  In the fusion community, the use of high performance computing (HPC) has been
mostly dominated by heavy-duty plasma simulations, such as those based on
particle-in-cell and gyrokinetic codes. However, there has been a growing
interest in applying machine learning for knowledge discovery on top of large
amounts of experimental data collected from fusion devices. In particular, deep
learning models are especially hungry for accelerated hardware, such as
graphics processing units (GPUs), and it is becoming more common to find those
models competing for the same resources that are used by simulation codes,
which can be either CPU- or GPU-bound. In this paper, we give examples of deep
learning models -- such as convolutional neural networks, recurrent neural
networks, and variational autoencoders -- that can be used for a variety of
tasks, including image processing, disruption prediction, and anomaly detection
on diagnostics data. In this context, we discuss how deep learning can go from
using a single GPU on a single node to using multiple GPUs across multiple
nodes in a large-scale HPC infrastructure.
