The VBT JupyterHub is a key component of the Virtual Brain Twin (VBT) ecosystem. It is an environment fully equipped to support simulation, analysis and workflow management for your use-cases - whether you're running notebooks, building interactive pipelines, or submitting jobs to HPC resources. It features a built-in kernel pre-loaded with all essential tools and libraries you need to get started immediately.
Live instance: https://twin.ebrains.eu
The built-in VBT Kernel (v1.0) includes a curated set of tools and libraries to get you started immediately:
-
Simulators:
BRIAN2,BSB,NEST,SNUDDA,TVB, and more -
Extensions:
tvb-ext-unicore,tvb-ext-xircuits -
Python Packages:
numpy,numba,ipywidgets, etc.
- Seamless access to JUWELS HPC resources
- HPC job submission and monitoring via
tvb-ext-unicore - Interactive workflows through
tvb-ext-xircuits - Rich interactive widgets for in-notebook data exploration
- Built-in Git integration for version control
Access to the VBT JupyterHub requires a valid JUDOOR account.
For account setup and login instructions, visit the official authentication guide.
This repository contains the customized frontend of the VBT JupyterHub. It builds upon the JupyterHub Enhanced Frontend and is tailored specifically to support the needs of VBT users.
The frontend is designed to operate with a dedicated backend optimized for JupyterHub deployments at the Jülich Supercomputing Centre (JSC). A compatible backend setup is essential for full functionality.
The startup page features a Sign In button along with links to information relevant to VBT:
After signing in, you are taken to the Home Page, where you can access the JupyterHub Control Panel.
Here, you'll see a list of your currently active JupyterLab instances. To start a new one, click the + button.
You can then configure the instance. The VBT project provides access to the JUWELS system hosted by JSC:
Use the left-side menu to navigate to the Kernels and Extensions section.
Here, you can choose which kernels and extensions you want to enable for your JupyterLab instance.
The VBT-specific extensions will be enabled by default.
Once your configuration is complete, click the Start button:
While your instance is starting, a new Logs section will appear in the left-side menu.
This displays the real-time logs of your JupyterLab session.
Once startup is complete, Open/Stop/Delete buttons will become available, allowing you to manage your instance:
Your JupyterLab session will automatically open in a new browser tab once it’s ready.
Alternatively, you can click the Open button to launch it manually.
In this environment, you will have access to the VBT Kernel:
To make TVB extensions (tvb-ext-unicore, tvb-ext-xircuits) available in your JupyterLab environment, you need to create a custom start script.
- Location:
$HOME/.jupyter - Name:
start_jupyter-jsc.sh
This script should load the necessary modules required for the TVB extensions. Below is a code snippet demonstrating how to configure it:
#!/bin/bash
export USERINSTALLATIONS=/p/project1/vbt
# load JupyterLab
module purge
module load Stages/2024
module load GCCcore/.12.3.0
module load Jupyter-bundle
# make extra extensions findable
module load tvb-ext-unicore/3.1.1
module load tvb-ext-xircuits/2.0.1
for path in ${JUPYTER_EXTRA_LABEXTENSIONS_PATH//:/ }; do
echo "c.LabServerApp.extra_labextensions_path.append('$path')" >> ${JUPYTER_LOG_DIR}/.config.py
done
After the custom script is in place, a new JupyterLab session must be started for the changes to take effect. During startup, the log files should include a message confirming the customized modules were loaded:
