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Jupyter Server

Open OnDemand Changes and Known Issues

  • Open OnDemand now uses the CILogon for user authentication.
  • To access your scratch directory from Jupyter on Open OnDemand please create a symlink to your scratch directory on your home directory:
    ln -s /global/scratch/users/$USER scratch

The Jupyter Notebook is a web application that enables you to create and share documents that can contain a mix of live code, equations, visualizations, and explanatory text. This is an introduction to using Jupyter notebooks on Lawrencium.

Before getting started, make sure you have access to the Lawrencium cluster.

As described next, you can start a Jupyter notebook via the Open OnDemand service, which allows you to operate completely via your web browser on your local computer (e.g., your laptop).

Jupyter notebooks on Open OnDemand

Running a notebook

  1. Connect to https://lrc-ondemand.lbl.gov .

  2. After logging in, you will get to the Open OnDemand welcome screen. Click the Interactive Apps pulldown.

  3. Choose the Jupyter Server option from the list of apps. Choose the Jupyter Server - interactive for exploration/debugging only if you are writing/debugging code and not doing any computationally intensive tasks. Jupyter App Selection

  4. Fill out the form presented to you and click on Launch. An example of filling this form is shown in the next section.

  5. Once the server is ready, you will be able to click on the Connect to Jupyter button to get a jupyter notebook.

Example: Launch a Jupyter Server on the ES1 GPU parition

  1. Select the following parameters to lauch a jupyter server on one GPU card of a A40 GPU node using a normal priority queue (with 16 CPU cores):

    • SLURM Partition: es1
    • Name of SLURM Quality of Service (QoS): es_normal
    • Number of nodes: 1
    • Select GPU Type from dropdown: es1: NVidia A40 (40 GB) 1-4x
    • Select number of GPU cards to use: 1
    • Number of CPU cores per Node: 16

    Please also choose or enter the SLURM Project/Account Name, the Wall Clock Time, and Name of the job according to your needs.

    Example form for choosing a A40 GPU card

    Jupyter Compute Option

  2. Upon clicking Launch, you may have to wait for the requested resource to be allocated.

  3. When the server is ready, click on the Connect to Jupyter button to open your jupyter server session. Jupyter Connect

  4. After clicking on Connect to Jupyter, you will enter the classic Jupyter or Jupyterlab environment.

    • Under File > New > Notebook, you will find several Jupyter kernels with different Python versions and packages that you can choose according to your requirements. These include:
      • Python 3 (ipykernel) - python through anaconda3/2023.02-1-11.4 module
      • torch 2.3.1 py3.11.7 - PyTorch 2.3.1 through ml/pytorch/2.3.1-py3.11.7 module
      • tf 2.15.0 py3.10.0 - TensorFlow 2.15.0 through ml/tensorflow/2.15.0-py3.10.0 module

  5. You can have your session continue to operate in the background by selecting the Logout button (upper right hand corner) on Open OnDemand.

  6. To terminate a running Notebook, select the My Interactive Sessions tab on the Open OnDemand menu and click on Delete.

Further information about working with Jupyter Notebooks can be found in the Jupyter Documentation and JupyerLab Documentation