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There is an ever-increasing need for computational power to train complexartificial intelligence (AI) & machine learning (ML) models to tackle largescientific problems. High performance computing (HPC) resources are required toefficiently compute and scale complex models across tens of thousands ofcompute nodes. In this paper, we discuss the issues associated with thedeployment of machine learning frameworks on large scale secure HPC systems andhow we successfully deployed a standard machine learning framework on a securelarge scale HPC production system, to train a complex three-dimensionalconvolutional GAN (3DGAN), with petaflop performance. 3DGAN is an example fromthe high energy physics domain, designed to simulate the energy patternproduced by showers of secondary particles inside a particle detector onvarious HPC systems.

Deploying Scientific AI Networks at Petaflop Scale on Secure Large Scale HPC Production Systems with Containers