users but applies to all) .
- Fairness and Machine Learning0The "Fairness and Machine Learning" book offers a rigorous exploration of fairness in ML and is suitable for researchers, practitioners, and anyone interested in understanding the complexities and implications of fairness in machine learning.
- Practical Machine Learning with Python0This video series provides a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. It covers topics such as linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. Goes over the high level intuitions of the algorithms and how they are logically meant to work. Apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn.
- Workshop on LangChain and GPT0
This interactive workshop introduces participants to the power of GPT and LangChain for solving domain-specific scientific challenges. Participants will learn how to use these tools to address real research problems, such as predicting molecular properties or analyzing large-scale datasets in genomics. Through guided tutorials and hands-on project development, attendees will leave with a working application tailored to their own research needs.
- Containerization Explained0Containerization is a software development method in which applications are packaged into standard units for development, shipment, and deployment.
- Paraview UArizona HPC links (beginner)0
- University of Arizona Visualization homepage
- Getting Started with Paraview
- Paraview Cameras and Keyframes
- Graphs and Data Exporting
- Visualizing netcdf files
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. - RMACC Systems Administrator Workshop Slides0A compilation of the slides from this year's RMACC Sys Admin Workshop. RMACC Sys Admin Workhop Schedule: Tuesday 12:00 PM Sign-in 1:00 PM Introductions 1:30 PM Lightning Talk - HPC Survival guide 2:00 PM Node Management - Scott Serr 2:30 PM Lightning Talk - Warewulf 3:00 PM Urgent HPC - Coltran Hophan-Nichols and Alexander Salois Wednesday 9:00 AM Breakfast 10:00 AM Round table Sites - BYU, INL, UMT, ASU, MSU 11:00 AM Open OnDemand setup - Dean Anderson 11:30 AM Lightning talk - Long term hardware support 12:00 PM Lunch 1:00 PM HPC Security - Matt Bidwell 2:00 PM Lightning talk- Security 2:30 PM ACCESS resources - Couso 3:00 PM Easybuild tutorial - Alexander Salois 3:30 PM General Q & A Thursday 9:00 AM Breakfast 10:00 AM Lightning Talk- Containers and Virtual Machines 11:00 AM University of Montana - Hellgate Site Tour 11:30 AM Closing Remarks
- PyTorch Introduction0This is a very barebones introduction to the PyTorch framework used to implement machine learning. This tutorial implements a feed-forward neural network and is taught completely asynchronously through Stanford University. A good start after learning the theory behind feed-forward neural networks.
- Bash shell tutorial0Training materials for using the bash (and zsh) shell.
- GPU Acceleration in Python0This tutorial explains how to use Python for GPU acceleration with libraries like CuPy, PyOpenCL, and PyCUDA. It shows how these libraries can speed up tasks like array operations and matrix multiplication by using the GPU. Examples include replacing NumPy with CuPy for large datasets and using PyOpenCL or PyCUDA for more control with custom GPU kernels. It focuses on practical steps to integrate GPU acceleration into Python programs.
- Ultimate guide to Unix0Unix is incredibly common and useful. This website provides all the common commands and explanations for one to get started with a unix system.
- Using Dask on HPC Systems0A 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
- Implementing Markov Processes with Julia0The 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.
- Numba: Compiler for Python0Numba 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.
- Introductory Python Lecture Series0A lecture and notes with the goal of teaching introductory python. Starting by understanding how to download and start using python, then expanding to basic syntax for lists, arrays, loops, and methods.
- RMACC Website0Rocky Mountain Advanced Computing Consortium Website
