  This paper contributes towards better understanding the energy consumption
trade-offs of HPC scale Artificial Intelligence (AI), and more specifically
Deep Learning (DL) algorithms. For this task we developed benchmark-tracker, a
benchmark tool to evaluate the speed and energy consumption of DL algorithms in
HPC environments. We exploited hardware counters and Python libraries to
collect energy information through software, which enabled us to instrument a
known AI benchmark tool, and to evaluate the energy consumption of numerous DL
algorithms and models. Through an experimental campaign, we show a case example
of the potential of benchmark-tracker to measure the computing speed and the
energy consumption for training and inference DL algorithms, and also the
potential of Benchmark-Tracker to help better understanding the energy behavior
of DL algorithms in HPC platforms. This work is a step forward to better
understand the energy consumption of Deep Learning in HPC, and it also
contributes with a new tool to help HPC DL developers to better balance the HPC
infrastructure in terms of speed and energy consumption.
