skaldsong-dev surfaced three contract corrections before the first
deploy:
- WORLDTREE_TOKEN (outbound HTTP Bearer) was missing — separate code
path from SKALDSONG_BIFROST_JWT_KEY (inbound HS256 verify) but
same secret value.
- WORLDTREE_BASE_URL replaces SKALDSONG_WORLDTREE_API_URL (the
former is what the app actually reads).
- SKALDSONG_HOST_WIZARD_AGENT_ID was missing entirely — must pin to
skaldsong:wizard-v2 to inherit the existing Worldtree agent slot;
blank would burn another slot of the 50-per-key Heimdall quota.
Registry-pull pattern matching Worldtree: CI on vh/skaldsong builds and
pushes gitea.phasefinal.com/vh/skaldsong:<sha>, this playbook pulls +
recreates. SHA-pin only per current preference; no :latest moving-tag
advance yet (revisit once /health exercises Worldtree + Kokoro reach).
Host port 8300 (host) → 8000 (container). Persistent state under
/opt/docker/conf/skaldsong/{db,runs}.
Bifrost endpoint URL 10.250.50.70:8300 will need a paired
BIFROST_CLIENT_ALLOWED_HOSTS update on corviduo-dev Worldtree at first
deploy.
Without -n, ssh inherits the surrounding loop's stdin and consumes
the heredoc that feeds $changed / $deleted, silently truncating the
diff output to the first file only.
Phase 3.1 closes the cross-process gap the Phase 3 smoke surfaced —
streaming events (msg_start/thinking/delta/complete/curated) flow
from agent-runner → chamber via valkey pub/sub rather than the
SQLite bridge (too high-volume + ephemeral for the DB).
New service: `althing-valkey` (stock `valkey/valkey:8-alpine`).
Internal-only — no exposed port, no volume. chamber + agent-runner
reach via docker DNS at `valkey:6379` on the compose default
network. healthcheck via `valkey-cli ping` (5s interval). chamber
+ agent-runner gain `depends_on: valkey: service_healthy` so the
bridge is up before either side starts publishing or subscribing.
Forseti unchanged — never publishes Phase 3 events.
Operational properties (per forseti's deployment notes):
- Mixed-state safe at every step. Missing valkey.url config key
→ chamber + runner stay on v3.0 / Phase 2 equivalent paths.
- Backward path is single config-key delete + restart.
- streaming_enabled: true (set on agent-runner 2026-05-17) is
unaffected by this change.
README's services table + playbook header + verify section all
extended to reflect the four-service shape. Forseti's contract
at vh/althing:docs/contracts/phase3_1_valkey_bridge.contract.md
carries the wire-protocol spec.
Phase 2 daemon added to the althing-chamber stack per forseti's request
(vh/althing@5cd088a..ad1d025). Polls floor_grants WHERE consumed_at IS
NULL AND agents.driver='worldtree', claims via atomic UPDATE, calls
Worldtree's conversation API, posts the response back through the bus
as a broadcast.
Shape matches the existing forseti daemon:
- Same ${ALTHING_IMAGE} (the binary is already in [project.scripts]
as of ad1d025)
- command: ["althing-agent-runner"]
- Same shared SQLite bind-mount at /app/data
- No port, no healthcheck (CLI doesn't expose one; same liveness
story as forseti)
Safe to enable preemptively per forseti — when no driver=worldtree
handles are declared in config, the runner sleeps at
poll_interval_seconds. Multi-instance safe via the atomic claim
primitive (no flock needed).
Compose top comment, README "Services in this stack" table, playbook
header + verify steps all extended to reflect the three-service
shape. Will land on ana-docker on vh/althing's next push (compose
deployed via the elway playbook's upload step; image already carries
the binary).
Two-service compose (chamber + forseti sidecar daemon) sharing a single
SQLite store via bind-mount under /opt/docker/conf/althing-chamber/data.
eventbus.bridge_from_db is the cross-process glue — forseti's commits
reach chamber's SSE subscribers via the bridge.
Pattern matches task-board's build-on-host deploy:
- elway playbook clones vh/althing into /opt/docker/build/
- docker build -t althing-chamber:local . (no registry)
- playbook uploads compose + seeds .env one-time, brings both
services up, polls /health
- Gitea Actions workflow lives in vh/althing; reference copy here.
