Authors: Daniel Vega-Myhre (Google), Abdullah Gharaibeh (Google), Kevin Hannon (Red Hat)
In this article, we introduce JobSet, an open source API for representing distributed jobs. The goal of JobSet is to provide a unified API for distributed ML training and HPC workloads on Kubernetes.
Why JobSet? The Kubernetes community’s recent enhancements to the batch ecosystem on Kubernetes has attracted ML engineers who have found it to be a natural fit for the requirements of running distributed training workloads.

Kubernetes is an open-source container orchestration platform for automating the deployment, scaling, and management of containerized applications. With Kubernetes, developers can deploy applications across clusters of machines, ensuring high availability, scalability, and resilience. Developers can learn about Kubernetes' architecture, core concepts, and deployment strategies, and gain hands-on experience with managing containerized workloads using Kubernetes.

Kubernetes

JobSet is a new open source API designed to streamline the management of distributed ML training and high-performance computing (HPC) workloads on Kubernetes. It addresses limitations in existing Kubernetes jobs by allowing for features like multi-template pods, job groups, inter-pod communication, and startup sequencing. JobSet models workloads as groups of Kubernetes jobs, enhancing scheduling and lifecycle management. Key features include replicated jobs, automatic headless service creation, configurable success and failure policies, and integration with Kueue for efficient capacity management.

Introducing JobSet