AI Infrastructure on Kubernetes
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AI Infrastructure on Kubernetes
Production cookbook for platform engineers running GPU clusters, model training, and inference on Kubernetes. What's inside: • NVIDIA GPU Operator install + ClusterPolicy tuned for production. • GPU
- Production-tested examples you can copy-paste today
- PDF format, optimized for screen and print
- Free updates whenever the source CLI changes
- Search-friendly, code-block heavy, no fluff
About this ebook
Production cookbook for platform engineers running GPU clusters, model training, and inference on Kubernetes.
What's inside:
• NVIDIA GPU Operator install + ClusterPolicy tuned for production.
• GPU sharing recipes: MIG, time-slicing, MPS - when to use which.
• Karpenter / Cluster Autoscaler templates for cost-aware H100 / A100 / L40S / L4 pools.
• Volcano gang scheduling + Kueue fair-share queues + priority and preemption.
• Fluid + Alluxio model caching from S3 / GCS to local NVMe.
• NCCL / RDMA / IB / RoCE setup for multi-node training.
• vLLM Deployment + HPA on tokens/sec, KServe InferenceService, KAITO patterns.
• KubeRay RayJob, PyTorchJob + FSDP, Kubeflow Pipelines for end-to-end MLOps.
• Model registries: MLflow, Hugging Face, OCI artifacts with cosign signing.
• Autoscaling: HPA + KEDA scale-to-zero on queue depth.
• dcgm-exporter + vLLM /metrics + Prometheus SLO rules + Grafana dashboards.
• Cost control: per-tenant quotas, idle-GPU alerts, spot pools, chargeback.
• Multi-tenant isolation: PSS, NetworkPolicy, Kyverno generate rules.
• Supply chain: signed model images + Kyverno verify-images.
• Disaster recovery for models and checkpoints, 10 common errors.
Tested against Kubernetes 1.29-1.31 on EKS, GKE, AKS, OpenShift, and on-prem. Lifetime updates while the recipes are maintained, delivered via /library.
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