AMLA: an AutoML frAmework for Neural Network Design

Abstract

AMLA is an Automatic Machine Learning frAmework for implementing and deploying neural architecture search algorithms. Neural architecture search algorithms are AutoML algorithms whose goal is to generate optimal neural network structures for a given task. AMLA is designed to deploy these algorithms at scale and allow comparison of the performance of the networks generated by different AutoML algorithms. Its key architectural features are the decoupling of the network generation from the network evaluation, support for network instrumentation, open model specification and a microservices based architecture for deployment at scale. In AMLA, AutoML algorithms and training/evaluation code are written as containerized microservices that can be deployed at scale on a public or private infrastructure. The microservices communicate via well defined interfaces and models are persisted using standard model definition formats, allowing the plug and play of the AutoML algorithms as well as the AI/ML libraries. This makes it easy to prototype, compare, benchmark and deploy autoML algorithms in production. AMLA is currently being used to deploy an AutoML algorithm that generates Convolutional Neural Networks (CNNs) used for image classification.

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Reproductions