75 lines
2.5 KiB
YAML
75 lines
2.5 KiB
YAML
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# Embeddings — Nomic Embed Text v2 (MoE), on CPU via HuggingFace TEI.
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#
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# CPU, not GPU, deliberately. All 4 V100s are claimed by the generation models,
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# and the device plugin hands out WHOLE GPUs — a 5th GPU-requesting pod is
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# unschedulable no matter how much VRAM is free. Sharing would need global
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# time-slicing, which on a single node cannot be scoped to one card and would let
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# the scheduler co-locate two ~20GB models and OOM both.
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#
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# worker-1 has 96 cores with ~250m requested, and this is a 475M-param encoder
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# (305M active). Retrieval runs once per agent-loop iteration, not per token, so
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# CPU latency here is immaterial. This is also what Plan 1 originally specified.
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#
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# TEI (not vLLM) because it is purpose-built for encoders and explicitly lists
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# nomic-embed-text-v2-moe as supported.
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#
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# NOTE: Nomic v2 requires task prefixes on the CLIENT side —
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# documents: "search_document: <text>"
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# queries: "search_query: <text>"
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# Embedding without the prefix silently degrades retrieval quality.
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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: embeddings
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labels:
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app.kubernetes.io/name: llm-embeddings
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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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# Pinned to worker-1 only so it can share the RWO models PVC with the GPU
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# pods (RWO = single node, any number of pods on it).
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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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- --model-id=nomic-ai/nomic-embed-text-v2-moe
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- --port=8080
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- --hostname=0.0.0.0
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# Truncate rather than 413 on over-long input.
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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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