RHEL AI Engineering Recipes
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RHEL AI Engineering Recipes
Production cookbook for Red Hat AI — the recipes platform and SRE engineers need to ship InstructLab fine-tunes, vLLM serving, RAG, and multi-agent stacks on RHEL 9 GPU nodes. What's inside: • Subscr
- 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 Red Hat AI — the recipes platform and SRE engineers need to ship InstructLab fine-tunes, vLLM serving, RAG, and multi-agent stacks on RHEL 9 GPU nodes.
What's inside:
• Subscription-manager + repo enablement for `ilab`, CUDA, and Intel Gaudi accelerators.
• GPU smoke tests and troubleshooting recipes (nvidia-smi, CDI device resolution, persistent mode, ECC).
• Cloud bootstrap recipes for AWS p5/g5, Azure ND H100 v5, GCP A3, and IBM Cloud GX3.
• Bare-metal kickstart for PXE-provisioned RHEL AI nodes.
• InstructLab taxonomy templates (knowledge + skills), SDG runbook, multi-GPU DeepSpeed ZeRO-3 training.
• vLLM serving with tensor parallel, chunked prefill, GPU memory tuning, and OpenAI-compatible inference.
• FastAPI wrapper, ChromaDB + LangChain RAG pipeline, CrewAI multi-agent template.
• Idempotent Ansible role to deploy RHEL AI across a fleet, vaulted activation keys included.
• Observability: nvidia-dcgm-exporter, vLLM /metrics scrape config, SLO Prometheus rules, Grafana alerts.
• Scale-out topologies: data-parallel vs tensor-parallel vs pipeline-parallel, multi-node DeepSpeed + Ray.
• Governance: SPDX lineage YAML, model cards, cosign keyless artifact signing, request-level audit logs.
• Blue-green and canary model rollouts via weighted upstreams.
• Hardware sizing reference (VRAM per parameter, tokens/sec per GPU class) and 10 common-error fixes.
Tested against RHEL AI 1.5 on A100, H100, and L40S GPUs. Lifetime updates while the recipes are maintained, delivered via /library.
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