ICCS Summer School 2023 - Resources and pre-reading

Contents of this page:

Programme

Handbook

ICCS RSE Open Office Hours

Through the summer school, attendees can book a session with one of the ICCS RSEs for advice or to discuss ongoing projects.

Hackathon

Pre-reading

Watch this introduction video to find out more! If you would like to suggest a project idea, please use the team hackathon ideas/pitches form to propose your project idea.

Hackathon sign-up

Using GitHub effectively for collaborative development

Pre-reading

Requisites: some familiarity with Git.

Last years’ training covers the theory behind using Git as presented at the ICCS summer school last year. The session this year will greatly expand on the last few minutes of that presentation.

Resources

• Example repository
• Worksheet
• There was a mention of choosealicense.com for information on software licenses.

Panel on the importance of software engineering goodpractices in climate science

A few resources that were mentioned during the panel:

• Regarding promoting diversity:
  • Understanding Equity, Diversity and Inclusion Challenges Within the Research Software Community, Chue Hong, N.P., Cohen, J., Jay, C. (2021)
  • N8CIR Recruitment Diversity Checklist
• Regarding reviewing of code in peer review context:
  • ACM Artifact Review and Badging

Introduction to GPU Programming

Prerequisite for hands-on lab: Basic understanding of the C programming language.

• Some knowledge of C programming

Resources

• Slides - Architectures Overview
• Slides - Introduction to CUDA
• Slides - Optimisation
• InstanceHub.com
• CUDA Hello World
• CUDA Lab One
• CUDA Lab Two

Introduction to Machine Learning with Pytorch

Pre-reading

To make the most of the session we expect participants to arrive with a (minimal) base-level understanding of machine learning concepts. In addition to this we will also assume knowledge of some basic mathematics and python abilities.

Details of these can be found on the workshop GitHub repository.

Slides

The slides for the course can be found at the following locations:

• Teaching
• Applications

Teaching material

The teaching material (including exercises as jupyter notebooks) can be found on the workshop GitHub repository alongside installation instructions.

Probabilistic Machine Learning - From Bayesian Linear Regression to Gaussian Processes

Colab notebooks:

• intro_to_gps.ipynb
• regression.ipynb
• intro_to_kernels.ipynb
• spatial.ipynb

Or setup locally:

# local instructions
git clone https://github.com/JaxGaussianProcesses/GPJax
cd GPJax
python -m venv venv
source venv/bin/activate
pip install poetry jupyterlab
poetry install --with docs
jupytext --to notebook docs/examples/*.py
cd docs/examples
jupyter notebook

Introduction to Hugging Face

Worksheet