60220e18b6
Idempotent vLLM OpenAI-compatible serving role, staged-first (does not
start/enable the systemd unit or touch production traffic by default).
Validated end-to-end against astro-orbiter in a brief shadow window
(llama-swap stopped ~5 min, per homelab-llm-inference skill's documented
shadow-validation pattern):
- /health 200, /v1/models returns Qwen2.5-32B-Instruct-AWQ,
/v1/completions live smoke test passes, clean journalctl
- 3 consecutive full-role runs confirmed changed=0 (idempotent)
- production restored: llama-swap active, /v1/embeddings against
nomic-embed-text-v1.5 confirmed still working (Hindsight retain path)
Deviates from the original spec's model choices (Qwen2.5-32B-Instruct /
Qwen3-8B-Instruct bf16) to use the official Qwen AWQ pre-quantized variants
instead -- vLLM does not do safe on-the-fly quantization on this host
(bitsandbytes OOM history) and unquantized bf16 32B does not fit 24GB VRAM.
Two real bugs found+fixed during first-start validation (systemd-only
repro, not visible via interactive SSH testing):
1. ninja not on systemd's minimal PATH -> vLLM torch.compile
FileNotFoundError. Fixed via explicit PATH env in the unit.
2. FlashInfer sampler JIT fails to compile on RTX 3090 (SM86) --
known upstream issue class (vLLM GH #23023, #44305). Fixed via
VLLM_USE_FLASHINFER_SAMPLER=0 (falls back to native sampler).
Also fixed a real idempotency bug: force-upgrading setuptools to latest
fought with vLLM's own setuptools<81.0.0 pin, causing an install/downgrade
flip-flop (changed:true) on every run.
vllm_service_enabled defaults to false -- a host reboot must not
auto-start vLLM and VRAM-collide with the still-live llama-swap production
service. Cutover (enabling + starting + migrating consumers) is an
explicit, separate step outside this role, gated on adding embedding-mode
support (--task embed) for nomic-embed-text-v1.5, which this role does
not yet implement (Hindsight retain still depends on llama-swap's
nomic-embed until that follow-up lands).
Role: roles/deploy-vllm/ (defaults/handlers/meta/tasks/templates/README)
Playbook: playbooks/day1_deploy_vllm.yml
57 lines
2.5 KiB
Django/Jinja
57 lines
2.5 KiB
Django/Jinja
[Unit]
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Description=vLLM OpenAI-compatible inference server — {{ item.id }} ({{ item.hf_repo }})
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After=network-online.target nvidia-persistenced.service
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Wants=network-online.target nvidia-persistenced.service
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[Service]
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Type=simple
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User={{ vllm_venv_owner }}
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Group={{ vllm_venv_owner }}
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EnvironmentFile={{ vllm_api_key_env_file }}
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Environment="HOME=/home/{{ vllm_venv_owner }}"
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Environment="HF_HUB_CACHE={{ vllm_hf_hub_cache }}"
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Environment="HF_HOME={{ vllm_cache_dir }}"
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# vLLM's torch.compile path shells out to `ninja` by bare name (not via
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# venv-relative path) — without the venv's bin/ on PATH, systemd's minimal
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# default PATH causes FileNotFoundError: 'ninja' deep in compile, even
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# though `pip install vllm` installs the ninja package (and its console
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# script) INTO the venv. Caught during Phase 5 validation (t_ca1af9fb,
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# 2026-08-31): interactive SSH sessions have a different PATH than systemd
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# services, so this only reproduces under systemd, not manual testing.
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Environment="PATH={{ vllm_venv_path }}/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
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# FlashInfer's bundled sampling.cu JIT-compiles against a cub template API
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# (BlockAdjacentDifference::FlagHeads) that this flashinfer/CUDA toolkit
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# combination does not provide on RTX 3090 (SM86) — 100 compile errors,
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# confirmed upstream-known (vLLM GH #23023, #44305: FlashInfer sampler JIT
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# breaks on various SM targets across flashinfer/vLLM version combos).
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# Falls back to vLLM's native PyTorch sampler, which is fully supported and
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# only marginally slower for single-request/low-concurrency serving. Caught
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# during Phase 5 validation (t_ca1af9fb, 2026-08-31).
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Environment="VLLM_USE_FLASHINFER_SAMPLER=0"
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ExecStart={{ vllm_venv_path }}/bin/python -m vllm.entrypoints.openai.api_server \
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--model {{ item.hf_repo }} \
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--served-model-name {{ item.id }} \
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--host {{ vllm_serve_host }} \
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--port {{ item.port }} \
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{% if item.quantization is defined and item.quantization != 'none' %}
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--quantization {{ item.quantization }} \
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{% endif %}
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--gpu-memory-utilization {{ item.gpu_memory_utilization }} \
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--max-model-len {{ item.max_model_len }} \
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--dtype {{ vllm_dtype }} \
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--api-key ${VLLM_API_KEY} \
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--enable-prefix-caching
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Restart={{ vllm_restart_policy }}
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RestartSec=10
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# vLLM torch.compile can take 4+ minutes before /health responds even after
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# weights are loaded (homelab-llm-inference skill pitfall) — give it room.
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TimeoutStartSec=600
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StandardOutput=journal
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StandardError=journal
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SyslogIdentifier=vllm-{{ item.id }}
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[Install]
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WantedBy=multi-user.target
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