  Machine Learning (ML) and Deep Learning (DL) are two technologies used to
extract representations of the data for a specific purpose. ML algorithms take
a set of data as input to generate one or several predictions. To define the
final version of one model, usually there is an initial step devoted to train
the algorithm (get the right final values of the parameters of the model).
There are several techniques, from supervised learning to reinforcement
learning, which have different requirements. On the market, there are some
frameworks or APIs that reduce the effort for designing a new ML model. In this
report, using the benchmark DLBENCH, we will analyse the performance and the
execution modes of some well-known ML frameworks on the Finis Terrae II
supercomputer when supervised learning is used. The report will show that
placement of data and allocated hardware can have a large influence on the
final timeto-solution.
