[Bug]: engine:status re-runs the readiness probe under the engine lock on every call; a slow engine /health starves the model-list sweep and, polled ~1/s, leaked a TP=4 head to death
@kjlubick 已经在做这个了。
开始于 2026年9月18日。
评估
这个 Issue 还没有评估数据。
描述
Area
Engine or model management
User problem
Every engine:status call re-runs the engine's readiness probe while holding that engine's lifecycle lock, and several PAIR components poll status independently. On an engine whose readiness endpoint is cheap that is invisible. On an engine whose /health does real work it has two consequences we hit in production this week on a 4-node tensor-parallel SGLang head (DGX Spark, GB10):
- Model-list starvation. The broker's advertiser (every 5 s), the loaded-model watcher (every 5 s) and the desktop's remote status poll (every 10 s) each call
engine:status.StatusAtPorttakesst.opMu, thenreconcilePresencerunsprobe(ready)andprobe(identity)with no caching. SGLang's/healthperforms a short generation and takes ~1.0 s on this build, so the mutex was held essentially 100% of the time andModelsResult's sweep (which needsStatusfirst) never got in.GET :14322/v1/modelson that node hung for 40 s+ indefinitely; peers piled up hundreds of CLOSE-WAIT sockets; the desktop loggedremote engine status ... unavailableevery 10 s. A standalone engine-manager with no broker traffic answered in 16 ms. Restarting engine-manager did not help. - The probe load itself leaked memory. The head's container log shows 72,144
GET /healthand 69,817GET /get_model_infoover a 20 h run, ~1/s each, with zero user requests for the final 30 min. The head's MemAvailable declined monotonically from 8.7 GB (00:20) to 2.5 GB (16:20) while the three worker ranks stayed flat, then earlyoom SIGTERMed the scheduler at 16:28 and the TP group died. After the crash the head returned to its idle baseline, so the growth was inside the front-end processes only rank 0 runs. Pointing the probes at/get_model_info(~1 ms, no generation) dropped/healthtraffic from ~3,500/h to the container's own healthcheck and the model list answers in 13 ms.
SGLang itself is not in develop yet (it lives in #50 and in my fork), but the mechanism is upstream code and applies to any engine whose readiness endpoint is not free; llama.cpp's /health under load and /v1/models on busy servers are candidates.
Where
services/nvpair-engine-manager/status.go:StatusAtPort→st.opMu.Lock()→reconcilePresence(context.Background(), ...)→e.probe(ctx, ready, port)thene.probe(ctx, identity, port)on every call.services/nvpair-engine-manager/models.go:ModelsResultcallse.Status(name)per engine before the 5 s action budget starts; the lock wait is unbounded.- Pollers:
nvpair-ui-broker/advertiser.go(autoAdvertiseInterval = 5 * time.Second),nvpair-engine-manager/loadedwatch.go(defaultLoadedPollSeconds = 5), the desktop's remote-get-installed loop.
Proposed fix
- Do not re-run the readiness probe for an engine that is already adopted and healthy with a live health loop; trust the health loop's last result, or cache presence for a few seconds.
- Do not take
opMufor the read-only status path; snapshot state, probe outside the lock. - Treat the manifest's
identityendpoint as the default readiness/health probe and require an explicit opt-in for anything that generates.
Workaround for operators
A per-engine manifest override (engines/sglang.json) pointing runtime.ready.http and runtime.health.http at /get_model_info, plus the advertiser change in https://github.com/jlacroix82/Personal-AI-Router/commit/c5b9be7 (on feat/vllm-sglang).
Environment
PAIR 0.1.1 services (engine-manager 0.21.0 / broker 0.42.2 as built from feat/vllm-sglang at ff26f5b), Linux arm64, DGX Spark x4 per TP group, SGLang lmsysorg/sglang:dev-dsv41 serving DeepSeek-V4.1-Flash. Related: #37 (probe connection reuse), #50 (SGLang engine), #24 (external backends).
- 主要语言
- Go
- 星标
- 1.4k
- 派生
- 250
- 平均合并
- 23 小时 27 分钟
- 30 天内合并 PR
- 1
贡献指南
从这里开始
- 先读完整个 Issue,再读项目的贡献指南。
- 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
- Fork 仓库,在一个分支上完成修改。
- 提交 Pull Request,并在描述里引用这个 Issue 编号。
NVIDIA/Personal-AI-Router 的其他 Issue
-
bug
难度 4/5 3-5 天 新手友好度 55/100
NVIDIA/Personal-AI-Router#94 · 1 条评论 ·
-
难度 3/5 1-2 天 新手友好度 74/100
NVIDIA/Personal-AI-Router#92 · 1 条评论 ·
-
NVIDIA/Personal-AI-Router#91 · 已指派 1 人 ·
-
enhancement
难度 4/5 3-5 天 新手友好度 45/100
NVIDIA/Personal-AI-Router#85 · 1 条评论 ·
-
enhancement
难度 4/5 3-5 天 新手友好度 52/100
NVIDIA/Personal-AI-Router#77 · 1 条评论 ·
查看 NVIDIA/Personal-AI-Router 的全部 Issue
相似的 Issue
-
kind/bug
难度 2/5 1-3 小时 新手友好度 88/100
kubernetes-sigs/prow#953 · 1 条评论 ·
-
难度 2/5 1-3 小时 新手友好度 88/100
caddyserver/caddy#8046 ·
-
难度 2/5 1-3 小时 新手友好度 86/100
-
L1 recommended for recruits
难度 2/5 1-3 小时 新手友好度 88/100
-
area/entangle bug
难度 1/5 1 小时以内 新手友好度 92/100