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. There is, therefore, a need for a tool/scheme that can scale and leverage HPC infrastructures such as supercomputers, with minimum programmers effort and little or no overhead in performance. We present a HPO scheme built on top of PyCOMPSs, a programming model and runtime which aims to ease the development of parallel applications for distributed infrastructures. We show that PyCOMPSs is a powerful framework that can accelerate the process of Hyperparameter Optimisation across multiple devices and computing units. We also show that PyCOMPSs provides easy programmability, seamless distribution and scalability, key features missing in existing tools. Furthermore, we perform a detailed performance analysis showing different configurations to demonstrate the effectiveness our approach.

Peer Reviewed