  Edge inference is becoming ever prevalent through its applications from
retail to wearable technology. Clusters of networked resource-constrained edge
devices are becoming common, yet there is no production-ready orchestration
system for deploying deep learning models over such edge networks which adopts
the robustness and scalability of the cloud. We present SEIFER, a framework
utilizing a standalone Kubernetes cluster to partition a given DNN and place
these partitions in a distributed manner across an edge network, with the goal
of maximizing inference throughput. The system is node fault-tolerant and
automatically updates deployments based on updates to the model's version. We
provide a preliminary evaluation of a partitioning and placement algorithm that
works within this framework, and show that we can improve the inference
pipeline throughput by 200% by utilizing sufficient numbers of
resource-constrained nodes. We have implemented SEIFER in open-source software
that is publicly available to the research community.
