Frank
49e8dfff66
halogen-flash-server is ACTIVE again with its 2026-09-16 measured ledger (budget 33.5 GiB against a need of 27.2 — no clamp — and 14.7 GiB left for everything else), ik_llama.cpp is retired with its measurements kept as the record of why, and the new frank-small runbook covers the -mtp file, the 64 KiB/token KV arithmetic, the MTP contract, and the reasoning_effort trap that cost me every subagent on the endpoint. AGENTS.md: topology, Active Services, runbook index, and three new llama.cpp lessons (MTP is a property of the file; --jinja is what `tools` needs; a model's template can reject the harness's thinking level). Three pre-existing defects found while editing it: a truncated sentence mid-file, a harness path that does not exist on either server, and the benchmarking stop/mask recipe still calling frank-large a root unit. Truthfulness notes worth keeping: the co-tenant stall NOTE prints again, so the claim that it "no longer prints" is gone and the three observed block counts are recorded instead of a guessed threshold; and the Observability page says out loud why frank-large has no scrape job rather than leaving a silent absence.
139 lines
5.2 KiB
Markdown
139 lines
5.2 KiB
Markdown
---
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type: guide
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title: LLM Deployment
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description: Scoped deployment and update process for the two inference services.
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tags:
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- llm
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- deployment
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- systemd
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timestamp: 2026-09-03
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---
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# Deployment and Update Process
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This page describes how to deploy and update the LLM servers on Dankserver and
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Dankclaw. The process uses Git as the single source of truth. A change to
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SearXNG, Caddy, or another container service does **not** restart inference.
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## Fleet
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| Target | Host | Service | Port |
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|---|---|---|---:|
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| Dankserver | `dank@dankserver` | `frank-medium` | 7770 |
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| Dankclaw | `dank@dankclaw` | `frank-large` | 7770 |
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| Dankclaw | `dank@dankclaw` | `frank-npu` (FLM on the XDNA2 NPU) | 7771 |
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## Scoped workflow
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```mermaid
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graph LR
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A[Edit LLM files] --> B[Run tests]
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B --> C[Commit main]
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C --> D[deploy.sh selects matching service]
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D --> E[Target pulls ff-only]
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E --> F[Relink one unit]
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F --> G[Restart one inference service]
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G --> H[Wait for that service health]
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```
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Run the unified deployer from the repository root:
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```bash
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./scripts/deploy.sh dankserver --service frank-medium
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./scripts/deploy.sh dankclaw --service frank-large
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./scripts/deploy.sh dankclaw --dry-run
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```
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Without `--service`, `scripts/deploy-plan.py` maps changed files to the service
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on the selected host. The following files select the corresponding service:
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- `scripts/frank-medium.env`, `frank-medium.service`, and
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`serve-frank-medium.sh` select `frank-medium` on Dankserver.
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- `scripts/frank-large.env`, `frank-large.service`, and
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`serve-frank-large.sh` select `frank-large` on Dankclaw.
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- `scripts/frank-npu.env`, `frank-npu.service`,
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and `serve-frank-npu.sh` select `frank-npu`
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on Dankclaw.
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- `scripts/chat-template.jinja` selects the LLM on the selected host only.
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- Documentation, tests, deployment tooling, and unrelated container changes
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do not select either inference service.
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The target-side `scripts/deploy-remote.sh` installs the selected service: for a
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**root unit** it relinks the repo file into `/etc/systemd/system/`, reloads
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systemd, enables it and restarts it (frank-small); for a **Quadlet** it retires
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the predecessor, stops the service's own unit, waits for the published port to
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clear, installs the `.container` and restarts the generated unit (frank-large).
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Each handler then waits on that service's health endpoint —
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`http://127.0.0.1:7770/health` for frank-large, `http://127.0.0.1:7771/health`
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for frank-small. The wait budget is 20 minutes because cold model loads are
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slow: halogen pins ~68 GiB of weights before it serves anything. A failed
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handler does not advance its per-service state.
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## Unit and artifact paths
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The units point into the stable target checkout:
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```text
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/home/dank/serverAppConfigs/scripts/frank-medium.service
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/home/dank/serverAppConfigs/scripts/frank-small.service
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```
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The unit files and serve scripts validate their model/runtime artifacts before
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starting. `frank-large` is a Quadlet under `dankclaw/frank-large/quadlet/`
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rather than a unit in `scripts/`; `frank-small` keeps the unit/env/serve trio
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above. Keep `REMOTE_DIR` stable; moving the checkout requires updating both the
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unit absolute paths and the deployment tests in the same change.
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## NPU unit
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`frank-npu` is an inactive, historical Dankclaw endpoint. It is
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relinked only when explicitly selected with `--service frank-npu`; the
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large-model handler never restarts it. This is intentional: the NPU is not part
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of the active inference path and should not be disturbed by a large-model
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change.
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## Isolation guarantee
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The remote runner has one explicit service dispatch. It does not run a
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host-wide LLM loop, `podman-compose up` for unrelated services, or a blanket
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systemd restart. Each successful service is recorded under the target user's
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state directory outside Git:
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```text
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~/.local/state/serverAppConfigs/deploy-state/
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```
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On a later ordinary deployment, a service remains pending only while its
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recorded revision differs from the checkout and the planner still finds a
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change in that service's inputs.
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## Manual recovery
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If the LLM handler fails after the target pull:
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1. Inspect `journalctl -u frank-small -n 200` (root unit) or
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`journalctl --user -u frank-large -n 200` (rootless Quadlet).
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2. Confirm the model files, the runtime binary and (for frank-large) the
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container image are present and readable by `dank`.
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3. Retry only the affected service:
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```bash
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./scripts/deploy.sh dankserver --service frank-medium
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./scripts/deploy.sh dankclaw --service frank-large
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./scripts/deploy.sh dankclaw --service frank-small
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```
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4. Verify the matching health endpoint before declaring recovery.
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Do not stop the other host's inference service during recovery. The two
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Dankclaw endpoints are independent processes on different devices: restarting
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frank-large does not disturb frank-small, and the reverse holds too — with one
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exception worth knowing, that the deployer waits for port 7770 to be released
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before it starts the replacement container. For benchmark work, follow the
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separate stop/benchmark/restart procedure in [[Testing Policy]] and the
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repository rules.
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## See Also
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- [Deployment Flow](../Architecture/Deployment%20Flow.md)
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- [[index]]
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- [Dankclaw deployment architecture](../Architecture/Network%20Architecture.md)
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