Scalable and Highly Available Multi-Objective Neural Architecture Search in Bare Metal Kubernetes Cluster

The interest in deep neural networks for solving computer vision task has dramatically increased. Due to the heavy influence of the neural networks architecture on its predictive accuracy, neural architecture search has gained much attention in recent years. This research area typically implies a high computational burden and thus, requires high scalability as well as availability to ensure no data loss or waist of computational power. Moreover, the thinking of developing applications has changed from monolithic once to microservices. Hence, we developed a highly scalable and available multi-objective neural architecture search and adopted to the modern thinking of developing application by subdividing an already existing, monolithic neural architecture search - based on a genetic algorithm - into microservices. Furthermore, we adopted the initial population creation by 1,000 mutations of each individual, extended the approach by inception layers, implemented it as island model to facilitate scalability and achieved on MNIST, Fashion-MNIST and CIFAR-10 dataset 99.75%, 94.35% and 89.90% test accuracy respectively. Besides, our model is strongly focused on high availability empowered by the deployment in our bare-metal Kubernetes cluster. Our results show that the introduced multi-objective neural architecture search can easily handle even the loss of nodes and proceed the algorithm within seconds on another node without any loss of results or the necessity of human interaction.