Internal tooling — host port 7881 (chamber's default of 7878 collides
with task-board). LAN-direct, no Traefik. Container always listens on
8000 internally.
Scaffold will fail to bring the chamber container up healthy until
galdrabok-side commits land:
- Dockerfile at vh/althing repo root (two-stage: uv-bookworm-slim
build → python:3.12-slim runtime, locked per open_questions §2
of the v1 contract).
- GET /health endpoint on the chamber app (200, no DB read).
- ALTHING_BIND / ALTHING_PORT env-var support in
core.cli.chamber_serve / core.chamber.cli (env > config.yaml >
defaults precedence).
Coordinated via althing thread 01KRMAK7RD7TP6C8DF4KXV31RT.
Previously a partial list "from vzdump logs + servers/ dirs". Replaced
with the full output of `qm list` on pfi-pve (2026-05-14), cross-
referenced against `servers/`. VMs that have a server dir are
annotated with the path; ones that don't are flagged "not yet in
`servers/` inventory" so the gap is discoverable.
Resolved:
- VM 100 = pbs-ana (was "VM (TBD)" in prior version)
- VM 106 = corviduo-dev (added 2026-05-12; was missing from the list)
Surfaced (new findings, not yet inventoried):
- VM 101 — PFI-ANA-DC (Active Directory domain controller)
- VM 103 — PFI-SlaveBot (purpose unclear from name alone)
- VM 104 — PFI-Mongo (MongoDB host, separate from pfi-postgres)
The three new-to-inventory VMs deserve `servers/<name>/` directories
with READMEs, but that's a follow-up — creating new inventory entries
is out of tend-docs scope.
Section claimed gitea + paperless-ng Postgres passwords were "currently
in use" with trivial values and that rotation was pending. Per
STATUS.md: "Rotate exposed secrets — done 2026-04-23. All six rotated:
vaultwarden/gitea/paperless-ng Postgres passwords (hardcoded
compose.yaml literals moved to gitignored .env files in the process)..."
Rotation happened; literals are no longer in compose.yaml; passwords are
no longer trivial. Removing the section rather than amending — once
fixed, there's no value in carrying a "we have weak passwords"
section that lies about the current state.
Surfaced by /tend-docs audit 2026-05-14.
VM 105's annotation said "PGDATA on NFS from ana-nas" — postgres
migrated off NFS to local VM disk on 2026-04-23 per STATUS.md ("DB data
on local disk, not NFS. pfi-postgres migrated 2026-04-23"). Updated to
reflect current reality with the migration date as the rot detector.
Still-TBD note for VM 33: `pbs-ana` is listed as "VM (TBD)" — was
deployed and has its own server dir, but its VM ID isn't recorded
here. `qm list | grep pbs` on pfi-pve would resolve, but my SSH to
pfi-pve as lkraven is currently password-required so I can't pull
this myself. Flag for next pfi-pve console session.
Surfaced by /tend-docs audit 2026-05-14.
Moved docs/asset-engine/design-brief.md → docs/archive/asset-engine/design-brief.md
with a 12-line archival header pointing at the live implementation
artifacts (vh/asset-engine source, stacks/asset-engine/ deploy,
CATALOG-CONTRACT.md, services.yaml).
The brief explicitly framed itself as a pre-implementation handoff
("Hand this to a design agent before any pixels"). Implementation
shipped 2026-05-12; the brief's role is past. Kept for the design
rationale it carries (why Asset is first-class, why v1 is synchronous,
v2/v3 seam reasoning) — future contributors benefit from finding it
when wondering "why is it this way."
Surfaced by /tend-docs audit 2026-05-14.
Stack was retired and replaced by the vllm stack (originally vllm-qwen3,
renamed 2026-05-13). Its README still framed it as a current solution
while ana-ml2's README + vllm's README both documented the retirement.
stacks/vllm/README.md "Migrating off Infinity" step 3 explicitly said
"Delete stacks/infinity/ from this workspace" — actioning that now.
No backwards-compat shims (PRACTICES §4): contract of a deleted system
has no historical value the next contributor needs; the replacement
path is documented in stacks/vllm/README.md.
Surfaced by /tend-docs audit 2026-05-14.
Two related changes shipped together. The stack rename is independent
but adding `vllm-reward` to the existing `vllm-qwen3` would have made
that name actively misleading.
**Rename:** `stacks/vllm-qwen3/ → stacks/vllm/`. Updated all in-repo
references (README.md root, servers/ana-ml2/, stacks/llama-swap/,
configs/restic/ana-ml2/, docs/runbooks/disaster-recovery.md). Two
intentional history mentions retained (servers/ana-ml2 + stacks/vllm
README).
**Add `vllm-reward` service:** serves Skywork-Reward-V2-Llama-3.1-8B-AWQ
on port 8003. The AWQ output is a locally-quantized model (not from HF),
so bind-mounts `/tank/aimodels/llm:/local-models:ro` rather than the
shared HF cache. Model config.json declares LlamaForSequenceClassification
which vLLM's pooling runner picks up automatically — produces a single
reward score per input via /classify.
**Flag note:** the user's spec listed `--task classify`, but vLLM 0.19.1
deprecated --task in favor of --runner pooling (model architecture in
config.json drives the classification head). Compose uses --runner
pooling with a comment explaining the substitution.
**GPU memory:** no rebalance needed — production had already tuned
EMBED/RERANK down from 0.40 to 0.20 each (canonical .env.example now
matches reality). Adding REWARD at 0.30 totals 0.70, leaving ~14 GB
headroom on the 48 GB Ada.
**Server-side:** brought existing vllm-qwen3 down, mv'd
/opt/docker/compose/vllm-qwen3 → /opt/docker/compose/vllm, appended
REWARD_* lines to existing .env (preserving API_KEY/HF_TOKEN), deployed
new compose via scripts/deploy-stack.sh, brought all 3 services up.
**Smoke tests:**
- /health on 8001/8002/8003 → 200
- /v1/models on 8003 → lists Skywork/Skywork-Reward-V2-Llama-3.1-8B-AWQ
with max_model_len 16384
- /classify with a sample conversation → returns LABEL_0 with prob 0.9999
(single-output regression-style reward score, expected shape for a
reward model)
Playbook handles models, datasets, and spaces (via --var repo_type=...)
since 3025d49 — the "-model" suffix was misleading. Renaming to match
actual scope.
Updates the single in-repo reference (changelog comment in
stacks/llama-swap/conf/config.yaml). config.yaml was scp'd to ana-ml2;
no docker compose restart needed (comment-only).
Adds optional repo_type var (default "model"; valid: model/dataset/space)
that's forwarded to `hf download --repo-type` and threaded through the
verify step (cache-dir prefix tracks repo_type: models--, datasets--,
spaces--).
The playbook was previously model-only because hf download defaults
repo_type=model — pulling a dataset surfaced as a 404 against the wrong
HF API path. Now generic.
Smoke-tested 2026-05-13:
scripts/elway ana-ml2 --playbook playbooks/pull-hf-model.yaml \
--var hf_repo=Skywork/Skywork-Reward-Preference-80K-v0.2 \
--var repo_type=dataset
→ 200 MB parquet cached at
/tank/aimodels/huggingface/hub/datasets--Skywork--Skywork-Reward-Preference-80K-v0.2/
Filename is misleading now (playbook handles more than models); leaving
the rename for a future cleanup since pull-hf-model.yaml is referenced
from the llama-swap config.yaml's 2026-05-13 changelog entry.
AtlaAI's Selene-1-Mini judge model for evaluation/scoring tasks.
Llama 3.1 8B base, mradermacher imatrix-quantized Q6_K (~6.5GB,
quality-leaning quant). Apache-2.0. Per Atla cookbook these defaults
hit 84% on RAGTruth hallucination eval.
New 'JUDGE / EVAL MODELS' section between the dense chat models and
the embedding models — separate category from chat/reasoning since
the run-params shape is different (deterministic-leaning: temp 0.01,
top-p 1.0, no repeat penalty).
q8_0 KV cache to fit 32K ctx cleanly on the 3090 with headroom.
Pre-pulled into the shared HF cache via the new
playbooks/pull-hf-model.yaml playbook (canonical replacement for
ad-hoc huggingface_hub.snapshot_download calls; see CHANGELOG).
