Skip to content

The UChicago JupyterLab

To support machine learning code development, our users can deploy one or more private JupyterLab applications.

To encourage fair sharing these applications are time limited. We also ask users to request only the resources that they need.

How to launch JupyterLab at UChicago

Once you login, click "Services" on the top menu bar, then choose "JupyterLab". You will need to make some choices in order to configure your JupyterLab notebook:

  1. Provide a notebook name that has no whitespace, using 30 characters or less from the set [a-zA-Z0-9._-] to name your notebook.
  2. You can request 1 to 16 CPU cores.
  3. You can request 1 to 32 GB of memory.
  4. You can request 0 to 7 GPU instances.
  5. A notebook can have lifetime of up to 72 hours (1 to 168 hours).
  6. You can select a GPU model based on its memory size. If you request a GPU, please make sure the GPU is available, by clicking on the icon next to GPU memory.
  7. You can choose a Docker image from the dropdown.

Resource Limitations

  • You can request 1 to 16 CPU cores.
  • You can request 1 to 32 GB of memory.
  • You can request 0 to 7 GPU instances.
  • A notebook can have lifetime of up to 72 hours.
  • You can select a GPU model based on its memory size. If you request a GPU, please make sure the GPU is available, by clicking on the icon next to GPU memory.

Selecting GPU memory and instances

The AF cluster has four NVIDIA A100 GPUs. Each GPU can be partitioned into seven GPU instances. This means the AF cluster can have up to 28 GPU instances running in parallel.

A user can request 0 to 7 GPU instances as a resource for the notebook. A user can request 40,836 MB of memory for an entire A100 GPU, or 4864 MB of memory for a MIG instance.

Docker Images

ml_platform

The primary image available is ml_platform, a comprehensive machine learning platform that includes:

  • Python 3.12 with ML frameworks (TensorFlow, Keras, scikit-learn)
  • ROOT 6.32+ for HEP analysis
  • JupyterLab with extensions (RISE, Git integration, ipywidgets)
  • HEP tools (uproot, atlasify, rucio-jupyterlab)
  • NVIDIA GPU support (CUDA 13.0)
  • Data science libraries (NumPy, Pandas, SciPy, PyArrow, HDF5)

Available tags:

  • ml_platform:latest - Latest stable version (recommended)
  • ml_platform:YYYY.MM - Specific (older) release versions

For the complete list of packages, version information, and detailed documentation, see the ml_platform repository.

AnalysisBase Images

  • AB-stable - Based on AnalysisBase
  • AB-dev - Based on AnalysisBase but with cutting edge uproot, dask, awkward arrays, etc.

Other Images

Additional images may be available in the dropdown menu. Contact the facility team if you need a specific software environment not provided by these images.

Getting help

For software additions, upgrades, or questions about the JupyterLab environment: