Accelerating Hyperparameter Optimisation with PyCOMPSs
Albert Njoroge Kahira, Leonardo Bautista-Gomez, Javier Conejero, Rosa M. Badia · 2019
Machine Learning applications now span across multiple domains due to the increase in computational power of modern systems. There has been a recent surge in Machine Learning applications in High Performance Computing (HPC) in an attempt to speed up training. However, besides training, hyperparameters optimisation(HPO) is one of the most time consuming and resource intensive parts in a Machine Learning Workflow. Numerous algorithms and tools exist to accelerate the process of finding the right parameters for a model. Most of these tools do not utilize the parallelism provided by modern systems and are serial or limited to a single node. The few ones that are offer distributed execution require a serious amount of programming effort.