[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
Mantenedores costumam responder em até 5 dias
@kjlubick já está trabalhando nisso.
Desde 18/9/2026.
Avaliação
Esta issue ainda não foi avaliada.
Descrição
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).
- Linguagem predominante
- Go
- Estrelas
- 1.6k
- Forks
- 266
- Merge médio
- 3d 6h
- PRs com merge (30d)
- 22
Preparar o ambiente
- Sem Dockerfile nem arquivo Docker Compose
- Tem um modelo de pull request
- Ler o guia de contribuição
Primeiros passos
- Leia a issue inteira e depois o guia de contribuição do projeto.
- Comente na issue dizendo que vai assumir — evita que duas pessoas façam o mesmo trabalho.
- Faça um fork do repositório e trabalhe em uma branch.
- Abra um pull request que referencie o número da issue.
Mais de NVIDIA/Personal-AI-Router
-
Dificuldade 5/5 Mais de uma semana Facilidade para iniciantes 20/100
NVIDIA/Personal-AI-Router#165 ·
Mantenedores costumam responder em até 5 dias
-
bug
Dificuldade 4/5 3-5 dias Facilidade para iniciantes 46/100
NVIDIA/Personal-AI-Router#164 ·
Mantenedores costumam responder em até 5 dias
-
enhancement
Dificuldade 4/5 Mais de uma semana Facilidade para iniciantes 12/100
NVIDIA/Personal-AI-Router#162 ·
Mantenedores costumam responder em até 5 dias
-
enhancement
Dificuldade 4/5 3-5 dias Facilidade para iniciantes 25/100
NVIDIA/Personal-AI-Router#154 · 1 comentário ·
Mantenedores costumam responder em até 5 dias
-
[Feature]: [Ollama] Alert the user about model pull failuresTalvez já em andamento @ckelseynv assumiu há 3 dias. Abertaenhancement
Dificuldade 3/5 1-2 dias Facilidade para iniciantes 56/100
NVIDIA/Personal-AI-Router#152 · 1 comentário · 1 responsável ·
Mantenedores costumam responder em até 5 dias
Todas as issues de NVIDIA/Personal-AI-Router
Issues semelhantes
-
📖 docs: Docker Compose quick start doesn't mention editing the project values in src/hive.yamlAbertadocumentation help wanted needs-triage
Dificuldade 1/5 1-3 horas Facilidade para iniciantes 75/100
hivecommons/hive#11606 · 2 comentários ·
Mantenedores costumam responder em até 1 dia
-
[docs] `/docs/identity-security/usage/alerts/`: docs reference a date that has already passedAbertadocumentation
Dificuldade 1/5 Menos de uma hora Facilidade para iniciantes 85/100
gravitational/teleport#69869 ·
-
category:agent
Dificuldade 2/5 1-3 horas Facilidade para iniciantes 82/100
LanternOps/breeze#8493 ·
Mantenedores costumam responder em até 1 dia
-
Dificuldade 2/5 1-3 horas Facilidade para iniciantes 70/100
Mantenedores costumam responder em até 1 dia
-
docs
Dificuldade 2/5 1-3 horas Facilidade para iniciantes 70/100
shaiknoorullah/hg-mono#815 ·
Mantenedores costumam responder em até 1 dia