What is TauGrid?

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TauGrid is an open, self-hosted AI compute platform for running AI workloads on Kubernetes. It brings the experience teams expect from a managed AI platform (simple submission, elastic distributed compute, shared GPU capacity, experiment visibility, and operational controls) to clusters they operate in their own cloud or datacenter.

Researchers work from a repository and the Tau CLI. Platform teams provide governed workspaces, queues, compute profiles, storage, identity, and observability. TauGrid connects those two experiences across training, fine-tuning, batch inference, and serving.

Architecture

Mon, 01 Jan 0001 00:00:00 +0000

TauGrid is a CLI, renderer, and local observer. Kueue, KubeRay, and the Kubernetes scheduler continue to own quota admission, Ray orchestration, and pod scheduling.

Layer	Responsibility
Research repository	Entrypoint, image, dependencies, resource intent, data, and outputs
TauGrid	Resolve, validate, render, submit, observe, and preserve workflow evidence
Workspace/platform	Cluster access, namespace, queue, priority, output root, identity, and shared policy
Kueue	Quota reservation, admission, priority, and preemption
Kubernetes/KubeRay	Pod scheduling and Job/Ray lifecycle
Project process	Training, evaluation, inference, and model-specific behavior

Code boundaries

cli/cmd: thin Cobra command wiring for the tau binary.
cli/internal: reusable capability packages behind tau.
core: the shared library module both binaries link.
portal: a separate module and binary, taugrid-portal, that hosts experiment tracking (Stellar) and the observability portal.
sdk/python: optional Python authoring APIs that delegate execution to the Go CLI.
controllers/tau-core: the separately deployed controller. One manager runs three reconcilers – TauCluster, TauWorkspace, and TauQuotaRequest.

The visual architecture guide maps the main packages and extension points.

Getting started on TauGrid

What is TauGrid?

Architecture

Code boundaries