Keysat
8d839e3714
- Add redaction gateway (redaction_gateway.py, redaction/ scrub + tests) - Add embeddings proxy and spark_embed service (Dockerfile + main.py) - Expand audio_proxy with speaker-aware handling; deep_health/health/server updates - Package: configureSparks action + sparkConfig model updates, manifest/main wiring - Docs: AUDIO_API, EMBEDDINGS, REDACTION_GATEWAY; HANDOFF and runbook/known-issues refresh
90 lines
4.2 KiB
Markdown
90 lines
4.2 KiB
Markdown
# spark-control runbook
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Operating notes for running and maintaining the cluster via spark-control.
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## Prerequisites (per Spark)
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spark-control is a **controller**, not a runtime. Each Spark in your cluster must already have the upstream `eugr/spark-vllm-docker` project set up:
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1. Clone `https://github.com/eugr/spark-vllm-docker` to `~/spark-vllm-docker` on Spark 1 (the head node).
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2. Build the vLLM container: `./build-and-copy.sh -c` (on a cluster) or `./build-and-copy.sh` (solo).
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3. Pre-download any models you want in the catalog: `./hf-download.sh <repo> -c --copy-parallel`.
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4. Verify: `./launch-cluster.sh status` returns sensibly.
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5. Set up passwordless SSH from your Start9 server's spark-control container to each Spark (use the Show Public Key action — see README.md "Post-install setup").
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Sharing this package with someone else who has a similar dual-DGX-Spark setup: they do the same per-Spark prerequisites, then sideload the `.s9pk` on their Start9 and run the setup actions.
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## Recent successful swaps
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- **2026-05-12 — gemma4 → qwen36** via `POST /api/swap` from laptop dev server. ~5:30 to "Application startup complete." Inference works (`/v1/chat/completions` returns reasoning content via `reasoning` field). `--moe_backend=flashinfer_cutlass` confirmed valid by vLLM (logged "Using 'FLASHINFER_CUTLASS' NvFp4 MoE backend").
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## Optimization flags (added 2026-05-12)
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Aligned `gemma4` and `qwen36` `vllm_args` with the project's sibling recipes (`qwen3.5-35b-a3b-fp8.yaml`, `gemma4-26b-a4b.yaml`):
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- `--load-format=fastsafetensors` — faster cold-start weight load.
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- `--enable-prefix-caching` — reuse cached prefix tokens (e.g. system prompt) across requests.
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- `--kv-cache-dtype=fp8` — store KV cache in 8-bit FP; halves memory used per active context.
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These take effect on the **next swap to that model**. If a swap fails after this change with errors mentioning fastsafetensors/prefix-caching/fp8, revert the entry in `models.yaml` and retry.
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## Day-to-day
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- The UI lives at `http://<your-start9>.local:9999` once the StartOS package is installed and configured.
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- Status auto-refreshes every 5 s.
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- A swap takes 3–6 minutes depending on the model. Don't close the tab — but if you do, the swap continues; reopen and you'll re-attach to the log stream.
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## Adding a new model
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1. Add an entry to `image/models.yaml`. Required fields: `display_name`, `repo`, `size_gb`, `mode` (`solo` or `cluster`), `vllm_args`. Optional but recommended: `description` (one paragraph — what the model is, what it's good for, how it differs from others; renders below the meta tags in each card), `capabilities` (tags like `[vision, reasoning, tools]`), `expected_ready_seconds`.
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2. Confirm the weights are on the Spark: `ssh <spark-user>@<spark-1-host>.local 'ls ~/.cache/huggingface/hub/'`. If not, download with `./hf-download.sh <repo>` on Spark 1.
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3. Rebuild + redeploy the package: `cd package && make x86 && make install`.
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If `description` is omitted, the card simply hides that section — no need to populate it for every model. Keep descriptions generic (not user-specific) so the catalog stays portable.
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## Manual swap fallback
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If the UI is unavailable and you need to swap by hand:
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```bash
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ssh <spark-user>@<spark-1-host>.local
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cd ~/spark-vllm-docker
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./launch-cluster.sh stop
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./launch-cluster.sh --solo -d exec vllm serve RedHatAI/gemma-4-31B-it-NVFP4 \
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--port 8888 --host 0.0.0.0 --gpu-memory-utilization 0.8 \
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--max-model-len 32768 --reasoning-parser gemma4 \
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--tool-call-parser gemma4 --enable-auto-tool-choice
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docker logs -f vllm_node # wait for "Application startup complete."
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```
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## Diagnostics
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```bash
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# Is vLLM serving?
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curl -s http://<spark-1-ip>:8888/v1/models | jq .
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# Cluster status (containers up?)
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ssh <spark-user>@<spark-1-host>.local 'cd ~/spark-vllm-docker && ./launch-cluster.sh status'
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# Tail current model's logs
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ssh <spark-user>@<spark-1-host>.local 'docker logs --tail 200 -f vllm_node'
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# Parakeet
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curl -s http://<spark-2-ip>:8000/health
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# Kokoro TTS (v0.14.0+)
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curl -s http://<spark-2-ip>:8880/health
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```
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## Hard reset
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If launch-cluster.sh gets stuck:
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```bash
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ssh <spark-user>@<spark-1-host>.local
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cd ~/spark-vllm-docker
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./launch-cluster.sh stop
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docker ps -aq | xargs -r docker rm -f
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# then relaunch your preferred model
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```
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