Jupyter Notebook and Ecosystem

• Hans Fangohr (European XFEL GmbH)

Room: Williamsburg room A brief introduction to the Jupyter Notebook and the ecosystem of tools that are based on it. Includes JupyterHub, JupyterLab, NBDIME, NBVAL, Binder.

Status updates from Soleil, CERN, European Southern Observatory, J-PARC MLF, Max IV Room: Williamsburg room

JupyterLab Tutorial

• Saul Shanabrook (Quansight)

Room: Williamsburg room JupyterLab is the next generation of the notebook interface. This talk will start with an overview of the JupyterLab interface to show how it can be used for more than just editing notebooks. JupyterLab was designed with extensibility in mind, so next I will show how to create a plugin that adds custom functionality to the interface. Then I will show some existing extensions that target the scientific ecosystem, including support for exploring HDF5 files. Finally, I will end with an exploration of new features coming to JupyterLab including real time collaboration.

Jupyter for processing neutron event data at ESS

• Jonathan Taylor (European Spallation Source)

Room: Williamsburg room

Jupyter at Brookhaven National Laboratory

• Daniel B. Allan (Brookhaven National Lab)

Room: Williamsburg room

Jupyter for Accelerator Physics

• Jonathan Edelen (RadiaSoft LLC)

Room: Williamsburg room RadiaSoft operates a public JupyterHub instance at jupyter.radiasoft.org, which is used for workshops, the US Particle Accelerator School (USPAS), our clients, and the accelerator community at-large. This free, public service simplifies classroom startup at USPAS, because all the accelerator codes and tools they might need are already installed. We deliver models to our clients via IPython notebooks who can run them instantly on JupyterHub. We make it easy for instructors to share notebooks during a workshop with all their students. Additionally, accelerator physicists at RadiaSoft use Jupyter as an “IDE” for developing models and running simulations. Most of the codes we use are Python-wrapped so IPython notebooks are an ideal tool for documenting the parameters associated with a model. RadiaSoft’s Jupyter Docker image contains most of the key accelerator physics codes including elegant, EPICS, FBPIC, JSPEC, OPAL, Radia, Shadow3, SRW, Synergia, Warp, Zgoubi as well as support tools such as SciPy, Tensorflow, and TeX. The image is large (10GB), but allows our staff, clients, and the public to work with all codes simultaneously and seamlessly. In order to facilitate the various use cases, we have implemented and open-sourced some subclasses and tools. RSDockerSpawner supports resource pools that allows us to manage our public, workshop, and private Jupyter instances on a single cluster. An MPI wrapper allows simple and secure container-based execution of MPI-based codes by our staff. Users can also persist their Jupyter environment customizations so that they survive container restarts. Here we will provide an overview of our capabilities and detail some example use cases for the simulation of particle accelerators.