Smoke-tested 2026-05-13: GET /v1/models lists selene-1-mini-8b,
POST /v1/chat/completions returns expected output cleanly.
Codifies the previously-manual workflow described in
stacks/llama-swap/README.md: install hf CLI via pipx (one-time),
inject hf_transfer for fast multi-connection downloads,
`hf download` into the shared HF cache at /tank/aimodels/huggingface
with optional --include filter.
Model-format-agnostic by design — same playbook handles GGUFs for
llama-swap and safetensors for vLLM (both stacks read the same cache
dir via HF_HOME=/hfcache). Does NOT edit any consumer's config.yaml;
per-model run params (ctx-size, sampler defaults, quant choice,
chat template, etc.) stay human-curated.
Usage:
scripts/elway ana-ml2 --playbook playbooks/pull-hf-model.yaml \
--var hf_repo=<user>/<repo> \
[--var allow_patterns='*Q6_K*']
Idempotent: hf CLI skips already-cached blobs; re-runs are
sub-second when the snapshot is already complete.
Smoke-tested 2026-05-13 against:
- mradermacher/Selene-1-Mini-Llama-3.1-8B-GGUF (Q6_K, ~6.5 GB)
- Skywork/Skywork-Reward-V2-Llama-3.1-8B (full safetensors, ~16 GB)
Captures the access path + emergency-ops runbook surfaced during the
2026-05-12 demo outage (Z_AI_API_KEY / runtime validator / :latest tag
drift cascade).
- servers/corviduo-dev/{README.md,ssh-target,system-details.txt}
- CLAUDE.md servers table + placement-rules note
The "docker-as-root sudo bypass" pattern (vh's docker-group membership
→ effective root for any bind-mount-able operation) is the canonical
fix path when /opt/worldtree*/.env edits are needed and the deploy
user's sudo is out of reach. The README documents the pattern with
the appropriate "docker-group ≈ sudo" security warning so future
emergency-ops sessions don't have to re-derive it.
Ownership stance matches SF client hosts: PFI hosts + provides
emergency-ops backstop, Worldtree-team owns OS config / deploys /
backup decisions. Coordinate data-affecting work with the architect.
Booted each service on irv-ml1 to capture the wrapper's actual voice
exposure, then took them back down. Initial state restored.
**Voxtral (v1 → v2)** — 20 native presets from live /v1/audio/voices:
neutral_{female,male}, casual_{female,male}, cheerful_female, plus
8 language-code variants ({ar,de,es,fr,hi,it,nl,pt}_{female,male}).
vLLM-Omni does NOT translate OpenAI aliases; `alloy` would 400. Default
flipped to `neutral_female` (matches Mistral docs).
**Qwen3-TTS (v1 → v2)** — 15 voices from live /v1/voices, NOT the 17
the upstream blog cites: 9 Qwen presets (vivian/serena/uncle_fu/ryan/
aiden/ono_anna/sohee/eric/dylan) + 6 OpenAI-compat aliases (alloy/
echo/fable/nova/onyx/shimmer). Default `vivian` matches the wrapper's
OpenAPI default. Catalog previously stated clone-only — wrong; the
wrapper does ship presets, the upstream blog list just doesn't match
the deployed wrapper. Cloning still works alongside via clone:<name>.
**Kyutai-TTS (v1 → v2)** — NillPointer wrapper does NOT expose any voice
listing endpoint (/v1/audio/voices 404; only /health + POST /v1/audio/
speech are wired). Voices are filesystem-discovered. Catalog now drops
the broken source_url, switches voice field to free-text with default
`unmute-prod-website/default_voice.wav` (upstream's named default).
Description lists the on-disk categories with counts + license posture
(vctk = CC BY 4.0 commercial-safe; expresso = CC BY-NC research-only).
Six default + description tightenings from upstream-source research:
- **Kokoro voice**: af_bella → af_heart. Per upstream VOICES.md, af_heart
is the only A-rated voice; also the kokoro-fastapi container's own
default. Applied to both kokoro (v2 → v3) and kokoro-captioned (v1 → v2).
- **SAO negative_prompt**: "Low quality." → "low quality, average quality".
Per diffusers official docs Tips section — the lowercase comma-separated
shape is the explicit recommendation; the period form was a code-example
string, never a documented default. SAO bumped v1 → v2.
