LMB 2023/07 Machine Learning GPU Cluster

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Machine Learning, also known as Artificial Intelligence, is a powerful new computational method, that has recently shown itself to be extremely useful in Biological research. A prominent example is AlphaFold-2; a tool for predicting protein structures based on primary sequence. This tool so much improved on previous computational methods for predicting protein structures, that it can be genuinely termed groundbreaking. Other structural biological problems, such as the prediction of protein-protein interactions have similarly benefited from the application of machine learning tools, as too have related problems, such as automated atomic modelling in cryo-EM maps. Machine Learning is not restricted to solving structural problems, it can extract phylogenetically useful patterns from amino acid or nucleotide sequences, and even predict protein function from similar data. Aside from sequence based data, machine learning can effectively analyse images. For example, machine learning can trace the path of neural axons through a 3D stacks of images. This will make the task of generating a complete wiring map of a vertebrate brain finally possible - it would otherwise take 1000's of human work years to complete. Similarly, machine learning can be used to analyse timecourse photos of embryo development, and track both individual cells, and cell lineages. The LMB is both a user, and developer of Machine Learning methods. As Machine Learning is an emergent, rapidly developing technology, we require a small, state of the art machine learning cluster. We propose to invest in two of the latest generation of Machine learning system, each with eight Nvidia H100 80GB SXM GPUs, interlinked with 200Gb ethernet.