Deploying Scientific AI Networks at Petaflop Scale on Secure Large Scale HPC Production Systems with Containers
arXiv:2005.10676
Abstract
There is an ever-increasing need for computational power to train complex artificial intelligence (AI) & machine learning (ML) models to tackle large scientific problems. High performance computing (HPC) resources are required to efficiently compute and scale complex models across tens of thousands of compute nodes. In this paper, we discuss the issues associated with the deployment of machine learning frameworks on large scale secure HPC systems and how we successfully deployed a standard machine learning framework on a secure large scale HPC production system, to train a complex three-dimensional convolutional GAN (3DGAN), with petaflop performance. 3DGAN is an example from the high energy physics domain, designed to simulate the energy pattern produced by showers of secondary particles inside a particle detector on various HPC systems.
arXiv commentThe Platform for Advanced Scientific Computing (PASC) Conference 2020. arXiv admin note: text overlap with arXiv:1905.10090
References in corpus (5)
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Conditional Image Synthesis With Auxiliary Classifier GANs
- Horovod: fast and easy distributed deep learning in TensorFlow
- Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets
- Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train