crater

Crater

A Kubernetes-native control plane for shared AI computing clusters

Operate shared GPU clusters, LLM training and serving workloads, developer environments, and data/model assets across research, education, and enterprise teams.

English · 简体中文

Documentation · Helm Chart · Backend · Frontend · CLI

🏢 Multi-Tenant Governance  ·  ⚙️ Policy-Aware Scheduling  ·  🚀 LLM Training & Serving  ·  🧩 Heterogeneous Accelerators  ·  🤖 AI-Assisted Operations

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• Overview
• Why Crater
• Designed For
• Features
• Architecture
• Getting Started
• Documentation
• Repository Structure
• Community & Support
• Contributing
• License

✨ Overview

Crater is a Kubernetes-native platform for operating shared AI computing clusters. It helps organizations manage heterogeneous compute resources, submit and govern AI workloads, quickly deploy large-model training and inference environments, and observe cluster health from a unified web console, CLI, and AI-assisted operations interface.

Crater is designed for environments where different teams and workloads share the same GPU cluster: long-running training jobs, bursty lab workloads, interactive notebooks, online AI services, LLM inference services, and offline data processing pipelines. It builds an operational control plane on top of Kubernetes and Volcano, connecting users, accounts, queues, quotas, images, datasets, models, jobs, services, and observability into one workflow.

🧪 Interactive Development — Jupyter, WebIDE, terminals, and external access	🚀 AI Workloads — training, serving, templates, and batch jobs
📈 Monitoring — real-time metrics & logs	📦 Models & Datasets — manage assets in one place

💡 Why Crater

Kubernetes and Volcano provide powerful low-level scheduling, but operating a shared GPU cluster for many teams still requires a lot of glue. Crater fills that gap:

Without a control plane	With Crater
Raw kubectl / YAML access, easy to misuse	Web console, CLI, and APIs with role-based, multi-tenant access
GPU usage is hard to attribute and bound	Accounts, queues, quotas, approvals, and cost visibility
Everyone rebuilds training/serving manifests by hand	Reusable job templates and one-click LLM deployment
Datasets, models, and images scattered across nodes	Managed datasets, models, images, and shared storage
Operators and users debug from different tools	Unified metrics, logs, GPU analysis, and AI-assisted operations

🌐 Designed For

Crater fits shared AI computing environments in universities, research institutes, enterprise AI teams, and internal platform teams.

Scenario	Typical workloads	What Crater provides
Research & engineering	Model fine-tuning, simulation, scientific computing, large experiments	Long-running GPU jobs, reusable environments, data/model mounting, logs, monitoring, and lifecycle controls
Teaching & training	Course labs, student projects, virtual experiments, workshops	Account and quota management, job templates, burst handling, fair access, and simple web-based submission
LLM training & serving	Fine-tuning, evaluation, inference endpoints, model demos, mixed training/serving clusters	Fast deployment templates, GPU-aware placement, data/model assets, service access, and train/serve resource governance
Enterprise AI services	Internal assistants, document intelligence, multimodal services, inference backends	Managed runtime environments, service access, operational visibility, and resource governance
Data processing	Dataset preparation, image analysis, batch pipelines, offline preprocessing	Storage integration, dataset/model management, schedulable batch jobs, and observability

🎯 Features

🏗️ Architecture

Crater is organized around four layers:

• User interfaces: web console, CLI, HTTP APIs, and agent-friendly command skills.
• Control plane: authentication, accounts, quotas, scheduling policies, jobs, services, templates, images, datasets, models, approvals, and operations.
• Execution layer: Kubernetes workloads, Volcano scheduling, accelerator resources, Pods, Services, PVCs, and external access rules for training, serving, and interactive environments.
• Observability and AI operations layer: metrics, logs, Grafana dashboards, operation records, runtime diagnostics, AI assistant workflows, and admin-side intelligent operations.

🚀 Getting Started

1. Prerequisites

• A running Kubernetes cluster
• kubectl
• Helm v3

2. Set up a cluster

Pick the option that fits your scenario:

Option	Best for	Reference
🐳 Kind	Local clusters in Docker	kind.sigs.k8s.io
🧱 Minikube	Single-node local dev & testing	minikube.sigs.k8s.io
☁️ Production K8s	Production or large-scale deployments	kubernetes.io/docs/setup

3. Install via Helm (OCI)

helm registry login ghcr.io
helm install crater oci://ghcr.io/raids-lab/crater --version <chart-version>

💡 The chart version is in charts/crater/Chart.yaml (field version) or in the GitHub releases.

Deployment guides:

• 📄 Minimal Deployment (Kind) — quickly spin up a basic Crater
• 📄 Cluster Deployment Guide — deploy a full Crater on a cluster

📚 Documentation

• 📘 Admin guide (English): https://raids-lab.github.io/crater/en/docs/admin/
• 📗 Admin guide (中文): https://raids-lab.github.io/crater/zh/docs/admin/

📁 Repository Structure

Path	Description
backend/	Backend services
frontend/	Web UI
cli/	Command-line interface
charts/	Helm charts for deploying Crater
website/	Documentation website source
grafana-dashboards/	Grafana dashboards used by Crater
docs/	Documentation entrypoints and localization resources
hack/	Developer tooling and scripts

💬 Community & Support

• 🐛 Issues — report bugs or request features: GitHub Issues
• 💡 Discussions — ask questions and share ideas: GitHub Discussions
• 📚 Docs — admin and user guides: raids-lab.github.io/crater
• ⭐ Star the project if you find Crater useful — it helps others discover it.

🤝 Contributing

We welcome community contributions! The complete development and contribution specification lives in CONTRIBUTING.md: global rules, environment setup (fork, hooks, unified config), workflow, commit convention, PR description template, and per-module entry points.

Per-module specs:

• Backend — backend/CONTRIBUTING.md
• Frontend — frontend/CONTRIBUTING.md
• Website / Docs — website/CONTRIBUTING.md
• CLI — cli/CONTRIBUTING.md

📝 License

Crater is licensed under the Apache License 2.0. See LICENSE.