Artificial Intelligence/Machine Learning Benchmark
Objective
To create a self-contained AI Benchmark/workflow in the domain of synthetic brain imaging.
Summary of work undertaken
Training epochs were run for three provided model configurations on the UCL AI platform on both a single GPU and multiple GPU devices. Several multiple day runs of ~100 epochs were run. As training scripts were configured to run for 100,000 epochs and each epoch takes around an hour or more to run, ‘full’ runs of the model were not performed.
Python requirements and the package associated with the code were installed on the Cambridge HPC service (following the same set up process documented in the repository README below).
Outputs
A Public GitHub repository containing the open-source (GPL v3) release of the research code developed by Kings College London. The GitHub repository (https://github.com/r-gray/3d_very_deep_vae) has a GPL v3 license file included.
The README file in the repository contains full details of how to install the dependencies and Python package, and includes platform dependent requirement specifications with pinned versions for the support operating system and Python version combinations. There is also documentation on how to run the model training with the example configurations provided.
Development of an Artificial Intelligence-Machine Learning Benchmark – Final Report
