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In development

JupyterLab is accessible for testing, and the service may be subject to disruption. To request access, or to provide feedback, please use our service desk. We are glad to hear from you and learn from your experience!


LuxProvide offers JupyterLab as SaaS on the MeluXina Cloud, with native Python support and capability for custom Jupyter kernels.

Gaining access

You can access JupyterLab at

Authentication is managed via Keycloak: you may refer to its documentation.


Starting a session

Once logged-in, you will be prompted to select your session options.


Option Required Description SBATCH equivalent
Account name Yes The Slurm project account to use, e.g. p000000 #SBATCH --account
Allocation time Yes Time allocation to request #SBATCH --time
Partition Yes The node type to allocate #SBATCH --nodes
Nodes count Yes The number of nodes to allocate #SBATCH --nodes
QOS No The QOS to request #SBATCH --qos
Job name No The job name to assign to the JupyterLab session #SBATCH --job-name
Log file No The file to write the JupyterLab logs to #SBATCH --output
Profile No The default software stack to use -

Finishing your session

You can terminate your JupyterLab session and release your compute allocation manually.

  • From the JupyterLab launcher: click File then Hub Control Panel
  • From the Hub Control Panel: click on Stop My Server



Creating a custom kernel

Python kernels can be created from Python virtual environments.

Please refer to the dedicated documentation.

Julia can be added as a JupyterLab kernel if installed in the user context.

Please refer to the dedicated documentation.

Using a custom kernel

Use a kernel from JupyterLab launcher

Select the kernel form the launcher tab:


Use a kernel from a notebook

  • From a notebook tab: click on the kernel name (top-right)
  • Select the kernel to use in the drop-down list


List kernels

jupyter-kernelspec list

Uninstall a kernel

jupyter-kernelspec uninstall ${kernel_name}