- **SAO prompt description**: added the model card's canonical examples
("128 BPM tech house drum loop", "the sound of a hammer hitting a wooden
surface") plus the Tips advice on descriptive prompts.
- **VibeVoice voice**: en-Carter_man → en-Alice_woman. Per upstream model
card, Alice is the documented default. Description now flags the
Alice-injects-BGM-for-intros foot-gun + the `_bgm`-suffix meaning.
VibeVoice bumped v2 → v3.
- **Fish-s2 text description**: added multi-tag placeholder example
combining emotion + physical tags, per upstream best-practice docs
(physical tags "feel flat without emotional context").
- **ace-step prompt description**: added the upstream Gradio UI's
pre-filled tag string as the canonical prompt-shape example.
Surfaced separately to lkraven (NOT applied here, need decisions):
- Voxtral voice "alloy" may need to become a native preset like
"neutral_female" — depends on whether vLLM-Omni translates OpenAI
aliases.
- Qwen3-TTS — research found 17 presets (Cherry, Ethan, ...) that the
catalog currently says don't exist. Catalog says clone-only; needs
wrapper-level verification before adding.
- Kyutai-TTS — service down, can't probe /v1/audio/voices. Default
remains undefaulted.
Three changes prepping infra for asset_engine's orchestration feature
(SSH-driven bring-up / bring-down of irv-ml1 inference services with
per-device VRAM gating, contract in vh/asset-engine commit 5a36f8c):
1. asset-engine compose + .env.example + playbook gain a read-only
bind-mount for /app/runtime/ssh — the dedicated ed25519 keypair
(generated on ana-docker, not in the repo) plus a pinned known_hosts
for irv-ml1's host fingerprint. Env vars SSH_KEY_PATH and
SSH_KNOWN_HOSTS are exposed for the app to consume.
2. docs/asset-engine/services.yaml gains a `lifecycle: { stack, vram_gb,
gpu_device_id }` block on each of 12 orchestratable irv-ml1 services
(kokoro, chatterbox, index-tts, qwen3-tts, cosyvoice, fish-s2,
kyutai-tts, vibevoice, voxtral, parakeet, stable-audio-open, ace-step).
VRAM numbers are estimates from model footprint at fp16 — tune from
real nvidia-smi measurements once the gate is live. comfyui and
kokoro-captioned are deliberately excluded (variable-VRAM and
shared-container respectively).
3. servers/irv-ml1/README.md docker-stacks table now lists all 13
inference stacks (was only dockge + agents + comfyui) with port +
GPU pinning columns.
Pubkey deployed to ~lkraven/.ssh/authorized_keys on irv-ml1;
end-to-end SSH from ana-docker → irv-ml1 verified with strict
host-key checking.
Adds VOR_WORLDTREE_KEY + VOR_WORLDTREE_BASE + VOR_WORLDTREE_MODEL to vor's
compose environment with sane defaults. Empty key falls back to the
in-process MockWorldtree (the /mockup/ surface returns canned fixtures);
a real key issued by architect routes LLM calls at the demo Saga instance.
Key itself lives in ana-docker:/opt/docker/compose/vor/.env (not in the
repo).
Internal tooling — accessed at http://10.250.50.70:8200, not through
Traefik. Removes the unused traefik labels (router rule, TLS, crowdsec
middleware, loadbalancer port) and the traefik-net network membership;
homepage.href now points at host:port for direct discovery, matching
task-board's pattern. Playbook verify drops the traefik-net membership
check.
Mirrors task-board's build-on-host pattern: elway playbook clones
vh/asset-engine into /opt/docker/build/, docker build, install compose +
seed .env, up -d, verify /health. No registry.
Internal-only tool — LAN port 8200 (bind 0.0.0.0) is primary; Traefik
labels additionally route asset-engine.phasefinal.com with TLS via the
anaprod cert resolver. DB and outputs are separate bind-mounts under
/opt/docker/conf/asset-engine/ so outputs/ can move volumes later
without touching DB state. INFERENCE_HOST defaults to 10.100.79.3
(irv-ml1 over WG). OIDC env seam is pre-allocated empty for v2.
asset-engine's seed_surface contract needs reproducibility.seed_field to name
which CatalogField carries the seed so the UI can render a Roll button and the
server can fill empty seeds before persistence. SAO's seed field is type=number,
satisfying CatalogService._validate_seed_field.
ace-step is intentionally not declared here — actual_seeds is type=json (a list)
with an upstream reproducibility gap; the planned fix surfaces resolved seeds via
response header, pending a separate contract.
