System and method for recommending an optimal virtual machine (vm) instance

Abstract

This disclosure relates generally to recommending an optimal VM instance. The increased use of Deep Learning (DL) models in several domains has resulted in an increased demand for hardware configurations to enable heavy computations and faster performance to support the DL techniques. However, the identification of the optimal hardware configuration for the DL requirement is challenging and requires a considerable amount of time and expertise, considering the highly configurable model configuration of DL techniques. The disclosed optimal selection of VM comprises several techniques including benchmarking, using benchmarked results for building an approximation function and use a Bayesian Optimizer (BO) technique to iterate through the search space and generate recommendations of VM configurations, that effectively address the challenges arising due to the dynamic nature of cloud services—pricing and hardware configuration, large number of VM available across regions and cloud service providers and estimating for different types of training code.