HPC University
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A comprehensive list of training resources from the HPC University. HPCU is a virtual organization whose primary goal is to provide a cohesive, persistent, and sustainable on-line environment to share educational and training materials for a continuum of high performance computing environments that span desktop computing capabilities to the highest-end of computing facilities offered by HPC centers.
Cornell Virtual Workshop
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Cornell Virtual Workshop is a comprehensive training resource for high performance computing topics. The Cornell University Center for Advanced Computing (CAC) is a leader in the development and deployment of Web-based training programs. Our Cornell Virtual Workshop learning platform is designed to enhance the computational science skills of researchers, accelerate the adoption of new and emerging technologies, and broaden the participation of underrepresented groups in science and engineering. Over 350,000 unique visitors have accessed Cornell Virtual Workshop training on programming languages, parallel computing, code improvement, and data analysis. The platform supports learning communities around the world, with code examples from national systems such as Frontera, Stampede2, and Jetstream2.
Attention, Transformers, and LLMs: a hands-on introduction in Pytorch
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This workshop focuses on developing an understanding of the fundamentals of attention and the transformer architecture so that you can understand how LLMs work and use them in your own projects.
Enhanced Sampling for MD simulations
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Using Linux commands in a python script (and the difference between the subprocess and os python modules)
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Learn how to use Linux commands in a python script. Specifically, learn how to use the subprocess and os modules in python to run shell commands (which run Linux commands) in a python script that is run on a cluster.
ACCESS HPC Workshop Series
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Monthly workshops sponsored by ACCESS on a variety of HPC topics organized by Pittsburgh Supercomputing Center (PSC). Each workshop will be telecast to multiple satellite sites and workshop materials are archived.
Introduction to Deep Learning in Pytorch
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This workshop series introduces the essential concepts in deep learning and walks through the common steps in a deep learning workflow from data loading and preprocessing to training and model evaluation. Throughout the sessions, students participate in writing and executing simple deep learning programs using Pytorch – a popular Python library for developing, training, and deploying deep learning models.
File management of Visual Studio Code on clusters
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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.
Texas A&M HPRC Training Site
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Training Resources and Courses offered by Texas A&M's Research Computing Group
TensorFlow for Deep Neural Networks
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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
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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.
NCSA HPC-Moodle
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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.
NERSC Training and Tutorials
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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
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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.
Understanding LLM Fine-tuning
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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.
Why 'N How: Martinos Center for Biomedical Imaging:
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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.
Thrust resources
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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.
DAGMan for orchestrating complex workflows on HTC resources (High Throughput Computing)
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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
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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.
Automated Machine Learning Book
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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.
OnShape FeatureScripts: Custom features for everyone
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OnShape FeatureScripts allow users to create their own features via OnShape's programming language. The user can make these as simple or complex as they need, and they can save tons of time for heavy OnShape users or complex projects!
A guide to pip in Python
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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.
Active inference textbook
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This textbook is the first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines including computational neurosciences, machine learning, artificial intelligence, and robotics. It was published in 2022 and it's open access at this time. The contents in this textbook should be educational to those who want to understand how the free energy principle is applied to the normative behavior of living organisms and who want to widen their knowledge of sequential decision making under uncertainty.
Implementing Markov Processes with Julia
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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.