vh
569e1af9ca
Move the ~22-service flat "AI Systems" group off the Main tab into a new four-tab layout (Main / AI / Infrastructure / Toolchain). The AI tab sorts the inference fleet by function into seven groups: AI - Inference gen, char-rp, char-rp-reasoning, Granite summarizer AI - Eval & Retrieval Selene, Skywork Reward, Qwen3 rerank/embed, image-bench AI - Gateways & Chat LiteLLM, Asset Engine, Gateway Chat, Open WebUI, ... AI - Speech (TTS) Chatterbox Fast, Kokoro, mOrpheus AI - Audio Tools Parakeet ASR, YT Voice Clipper AI - Image & Media ComfyUI, Arbo AI - Dormant stopped rollback seats + retired auditions Relabel each stack's homepage.group so canonical stacks/ matches the live containers on ana-ml2, ana-docker, and irv-ml1. Dormant stacks were refreshed with `docker compose up --no-start` so they carry the new label while staying stopped (compose-start rollback preserved). settings.yaml drives tab/order/ columns; services.yaml and README updated to the new scheme.
87 lines
2.7 KiB
YAML
87 lines
2.7 KiB
YAML
# selene — AtlaAI Selene 1 Mini (Llama 3.1 8B) judge/eval model on ana-ml2 GPU 1.
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#
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# Restores the judge that went offline when llama-swap was downed (it was the
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# Q6_K GGUF `selene-1-mini-8b` in the llama-swap zoo). Re-served on vLLM at the
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# operator's request, FP8 (NVFP4 had no pre-made checkpoint and W4A4 is too
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# aggressive for a precision judge validated at Q6_K — FP8 ≥ Q6_K fidelity).
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#
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# FP8 = vLLM DYNAMIC --quantization fp8 (W8A8) of the bf16 AtlaAI checkpoint —
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# no offline quant needed, near-lossless, and Selene is text-only Llama 3.1 so
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# there's NO vision tower for dynamic fp8 to noise-quantize (the qwen35-VL
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# footgun doesn't apply here). ~8 GiB weights on GPU 1's headroom.
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#
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# Co-tenant on GPU 1 with qwen36 (NVFP4) + granite + embed/rerank/reward. Sized
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# to fit the ~24 GB headroom while leaving GPU 1 a safe buffer (see .env).
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# Served ONLY as `selene-1-mini-8b` (the name its consumers know). All tunables
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# live in .env.
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name: selene
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services:
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vllm-selene:
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image: ${SELENE_IMAGE}
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container_name: ${SELENE_CONTAINER_NAME}
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restart: unless-stopped
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ipc: host
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ports:
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- "${SELENE_PORT}:8000"
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volumes:
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- /tank/aimodels/huggingface:/hfcache
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environment:
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- HF_HOME=/hfcache
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- HF_HUB_CACHE=/hfcache/hub
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- HUGGING_FACE_HUB_TOKEN=${HF_TOKEN:-}
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- VLLM_API_KEY=${API_KEY:-}
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command:
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- ${SELENE_MODEL}
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# Dynamic FP8 (W8A8) from the bf16 checkpoint — no pre-quant needed.
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- --quantization
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- fp8
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- --served-model-name
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- selene-1-mini-8b
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- --host
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- 0.0.0.0
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- --port
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- "8000"
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- --gpu-memory-utilization
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- ${SELENE_GPU_MEM_UTIL}
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- --max-model-len
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- ${SELENE_MAX_MODEL_LEN}
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- --max-num-seqs
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- ${SELENE_MAX_NUM_SEQS}
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# fp8 KV — matches the judge's old q8 KV posture + keeps the pool compact
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# on the shared card.
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- --kv-cache-dtype
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- fp8
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- --dtype
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- auto
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- --enable-prefix-caching
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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device_ids:
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- "${SELENE_GPU_ID}"
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capabilities:
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- gpu
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 180s
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networks:
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- tnet
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labels:
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- homepage.group=AI - Eval & Retrieval
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- homepage.name=Selene 1 Mini 8B (judge, FP8)
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- homepage.icon=mdi-gavel
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- homepage.description=AtlaAI Selene 1 Mini Llama-3.1-8B judge (FP8) via vLLM (ana-ml2 GPU1)
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- homepage.href=http://10.250.50.54:${SELENE_PORT}/docs
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networks:
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tnet:
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name: traefik-net
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external: true
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