Logging in

This page explains how to log into the Rubin Science Platform and launch a JupyterLab server.

Before you get started, make sure you have an account. See Getting an account for details.

Step 1: Log in

  1. Open the Notebook Aspect in your web browser or click on the Notebook Aspect link from the Rubin Science Platform homepage.

  2. If you are redirected to the GitHub login page, enter your GitHub account credentials. Once the log in is complete, your are redirected to the Server Options page.

Step 2: Select a machine image and size

The Server Options page lets you select the machine image and size that you’ll work in.


The machine images are based on LSST Science Pipelines Docker images, which are built on the CentOS 7 Linux operating system.

You can choose which version of the LSST Science Pipelines to run:

  • Recommended

  • Release (rX.Y)

  • Weekly releases (YYYY_WW)

  • Daily releases (YYYY_MM_DD)

The Recommended image is often the best choice because it has update-to-date versions of both the LSST Science Pipelines and supporting Jupyter and Python software, and is backed by additional testing.

For a scientific analysis work where reproducibility between Notebook Aspect sessions is important, you may wish to select a specific major or weekly release, and stick with that release for each log in. To continue using a specific release, you may need to select it from the historical image drop down.

For developing with the LSST Science Pipelines, we recommend using the latest daily release to ensure your work is compatible with current versions of Science Pipelines packages. See Tutorial: developing LSST Science Pipelines packages in the Notebook Aspect for more information.

Options: size

You can also choose your machine size from the Server Options page. Since the Rubin Science Platform is a shared resource, try to use the smallest machine possible so that resources are available to other users.

For most light-weight tasks, such as editing notebooks or analyzing small datasets, small-scale analyses, the Small images will be just fine.

For running data processing tasks with the LSST Science Pipelines, choose the Medium or Large images to ensure that datasets fit in RAM.

Options: Enable debug logs

Only select this option if requested by Rubin Observatory staff.

Options: Clear the .local directory

Only select this option if you are having difficulties starting the Notebook Aspect because Python packages or other software that you installed yourself are now incompatible with the image that you want to launch.

Start the notebook aspect

Click Start and to launch into the Notebook Aspect.

Next steps