An Introduction to Docker Workshop

New

This course will provide an introduction to Docker.

Writing research software in Python presents numerous challenges to reproducibility - what version of Python is being used? What about the versions of PyTorch, Scikit Learn or Numpy? Should we use Conda, or venv, or Poetry to manage dependencies and environments? How can we control randomness? Do I have the right version of Cuda Toolkit? In principle, given the same data, and same algorithms and methodology, we should be able to reproduce the results of any given experiment to within an acceptable degree of error. Dealing with the above questions introduces significant problems to reproducing experiments in machine learning. This workshop will explore the use of Docker to help alleviate almost all of these questions. Furthermore, combining Docker, git and GitHub can be a powerful workflow, helping to minimise your tech stack, and declutter your python development experience.

Postgraduate students and research staff

Familiarity with Python and some knowledge of machine learning and neural networks is required

Number of sessions: 1

Presentations, demonstrations, group discussion and practicals

Python will need to be downloaded prior to the course

Refreshments will be provided, please add any dietary requirements to the special requirements section.

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Half day course

Once or twice a term

• AI and Large Language Models Workshop
• An Introduction to Diffusion Models in Generative AI
• Hands On AI Workshop
• Packaging and Publishing Python Code for Research workshop
• LLM Hands on Workshop