Data Visualization tools for Python
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Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It makes analyzing and presenting your data extremely easy and works with Python which many people already know.
Enhanced Sampling for MD simulations
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Introduction to Python for Digital Humanities and Computational Research
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This documentation contains introductory material on Python Programming for Digital Humanities and Computational Research. This can be a go-to material for a beginner trying to learn Python programming and for anyone wanting a Python refresher.
Managing Python Packages on an HPC Cluster
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This workshop will go into the different ways python packages can be managed in a cluster environment using conda and python virtual environments both in batch mode from the command line and with Jupyter Notebooks and Jupyter Lab on the cluster. The examples will be run on the GMU HOPPER Cluster.
MATLAB with other Programming Languages
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MATLAB is a really useful tool for data analysis among other computational work. This tutorial takes you through using MATLAB with other programming languages including C, C++, Fortran, Java, and Python.
Python Tools for Data Science
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Python has become a very popular programming language and software ecosystem for work in Data Science, integrating support for data access, data processing, modeling, machine learning, and visualization. In this webinar, we will describe some of the key Python packages that have been developed to support that work, and highlight some of their capabilities. This webinar will also serve as an introduction and overview of topics addressed in two Cornell Virtual Workshop tutorials, available at https://cvw.cac.cornell.edu/pydatasci1 and https://cvw.cac.cornell.edu/pydatasci2
Optimizing Research Workflows - A Documentation of Snakemake
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Snakemake is a powerful and versatile workflow management system that simplifies the creation, execution, and management of data analysis pipelines. It uses a user-friendly, Python-based language to define workflows, making it particularly valuable for automating and reproducibly managing complex computational tasks in research and data analysis.
AI/ML TechLab - Accelerating AI/ML Workflows on a Composable Cyberinfrastructure
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This technology lab contains a set of sessions to help a new user start an AI project on the ACES cluster, a composable accelerator testbed at Texas A&M University. You will learn how to create and activate a virtual environment, manipulate and visualize data with Pandas and Matplotlib, use Scikit-learn for linear regression and classification applications, and use Pytorch to create and train a simple image classification model with deep neural networks (DNN).
AHPCC documentary
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This link is a documentary website to use AHPCC.
Quick and Robust Data Augmentation with Albumentations Library
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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
Electric field analyses for molecular simulations
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Tool to compute electric fields from molecular simulations
Practical Machine Learning with Python
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This 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.
CUDA Toolkit Documentation
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NVIDIA CUDA Toolkit Documentation: If you are working with GPUs in HPC, the NVIDIA CUDA Toolkit is essential. You can access the CUDA Toolkit documentation, including programming guides and API references, at this provided website
Official Documentation for PyTorch and NumPy
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The official documentation for PyTorch, a machine learning tensor-based framework, and NumPy, which allows for support for ndarrays which is useful to make tensors when implementing NNs. Both libraries can be installed with pip.
AI powered VsCode Editor
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**Cursor: The AI-Powered Code Editor**
Cursor is a cutting-edge, AI-first code editor designed to revolutionize the way developers write, debug, and understand code. Built upon the premise of pair-programming with artificial intelligence, Cursor harnesses the capabilities of advanced AI models to offer real-time coding assistance, bug detection, and code generation.
**How Cursor Benefits High-Performance Computing (HPC) Work:**
1. **Efficient Code Development:** With AI-assisted code generation, researchers and developers in the HPC realm can quickly write optimized code for simulations, data processing, or modeling tasks, reducing the time to deployment.
2. **Debugging Assistance:** Handling complex datasets and simulations often lead to intricate bugs. Cursor's capability to automatically investigate errors and determine root causes can save crucial time in the HPC workflow.
3. **Tailored Code Suggestions:** Cursor's AI provides context-specific code suggestions by understanding the entire codebase. For HPC applications where performance is paramount, this means receiving recommendations that align with optimization goals.
4. **Improved Code Quality:** With AI-driven bug scanning and linter checks, Cursor ensures that HPC codes are not only fast but also robust and free of common errors.
5. **Easy Integration:** Being a fork of VSCode, Cursor allows seamless migration, ensuring that developers working in HPC can swiftly integrate their existing VSCode setups and extensions.
In essence, for HPC tasks that demand speed, precision, and robustness, Cursor acts as an invaluable co-pilot, guiding developers towards efficient and optimized coding solutions.
It is free if you provide your own OPEN AI API KEY.
Official Python Documentation
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The official documentation for Python 3.11.5. Python comes with a lot of features built into the language, so it is worth taking a look as you code.
Globus Documentation
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Globus is a data transfer, sharing, automation, and discovery service used by hundreds of thousands of researchers to manage "big data" at universities, research labs, and national systems such as ACCESS. The Globus documentation website provides how-to guides, reference documentation, and examples for Globus's web application, command-line interface, Python software development kit (SDK), and APIs.
Conda
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Conda is a popular package management system. This tutorial introduces you to Conda and walks you through managing Python, your environment, and packages.
MDAnalysis - Python library for the analysis of molecular dynamics simulations
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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.
Intro to Statistical Computing with Stan
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The Stan language is used to specify a (Bayesian) statistical model with an imperative program calculating the log probability density function. Here are some useful links to start your exploration of this statistical programming language, and a Python interface to Stan.
Reinforcement Learning For Beginners with Python
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This course takes through the fundamentals required to get started with reinforcement learning with Python, OpenAI Gym and Stable Baselines. You'll be able to build deep learning powered agents to solve a varying number of RL problems including CartPole, Breakout and CarRacing as well as learning how to build your very own/custom environment!