Running ML Workloads on K8s Clusters

In this article, I explain how to run distributed machine learning workloads on a Kubernetes cluster using PyTorch’s Distributed Data Parallel (DDP) approach. DDP enables efficient multi-node training by replicating the model across processes on different nodes, each processing a unique subset of the training data. After computing gradients locally, DDP synchronizes them across all processes to maintain model consistency. It supports multiple communication backends like NCCL, Gloo, and MPI, allowing flexibility based on the cluster’s networking and hardware configuration.