Hyperparameter optimization of data-driven AI models on HPC systems

Understand

In the European Center of Excellence in Exascale computing "Research on AI- and Simulation-Based Engineering at Exascale" (CoE RAISE), researchers develop novel, scalable AI technologies towards Exascale.

• This work exercises High Performance Computing resources to perform large-scale hyperparameter optimization using distributed training on multiple compute nodes.
• This is part of RAISE's work on data-driven use cases which leverages AI- and HPC cross-methods developed within the project.
• In response to the demand for parallelizable and resource efficient hyperparameter optimization methods, advanced hyperparameter search algorithms are benchmarked and compared.