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!
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.
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.
HPCwire 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.
These slides provide an introduction on how Termius and Cursor, two new and freemium apps that use AI to perform more efficient work, can be used for faster HPC research.
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
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).
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.