Metafaas architecture for training on serverless instances

Abstract

Hardly any work in literature attempts employing Function-as-a-Service (FaaS) or serverless architecture to accelerate the training or re-training process of meta-learning architectures. Embodiments of the present disclosure provide a method and system for meta learning using distributed training on serverless architecture. The system, interchangeably referred to as MetaFaaS, is a meta-learning based scalable architecture using serverless distributed setup. Hierarchical nature of gradient based architectures is leveraged to facilitate distributed training on the serverless architecture. Further, a compute-efficient architecture, efficient Adaptive Learning of hyperparameters for Fast Adaptation (eALFA) for meta-learning is provided. The serverless architecture based training of models during meta learning enables unlimited scalability and reduction of training time by using optimal number of serverless instances. An analytical model for gradient based meta learning architectures that predicts training time required for the number of FaaS instances is provided which further enables estimating the cost incurred during training models in meta-learning.