Aidan Scannell

TL;DR Run your notebooks fast using GPU.

TL;DR Use python packages in editable mode with setup.py and pip install -e .

TL;DR ’s makes it easy to submit SLURM jobs Easily sweep over multiple random seeds Easily sweep over different combinations of hyperparameters Hydra submitit launcher ’s provides a launcher based on .

TL;DR Use the inside interactive jobs (on GPU) Check you have GPU PyTorch/JAX/TensorFlow Debugging on a cluster Start a short (5 minute) interactive job (on GPU) srun --mem=32gb --gres=gpu:1 -p gpu --time=0:05:00 --pty zsh Run code on GPU with python deb…

TL;DR handles installing PyTorch with CUDA environment.yaml This is a minimal conda environment config environment.yaml which will install PyTorch with CUDA 11.8 name: ENV_NAME channels: - nvidia - pytorch - conda-forge dependencies: - pytorch=2.0 -…

TL;DR Don’t enter your password every time you SSH into a cluster Configure SSH Add your cluster login details to your ssh config ~/.ssh/config so that you can log in with shh CLUSTER_NAME Importantly, you should not need to enter your password.

I am in the process of creating Jupyter notebooks for several probabilistic models and approximate inference algorithms.

In this work I reimplemented the PILCO algorithm (originally written in MATLAB) in Python using Tensorflow and GPflow.

This work implements and compares a variety of approximate inference techniques for the tasks of image de-noising (restoration) and image segmentation.

During the first (taught) year of the FARSCOPE CDT program I conducted my masters thesis under the supervision of Professor Weiru Liu and Dr Kevin McAreavey titled “Extending BDI Agents to Model and Reason with Uncertainty”.

As part of the FARSCOPE CDT program I worked in a team to develop a solution to Amazon’s picking challenge.

I undertook this project to strengthen my C++ and to gain a deeper understanding of the difficulties of implementing autonomous capabilities.

This project entailed the design of an ultrasonic phased array for operation into the human body using Matlab.

This project involved developing algorithms capable of localising a robot within a known environment but at an unknown position and moving it to a target location.

This project involved developing distributed software enabling a swarm of fixed wing UAVs to track a pollutant cloud.