Spawner options#

After you log in into Jupyterhub you will need to start a session. We provide two different option forms (for all DKRZ systems) to customize your session:

  • Preset: A simplified form with commonly used settings for SLURM and the notebook environment (e.g., memory, CPUs, and walltime) preconfigured.

  • Advanced: Designed for experienced users who are familiar with Levante configuration and SLURM, this form offers full control over session parameters.

../../../_images/new-spawner-home.png

Advanced#

The advanced options form is intended for experienced users who have at least a basic understanding of Slurm parameters. It offers enhanced flexibility for:

  • Selecting accounts, partitions, gpu types, reservations, and features via dropdown menus

  • Choosing between various notebook interfaces, such as Notebook, Lab, or Terminal

../../../_images/advanced.png

Parameter

Mandatory

Description

Account

Yes

Project that should be charged

Partition

Yes

Partition to run the job

Reservation

No

Resources reserved for certain time/accounts

Time

No (Default: 1 Hour)

The maximum amount of time your job can take before Slurm forcefully kills it.

Number of cores

No (Default: 1 core)

Number of threads (logical cores) per task.

Memory

No (Default: 1024 MB)

The total amount of RAM to allocate.

QoS

No

Quality of Service often puts the job in high priority queue (e.g. training).

GPU Configuration

No (Default: gpu:0)

Type and count of GPU devices

Log File Name

No (Default: jupyterhub_slurmspawner_advanced)

Notebook log

Request Features

No

Node-features requested for the job.

User interfaces

No (Default: jupyterlab)

Notebook/Lab/Terminal/VNC Desktop

Container mode

Default: No

Launch jupyterlab in a container

Container mode#

Container mode enables users to launch different server environments within an isolated, containerized session. Its primary purpose is to facilitate testing of new software and improve reproducibility.

container-mode

Currently, the following pre-built images with different software stack are available:

  • jupyter-code-rstudio

    • Code-Server (VS Code in the browser) [1]

    • RStudio – R development environment [4]

    • Xpra - Persistent remote applications for X11 [2]

    • Tensorboard - TensorFlow’s visualization toolkit [3]

  • jupyter-processing

    • minimal image with state-of-the-art python packages for data processing

      • xarray, dask and more (check with micromamba list)

      • updated regularly/on-demand

Levante environment modules and SPACK enabled.

Additionally new:

  • customized launcher to provide meaningful category entries

launcher

  • default and in-container kernels

  • new button on the top right for Hub Control Panel

  • extension to create favorite directories

Check the system requirements for the requested application and set the amount of memory before starting your session. The table below shows the minimal required memory to start the container only.

Recommended Memory for Singularity Images#

Image Name

Required Memory (MB)

jupyter-processing

6000

jupyter-code-rstudio

7000

Note

In some cases, we can help users create customized container images for training sessions, ensuring all participants have exactly the same tools and libraries. This allows organizers to focus on the topic, not on environment setup or package installation issues.

Form options / browser’s storage#

For both preset and advanced, the form options are stored every 10s and are not lost when you close/stop your notebook. This is a nice feature especially for the advanced options where there are many inputs. You can also reset the form to the initial state.

../../../_images/options_saved1.gif

Note

This feature is based on the browser’s storage. If you delete the cache, the options are also deleted.

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