JupyterHub requires a user to be a member of a project on either:

Either request a new project on one of the sites above (for example: a project dedicated to your PhD research), or request to join an existing project if you need to collaborate with other users.

Each project receives a separate project storage on JupyterHub (that can also be used on GPULab).

Upon starting your Jupyter-instance you can specify the amount of CPUs, GPUs and Memory that you require. The Jupyter-instances are launched within Docker-containers on the Virtual Wall-infrastructure via GPULab. We support usage of the Jupyter Docker Stacks images, which provide a great starting point for all usage scenarios. Interesting images are:

  • jupyter/scipy-notebook includes popular packages from the scientific Python ecosystem.
  • jupyter/r-notebook includes popular packages from the R ecosystem.
  • jupyter/tensorflow-notebook includes popular Python deep learning libraries like tensorflow and keras.
  • jupyter/datascience-notebook includes libraries for data analysis from the Julia, Python and R communities
  • jupyter/pyspark-notebook includes Python support for Apache Spark.
  • jupyter/all-spark-notebook includes Python, R and Scala support for Apache Spark.

We also offer GPU-enabled images, as the default images mentioned above are not. These images are built on top of the nvidia/cuda base images, and include the gpu-enabled software packages were applicable. They are available pre-built in the public GPULab Docker repository. For example:

  • gitlab.ilabt.imec.be:4567/ilabt/gpu-docker-stacks/tensorflow-notebook:latest-gpu is a GPU-enabled variant of the original tensorflow-notebook.

Your Jupyter instance gives direct access to the storage of your projects on the imec iLab.t infrastructure, you can thus also use this service to view, upload and download files in your project folder.