Per althing thread 01KRCNSF0V5NDCKB34H663MXHS — the catalog declared
14 services but 6 of them aren't running on irv-ml1 (chatterbox,
index-tts, qwen3-tts, cosyvoice, voxtral, kyutai-tts; missing from
docker ps entirely). Without action, the asset-engine UI would
declare them as available and consumers would hit unreachable
endpoints.
asset-engine consumer chose option (1) of three I sketched: extend
StatusT with `down` and treat it identically to `catalog-deferred`
in the picker (greyed, non-clickable). Lightweight, declarative, no
runtime health-check machinery, easy to revert when services
return.
Changes:
- StatusT enum (in asset_engine/catalog.py — committed there
separately) extended from
Literal["ready", "catalog-deferred", "experimental"]
to
Literal["ready", "catalog-deferred", "experimental", "down"]
- 6 services flipped to status: down.
- CATALOG-CONTRACT.md: replaced the bare-enum status row with a
four-row sub-table that names each value's meaning AND its picker
behavior. `down` and `catalog-deferred` get the same UI treatment
but the tooltip text differentiates ("Catalog-deferred" vs
"Service down — temporarily unreachable on irv-ml1") so the
semantic distinction (design state vs fleet-ops state) is
preserved.
- CATALOG-CONTRACT.md versioning policy table: new row codifying
"extending an existing enum (StatusT, FieldTypeT, ResponseTypeT,
CategoryT) with a non-conflicting value, with the consumer
updated in the same coordinated change" → no catalog_version
bump. Explicit rule for future enum extensions.
- JSON Schema regenerated.
catalog_version stays at 1.
Operational note (not catalog-side): the down services likely got
reaped 13+ days ago per the docker timestamps when other unrelated
work was done on irv-ml1. Bringing them back is a deploy task
outside this commit's scope. Flip status: down → ready in this file
once each one's confirmed running.
Sweep round caught vibevoice catalog drift in three dimensions; all
verified against the live OpenAPI + endpoint exercise, NOT against
documentation (which is what produced the bad values originally).
model:
was: options=[vibevoice], default=vibevoice
now: options=[tts-1, tts-1-hd, vibevoice], default=tts-1
why: the wrapper accepts all three (OpenAI-compat aliases all map
to VibeVoice internally per upstream README); wire default is tts-1
per /openapi.json. Catalog over-constrained users to a single value.
voice:
was: default=Carter; description listed [Carter, Davis, Emma, Frank,
Grace, Mike, Samuel] as built-ins
now: default=en-Carter_man; options enumerated:
OpenAI: alloy, echo, fable, onyx, nova, shimmer
VibeVoice: en-Alice_woman, en-Carter_man, en-Frank_man,
en-Mary_woman_bgm, en-Maya_woman, in-Samuel_man,
zh-Anchen_man_bgm, zh-Bowen_man, zh-Xinran_woman
why: discovered by hitting the endpoint with the catalog's claimed
"Carter" — wrapper returned 400 with the actual valid list inline
in the error body. The previous catalog values were fabrications,
not derived from any real source.
response_format:
was: options=[wav, mp3]
now: options=[wav, mp3, opus, flac, pcm]; default mp3 (was wav)
why: probed all 7 plausible formats; 5 return audio (200), aac and
m4a return 500. Catalog was over-restrictive; an earlier sweep
draft over-claimed [wav, mp3, opus, aac, flac, pcm, m4a] from
documentation that I refused to apply unverified. Now matches the
empirically-confirmed set.
Bumped vibevoice version 1 -> 2. catalog_version stays at 1.
Lesson reinforced: the only source-of-truth for catalog values is
the live wire. /openapi.json doesn't enumerate enums (returns bare
"string"); error responses from the endpoint with bad inputs are
the most reliable enum-discovery mechanism.
asset_engine consumer audited the entire ace-step entry's defaults
and slider ranges against acestep/ui/components.py (althing thread
01KRCN0SHP9YJGQD58EE95DC5P). The catalog had been authored from
documentation rather than from source; ten defaults were wrong and
several slider ranges were either too narrow or impractically wide.
