Scaling Distributed Machine Learning Systems with Kubernetes and Asynchronous Stochastic Gradient Descent
Introduction Training modern deep‑learning models often requires hundreds of gigabytes of data and billions of parameters. A single GPU can no longer finish the job in a reasonable time, so practitioners turn to distributed training. While data‑parallel synchronous training has become the de‑facto standard, asynchronous stochastic gradient descent (ASGD) offers compelling advantages in elasticity, fault tolerance, and hardware utilization—especially in heterogeneous or spot‑instance environments. At the same time, Kubernetes has emerged as the leading platform for orchestrating containerized workloads at scale. Its declarative API, built‑in service discovery, and robust auto‑scaling capabilities make it an ideal substrate for running large‑scale ML clusters. ...