Best Practices in AI Afternoon

5 July 2024 - 12:00-18:00
Workroom 2, 38 Mappin, Sheffield, S1 4DT and Online
Various, RSE, CMI

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Best Practices in AI Afternoon Banner


We are excited to present Best Practices in AI Afternoon which will be held on the 5th of July, 12-6pm at Workroom 2, 38 Mappin, Sheffield, S1 4DT and online.

The afternoon will consist of talks and walkthroughs on best practices for research, design, development, and deployment of AI systems with guest speakers from Nvidia, University of Cambridge and University of Bristol. A focus on practical aspects such as tooling, optimisation, profiling, tips and tricks to supercharge AI in your research!

Buffet lunch and coffee will be provided, with a drinks reception sponsored by Nvidia after the event.

This event is held in collaboration between the Research Software Engineering (RSE) group and the Centre for Machine Intelligence (CMI) in the University of Sheffield.


1:1 meetings with Nvidia
Book a 1 to 1 meeting with Nvidia experts on the day! For details, see below.
Networking Lunch and registration
Buffet lunch, soft drinks and coffee.
Welcome & Housekeeping
Maximizing Efficiency in Large Language Models: Compute, Memory, and Fine-Tuning
SpeakerKarin Sevegnani, Senior Solutions Architect, Nvidia
In this talk, we will explore the intricate balance between computational resources, memory limitations, and parameter-efficient fine-tuning techniques in large language models (LLMs). We will analyse strategies to optimize the performance of LLMs while managing these constraints effectively. From efficient memory utilization to streamlined parameter fine-tuning methods, we will discuss practical approaches to maximize the efficiency of LLMs without sacrificing performance.
Docker for Machine Learning
SpeakerRyan Daniels, University of Cambridge
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. In this talk, I would like to convince you that Docker can 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.
How do you unit test an ML model?
SpeakerWahab Kawafi, University of Bristol (Remote speaker)
Covering methods such as mock testing, simulation, experiment tracking, and dataset curation. With examples in medicine, chemistry, aerospace engineering, and LLMs.
Coffee break
Lightning talk 1: Research Software Engineering
SpeakerTwin Karmakharm, Research Software Engineering, University of Sheffield
Introduction to the RSE team and how they can help support your project.
Lightning talk 2: AMRC Standardised Data-Centric Manufacturing Workflow
SpeakerLindsay Lee, AMRC, University of Sheffield
We have created a standardised workflow for data-driven projects at the AMRC including a Github site for consistent documentation, data management and presentation and a wiki with supporting documentation. These are based on the Microsoft Team Data Science Process and CookieCutter as well as our own experiences and existing processes.
Lightning talk 3: Nvidia Self-paced Training Courses
SpeakerDenis Battistella, Higher Education and Research, Nvidia
How to make your machine learning code faster
SpeakerEdwin Brown, Research Software Engineering, University of Sheffield
Practical guide to profile machine learning code to find bottlenecks and to remove these bottlenecks.
From Research Software to Software as a Service
SpeakerRobert (Bob) Turner, Research Software Engineer, University of Oxford
A workshop session based on a recent blog post I wrote. A mind-mapping type approach with lots of audience engagement, not a traditional presentation, will be used to explore the things that need to be considered when making Software as a Service based on research software. We will discuss how ideas in the post might be applied to AI.
Q&A Panel
Wrap up
Drinks reception sponsored by Nvidia
Alcoholic, soft drinks and nibbles.

Speaker Profiles

Karin Sevegnani, Senior Solutions Architect, Nvidia

Karin is a Senior Solutions Architect at NVIDIA, with a specific focus on the Higher Education and Research (HER) industry in the UK. At NVIDIA, she’s leading collaborative efforts with the NVAITC initiative, particularly centred around Isambard-AI. Prior to joining NVIDIA, Karin was a research engineer at Alana AI, where she specialized in Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) systems. Her primary research interests include exploring topic transitions within conversational systems and devising effective strategies for recommendation settings. Karin holds a Ph.D. in Natural Language Processing from Heriot-Watt University, UK, which she completed in July 2023. During her doctoral studies, she focused on advancing conversational AI and recommendation systems. Notably, she contributed to Heriot-Watt’s participation in the Amazon Alexa Prize 2018 competition. Additionally, Karin gained valuable industry experience through an internship at Amazon in 2021, where she worked as an applied scientist on recommendation algorithms.

Ryan Daniels, Machine Learning Engineer, University of Cambridge

Ryan is a machine learning engineer at the Accelerate Programme for Scientific Discovery and is interested in driving forward scientific research which is grounded in excellent software engineering and machine learning fundamentals. Before working at Accelerate, Ryan’s research interests explored unconventional approaches to computing using complex physical devices from the world of condensed matter physics.

Wahab Kawafi, AI Supercomputing Infrastructure Engineer, University of Bristol

Wahab finished their PhD in machine learning from the University of Bristol on computer vision applications in biomedicine and material science. They worked as a visiting researcher at the European Space Agency analysing motion capture data of stroke-survivors. More recently working as a machine learning engineer in robotics and aerospace applications. He joined the Isambard AI team in April 2024.

Edwin Brown, Research Software Engineer, University of Sheffield

Edwin joined the RSE team in October 2022. He comes from a background in geophysics following a BSc and MSc in Geophysical Sciences at the University of Leeds. After university, he worked in the private sector, developing machine learning (ML) workflows to solve geophysical imaging and inversion problems.

Edwin has practical experience in the designing, training and evaluation of ML models. He is experienced in Python having worked with data science libraries such as Numpy, Pandas, Scikit-learn, Tensorflow and Keras. He has a growing interest in MLOps (Machine Learning Operations) and the practical challenges of scaling up ML practices.

Robert (Bob) Turner, Research Software Engineer, University of Oxford

Bob works in the Modernising Medical Microbiology group in the Nuffield Department of Medicine on software as a service platforms for infectious disease diagnostics, public health and research applications.

He has collaborated with a wide range of academic, public and private sector partners including GPAS, AirQo, Institut Pasteur, the UK Health Security Agency and Health Data Research UK. Bob has been a maintainer and a reviewer for The Carpentries, and has contributed to more than 20 peer-reviewed publications, including one in Nature. He is currently interested in technologies and working practises that result in useful research software.

Book a 1 to 1 with Nvidia

Book a 15 to 30 minutes 1 to 1 meeting with Nvidia experts on the day! They are open to discuss your research project at any stage whether you are already familiar and use accelerated computing looking to optimise or scale your project or whether you are relatively new and want to explore how to use AI/HPC in your project. Example discussion topics include:

  • Possible approaches and techniques for your research problem/domain
  • Available training courses
  • Which GPU to use for your workload
  • Generative AI and LLM related topics

Nvidia experts on the day:

  • Andy Grant, EMEA Director for Supercomputing and AI at Nvidia
  • Denis Battistella, Higher Education and Research at Nvidia
  • Karin Sevegnani, Senior Solutions Architect at Nvidia

In-person meetings will take place on the day and slots are limited and are first come, first served. Remote meetings (may not happen on the day) will be offered once we’ve run out of meeting slots.

Participation and feedback

Contact Us

For queries relating to collaborating with the RSE team on projects:

Information and access to JADE II and Bede.

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Queries regarding free research computing support/guidance should be raised via our Code clinic or directed to the University IT helpdesk.