Knowledge Base Resources
These resources have been contributed and “vetted” by the community of cyberinfrastructure professionals (researchers, research computing facilitators, research software engineers and HPC system administrators) that are participating in programs such as this one, that are supported by the ConnectCI community management platform. Additional Knowledge Base Resources are always welcome!
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.
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.
Pandas - Python
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pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. It lets you store data in easy to manage and display data frames, with column names and datatypes.
Contributing cycles to the Open Science Grid
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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 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.
Guide to building AirSim on Linux machines
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This article provides step-by-step instructions on how to build AirSim, a simulator for autonomous vehicles, on Linux. It includes both Docker and host machine setup options, along with details on building Unreal Engine, AirSim, and the Unreal environment. It also provides guidance on how to use AirSim once it is set up.
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).
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.
Neocortex Documentation
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Neocortex is a new supercomputing cluster at the Pittsburgh Supercomputing Center (PSC) that features groundbreaking AI hardware from Cerebras Systems.
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.