Platform teams that run AI workloads on Kubernetes often stitch together several components by hand: a job submission CLI, a queueing system like Kueue, a distributed runtime orchestrator such as KubeRay, GPU health monitoring, dashboards, and custom scripts that tie everything together. This manual assembly creates operational overhead, version drift, and longer time to value for researchers who just want to submit a training job without worrying about the underlying cluster details.
TauGrid solves this problem by delivering a single Helm install that bundles the tau CLI, Kueue admission control, KubeRay RayJob orchestration, node‑level GPU health checks, and an observability layer into one cohesive platform. After installing the chart from the Microsoft Container Registry on any Kubernetes 1.30+ cluster with GPU nodes, kubectl and Helm 3.0+, users define a workload in a simple tau.yaml file. Running tau run validates the file, resolves platform policy, renders the appropriate Kubernetes Job or KubeRay RayJob, and submits it through Kueue. The platform then handles queueing, execution on healthy GPUs, continuous monitoring, automatic recovery from failures with checkpoint resume, and the creation of an evidence record that captures configuration, logs, metrics and checkpoints for reproducibility and audit.
Key benefits include reduced integration effort, consistent policy enforcement across teams, transparent quota and priority management via shared Kueue ClusterQuotas, and built‑in reproducibility through evidence records. TauGrid is MIT licensed, ships container images and Helm charts as public OCI artifacts, and sends no telemetry by default while allowing operators to plug in their own observability backends.
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