Story Crater Bot
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Layout on 4x Tesla V100 32GB (PCIe, no NVLink), all TP=1: GPU0+1 vLLM DeepSeek-R1-Distill-Qwen-32B bnb-nf4 (2 replicas) GPU2 Ollama ornith:35b + qwen2.5:3b-instruct (co-resident) GPU3 vLLM Qwen2.5-Math-PRM-7B (reward model) CPU TEI nomic-embed-text-v2-moe, bge-reranker-base Volta constraints, each verified against live output rather than config: - vLLM pinned v0.11.0: sm_70 dropped from CUDA_SUPPORTED_ARCHS at v0.11.1. - AWQ hard-rejected (needs sm_75). GPTQ passes vLLM's min_capability=60 gate but is NUMERICALLY WRONG on sm_70 — emits garbage logits. Proven by an fp16 control run producing correct text on an identical backend. bitsandbytes nf4 verified correct by output. - flashinfer's check_cuda_arch() crashes on any sm_7x (calls .isdigit() on an int) -> VLLM_USE_FLASHINFER_SAMPLER=0. - xformers has no sm_70 kernel for V1's paged-attention bias, and V0 was removed in v0.11.0 -> TRITON_ATTN. - Ornith is Qwen3.5-MoE hybrid-attention; vLLM added that arch after dropping Volta, so no build has both -> Ollama, which also multiplexes a second model on the same card for free. Cluster prereqs that were absent: - RuntimeClass nvidia: the Talos toolkit extension registers the containerd handler but not the k8s object; without it every pod is rejected at admission. - gpu-system pinned to privileged PSA: a device plugin cannot satisfy the cluster-default baseline, it must mount hostPath. - device-plugin affinity=null: the chart requires NFD labels that do not exist here, so it matched zero nodes and reported desiredNumberScheduled=0 silently. - Recreate strategy on GPU services: with GPUs allocated exactly 4/4, a RollingUpdate surge pod has no card and deadlocks the rollout. - longhorn-llm-local SC (1 replica, strict-local, disk tag llm): the default 3-replica class could not place the volume at all (every control-plane disk was at its over-provisioning ceiling), and this keeps ~60GB of weights on worker-1's own NVMe instead of reading them over the network. deploy-gpu-serving.sh sequences ArgoCD syncs (or helm/kubectl in --manual mode) and never applies a manifest absent from git; doctor/unstick/teardown stages exist so this is diagnosable without ad-hoc kubectl archaeology.
72 lines
2.5 KiB
YAML
72 lines
2.5 KiB
YAML
# Reranker — BAAI/bge-reranker-base, on CPU via HuggingFace TEI.
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#
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# Second stage of retrieval: the embedding model fetches a coarse top-k by
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# vector similarity, this cross-encoder re-scores those candidates against the
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# query directly. That is what fixes the "semantic dilution" problem in Plan 1 —
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# a single embedding vector cannot represent a large chunk faithfully, so
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# ranking by cosine alone surfaces near-misses.
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#
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# CPU for the same reason as the embedding service: all 4 GPUs are claimed and
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# the device plugin allocates whole cards. A 568M cross-encoder scoring ~20-50
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# candidates per query is well within CPU budget.
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#
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# Arch is XLMRobertaForSequenceClassification, which TEI serves as /rerank.
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# 278M params — smaller than v2-m3 (568M) and English/Chinese rather than
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# multilingual, which suits code+docs retrieval and is faster on CPU.
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apiVersion: serving.kserve.io/v1beta1
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kind: InferenceService
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metadata:
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name: reranker
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labels:
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app.kubernetes.io/name: llm-reranker
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app.kubernetes.io/part-of: llm-serving
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spec:
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predictor:
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minReplicas: 1
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maxReplicas: 1
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# Same worker-1 pin as the embedding service, to share the RWO models PVC.
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nodeSelector:
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kubernetes.io/hostname: worker-1
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containers:
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- name: kserve-container
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image: ghcr.io/huggingface/text-embeddings-inference:cpu-1.8.2@sha256:4d632b76bd14cb57044a1ffb0ad48ab0ba4939e705a9a615ccc740658575c26e
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args:
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# bge-reranker-base, NOT v2-m3. TEI's CPU image starts the ONNX
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# Runtime backend and v2-m3 ships no ONNX files, so it dies with
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# "Model ONNX files not found in the repository". This build does.
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- --model-id=BAAI/bge-reranker-base
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- --port=8080
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- --hostname=0.0.0.0
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- --auto-truncate
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env:
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- name: HUGGINGFACE_HUB_CACHE
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value: /mnt/models
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ports:
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- containerPort: 8080
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protocol: TCP
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resources:
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requests:
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cpu: "8"
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memory: 4Gi
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limits:
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cpu: "16"
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memory: 8Gi
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volumeMounts:
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- name: models
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mountPath: /mnt/models
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startupProbe:
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httpGet:
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path: /health
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port: 8080
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periodSeconds: 10
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failureThreshold: 60
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readinessProbe:
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httpGet:
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path: /health
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port: 8080
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periodSeconds: 10
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volumes:
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- name: models
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persistentVolumeClaim:
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claimName: llm-models
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