  Edge devices have typically been used for DNN inferencing. The increase in
the compute power of accelerated edges is leading to their use in DNN training
also. As privacy becomes a concern on multi-tenant edge devices, Docker
containers provide a lightweight virtualization mechanism to sandbox models.
But their overheads for edge devices are not yet explored. In this work, we
study the impact of containerized DNN inference and training workloads on an
NVIDIA AGX Orin edge device and contrast it against bare metal execution on
running time, CPU, GPU and memory utilization, and energy consumption. Our
analysis shows that there are negligible containerization overheads for
individually running DNN training and inference workloads.
