JupyterHub Environment

JupyterHub Environment#

Build and Test Your OpenSees Scripts with Flexibility and Speed

The JupyterHub Environment on DesignSafe runs on a Kubernetes-managed cluster at the Texas Advanced Computing Center (TACC). It provides a highly accessible, interactive platform for developing, testing, and refining OpenSees scripts—whether you’re using Tcl, Python (OpenSeesPy), or both.

This environment is the starting point (and end point) for most workflows. It allows you to iteratively build and test your input scripts in real time before scaling up to HPC batch jobs.

Why Use Jupyter Hub?#

  • Immediate startup — no job queues or allocation requests

  • Full control and feedback — ideal for debugging, exploration, and visualization

  • Integrated tools — notebook interface, terminal, file manager, code editor, and Python console

  • Supports OpenSees, OpenSeesMP/SP, and OpenSeesPy, plus other languages (MATLAB, Julia, R)

  • Direct connection to Tapis — submit HPC jobs from within the notebook

Performance and Resources#

  • Jupyter Hub supports Python, Julia, MATLAB, and R — useful for researchers integrating multi-language workflows.

  • Each Jupyter session runs in its own container, orchestrated by Kubernetes. The system guarantees:

  • Up to 8 CPU cores and 20 GB of RAM per user session

  • Exclusive use of these resources within your container (not shared with other users)

  • Shared physical nodes underneath, which may cause minor I/O contention under heavy load

This environment is optimized for:

  • Interactive model development

  • Small to medium-sized simulations

  • Pre- and post-processing of data

  • Batch job submission to HPC systems

Summary#

The DesignSafe Jupyter Hub is a powerful entry point into workflows. It offers dedicated computing resources in an easy-to-use environment where you can:

  • Develop, debug, and run your scripts in real time

  • Automate job submission to HPC systems like Stampede3

  • Perform pre- and post-processing without leaving the notebook interface

Use it as your launchpad for scalable computation: once your workflow is ready, move to HPC for large-scale runs without leaving Jupyter.

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