May 12, 2021 - by CSCS

In this tutorial we discussed how to use the JupyterHub service on Piz Daint for data analysis, visualization and rapid prototyping of code.

In the first part we showed how to install Python modules, use virtual environments and install custom kernels into your JupyterLab or notebook environment. We discussed dask, ipyparallel and mpi4py, and demonstrated NVIDIA's GPU dashboards for basic profiling of GPU code.

The second part was focused on the use of ParaView in Jupyter notebooks, covering topics including migrating from ParaView desktop to ParaView in Jupyter, SMP- and MPI-based parallellism in ParaView within a notebook, and dask, numpy and xarray in ParaView.

The final part covered the Julia language, providing an overview of the Julia package ecosystem and an introduction to GPU programming with Julia.

Here you can watch the videos of the tutorial "Interactive Computing with Jupyter on Piz Daint, using Python, ParaView and Julia" >