INFaaS: Managed & Model-less Inference Serving

The number of applications relying on inference from machine learning models is already large and expected to keep growing. For instance, Facebook applications issue tens-of-trillions of inference queries per day with varying performance, accuracy, and cost constraints. Unfortunately, existing inference serving systems are neither easy to use nor cost effective. Developers must manually match the performance, accuracy, and cost constraints of their applications to a large design space that includes decisions such as selecting the right model and model optimizations, selecting the right hardware architecture, selecting the right scale-out factor, and avoiding cold-start effects. These interacting decisions are difficult to make, especially when the application load varies over time, applications evolve over time, and the available resources vary over time. We present INFaaS, an inference-as-a-service system that abstracts resource management and model selection. Users simply specify their inference task along with any performance and accuracy requirements for queries. Given the currently available resources, INFaaS automatically selects and serves inference queries using a specific model that satisfies these requirements. INFaaS autoscales resources as model load changes both within and across inference workers. It also shares workers across users and models to increase utilization. We evaluate INFaaS using 44 model architectures and their 270 model variants against serving systems rely on users for model se push model variant section and pre-load models, fix the scale policy, or use dedicated hardware resources. Our evaluation on realistic workloads shows that INFaaS achieves 2$\times$ higher throughput and violates latency SLO goals 3$\times$ less frequently, while maintaining high utilization and having overheads that are less than 12% of millisecond-scale queries.