Eidos (Eidos) provides validated configuration guidance for deploying GPU-accelerated Kubernetes infrastructure. It captures known-good combinations of software, configuration, and system requirements and makes them consumable as documentation and generated deployment artifacts.

Running GPU-accelerated Kubernetes clusters reliably is hard. Small differences in kernel versions, drivers, container runtimes, operators, and Kubernetes releases can cause failures that are difficult to diagnose and expensive to reproduce.

Historically, this knowledge has lived in internal validation pipelines, playbooks, and tribal knowledge. Eidos exists to externalize that experience. Its goal is to make validated configurations visible, repeatable, and reusable across environments.

Eidos is a source of validated configuration knowledge for NVIDIA-accelerated Kubernetes environments.

It is:

• A curated set of tested and validated component combinations
• A reference for how NVIDIA-accelerated Kubernetes clusters are expected to be configured
• A foundation for generating reproducible deployment artifacts
• Designed to integrate with existing provisioning, CI/CD, and GitOps workflows

It is not:

• A Kubernetes distribution
• A cluster provisioning or lifecycle management system
• A managed control plane or hosted service
• A replacement for cloud provider or OEM platforms

Eidos separates validated configuration knowledge from how that knowledge is consumed.

• Human-readable documentation lives under docs/.
• Version-locked configuration definitions (“recipes”) capture known-good system states.
• Those definitions can be rendered into concrete artifacts such as Helm values, Kubernetes manifests, or install scripts.- Recipes can be validated against actual system configurations to verify compatibility.

This separation allows the same validated configuration to be applied consistently across different environments and automation systems.

For example, a configuration validated for gb200 on Ubuntu 22.04 with Kubernetes 1.29 can be rendered into Helm values and manifests suitable for use in an existing GitOps pipeline.

Some tooling and APIs are under active development; documentation reflects current and near-term capabilities.

curl -sfL https://raw.githubusercontent.com/nvidia/eidos/refs/heads/main/install | bash -s --

See Installation Guide for manual installation, building from source, and container images.

Get started quickly with Eidos:

1. Review the documentation under docs/ to understand supported platforms and required components.
2. Identify your target environment:
   • GPU architecture
   • Operating system and kernel
   • Kubernetes distribution and version
   • Workload intent (for example, training or inference)
3. Apply the validated configuration guidance using your existing tools (Helm, kubectl, CI/CD, or GitOps).
4. Validate and iterate as platforms and workloads evolve.

These use cases reflect common ways teams interact with Eidos.

• api/ — OpenAPI specifications for the REST API
• cmd/ — Entry points for CLI (eidos) and API server (eidosd)
• deployments/ — Kubernetes manifests for agent deployment
• docs/ — User-facing documentation, guides, and architecture docs
• examples/ — Example snapshots, recipes, and comparisons
• infra/ — Infrastructure as code (Terraform) for deployments
• pkg/ — Core Go packages (collectors, recipe engine, bundlers, serializers)
• tools/ — Build scripts, E2E testing, and utilities

• Documentation – Documentation, guides, and examples.
• Roadmap – Feature priorities and development timeline
• Overview - Detailed system overview and glossary
• Security - Security-related resources
• Releases - Binaries, SBOMs, and other artifacts
• Issues - Bugs, feature requests, and questions

Contributions are welcome. See contributing for development setup, contribution guidelines, and the pull request process.