Training for Expanse Users

Training classes and workshops introduce new and current users to the high-performance computing, data, and visualization resources available at SDSC and provide the programming skills necessary to use SDSC resources effectively and efficiently. Workshops offer experienced users more in-depth instruction, including hands-on assistance with their own codes and collaborative discussions with other users and parallel computing experts.

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Upcoming

  • Feb162023

    Getting Started with Batch Job Scheduling: Slurm Edition

    Learn how to write your first batch job script and submit it to a Slurm batch job scheduler. We discuss best practices on how to structure your batch job scripts, teach you how to leverage Slurm environment variables, and provide you with some tips on how to request resources from the scheduler to get your work done faster. We also introduce you to some advanced features like Slurm job arrays and job dependencies for more structured computational workflows.

    This event will be held remotely.

  • Mar162023

    Data Management & File Systems

    More details coming soon...

    This event will be held remotely.

  • Apr202023

    Introduction to Neural Networks, Convolution Neural Networks and Deep Learning

    This webinar will be a quick introduction and overview of neural networks, convolution networks, and demonstration of executing deep learning models in an HPC environment.

    This event will be held remotely.

  • May182023

    Singularity Containers

    This webinar will be a quick introduction to show how to build Singularity images and then run them on the SDSC supercomputer clusters. The insider knowledge of best practices along with pitfalls to avoid while working with Singularity will also be shared.

    This event will be held remotely.

Training Catalog

Online training classes and workshops are recorded to enable the skills development necessary to use SDSC resources. The following list of previously recorded events offers users an opportunity to train at their own pace and schedule.