Accelerate Programme for Scientific Discovery course timetable
Monday 2 December
| 09:30 |
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. |
| 13:30 |
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. |
Wednesday 4 December
| 09:00 |
Hands On AI Workshop
[Full]
We know that when you’re learning AI & ML, a mix of classroom theory and hands-on practice is the best way to learn. So, we’re running a 1-day hands-on ML workshop to help you apply and develop further practical ML skills. During this workshop, you’ll work in teams on a real dataset of your choice, with support from Accelerate Science Machine Learning Engineers and researchers. You’ll need to work on building, tuning and evaluating ML models for your chosen dataset - we have some dataset ideas to kick you off, or you can bring your own. This is an opportunity to work on real-life ML problems and data, and gain confidence in using tools that you can take back to your own domain and research project. This workshop is for people who are already confident with both ML fundamentals and Python programming. This isn’t a Python or ML introduction day - you’ll spend most of the day programming! We’ll use open-source libraries including HuggingFace and scikit-learn, so please come with a laptop and be prepared to get coding, before presenting your results to the group at the end of the day. |
| 13:30 |
Hands On AI Workshop
[Full]
We know that when you’re learning AI & ML, a mix of classroom theory and hands-on practice is the best way to learn. So, we’re running a 1-day hands-on ML workshop to help you apply and develop further practical ML skills. During this workshop, you’ll work in teams on a real dataset of your choice, with support from Accelerate Science Machine Learning Engineers and researchers. You’ll need to work on building, tuning and evaluating ML models for your chosen dataset - we have some dataset ideas to kick you off, or you can bring your own. This is an opportunity to work on real-life ML problems and data, and gain confidence in using tools that you can take back to your own domain and research project. This workshop is for people who are already confident with both ML fundamentals and Python programming. This isn’t a Python or ML introduction day - you’ll spend most of the day programming! We’ll use open-source libraries including HuggingFace and scikit-learn, so please come with a laptop and be prepared to get coding, before presenting your results to the group at the end of the day. |