Defaults changed (catalog -> upstream-authoritative):
infer_step 20 -> 60
guidance_scale 7.5 -> 15.0
cfg_type cfg -> apg
omega_scale 0.5 -> 10.0
guidance_interval 0.0 -> 0.5
guidance_interval_decay 1.0 -> 0.0
min_guidance_scale 1.0 -> 3.0
use_erg_tag false -> true
use_erg_diffusion false -> true
actual_seeds [42] -> [] (random per call)
Slider ranges adopted from upstream where reasonable; bounded
locally where upstream's range is so wide it's unusable as a UI
slider:
guidance_scale [1.0, 15.0] -> [0.0, 30.0] (upstream)
guidance_scale_text [0.0, 15.0] -> [0.0, 10.0] (upstream)
guidance_scale_lyric [0.0, 15.0] -> [0.0, 10.0] (upstream)
lora_weight [0.0, 2.0] -> [-3.0, 3.0] (upstream)
audio_duration [5.0, 600.0] -> [5.0, 240.0] (upstream max)
omega_scale [0.0, 1.0] -> [-10.0, 30.0] (UI bound; upstream is [-100, 100])
min_guidance_scale [0.0, 10.0] -> [0.0, 20.0] (UI bound; upstream is [0, 200])
Verified empty-string actual_seeds path against the live pipeline
source: pipeline_ace_step.py:set_seeds() falls through to
torch.randint when manual_seeds is "" (string, no comma, not all
digits). Smoked end-to-end: HTTP 200 in 11s, real WAV bytes back.
Reproducibility gap honestly documented in the entry's
reproducibility.notes and the actual_seeds field description: with
the new default `actual_seeds: []`, the wrapper rolls a random seed
inside the pipeline but doesn't capture or surface the chosen seed
back through the response. Default-defaulted assets cannot be
regenerated bit-exact; users requiring reproducibility must set
actual_seeds explicitly. Wrapper enhancement to surface the chosen
seed via X-Actual-Seeds header + a CatalogResponse.header_accessories
schema field is the planned fix.
ace-step bumped version 4 -> 5. catalog_version stays at 1 (no
schema changes).
Also added a "source-of-truth precedence" subsection to
CATALOG-CONTRACT.md's service-authoring notes, codifying the
read-order (Pydantic model > handler/pipeline code > Gradio UI >
README). Three ace-step bugs in three rounds (missing field, wrong
enums, stranded bytes, wrong defaults — really four) all share the
same root cause: catalog authored from doc surfaces that lie by
omission.
The pre-fix wrapper at stacks/ace-step/infer-api.py returned a JSON
{output_path: "..."} reference to a file written inside the
container at /app/outputs/. That path was unreachable from outside
the container — every consumer got 134 bytes of JSON-pretending-to-
be-WAV instead of audio. Surfaced by the asset_engine consumer's
end-to-end smoke (althing thread 01KRCJF7NGMXYE9F62Q1A6KFD4 msg 5);
my own earlier smoke missed it because I checked HTTP=200 and stopped
reading instead of inspecting the response body.
Wrapper now reads back the file the pipeline writes and streams the
bytes via fastapi.responses.Response with media_type set from the
audio_format request field (audio/wav | audio/mpeg | audio/flac).
The in-container path is exposed via X-Output-Path header for log
correlation but is no longer load-bearing.
Verified end-to-end against live ace-step on irv-ml1:
POST /generate -> HTTP 200 in 80s
content-type: audio/wav
content-length: 945226
x-output-path: /app/outputs/output_cfe87d1d....wav
$ file response.wav
RIFF (little-endian) data, WAVE audio, Microsoft PCM, 16 bit,
stereo 48000 Hz
Catalog: ace-step bumped version 3 -> 4. Dropped
response.output_field (no longer applicable). reproducibility.notes
expanded to record both the v2 18-arg-tuple fix and this v4
inline-streaming change so the history is auditable from the
catalog itself.
Stale ACEStepOutput Pydantic model left in infer-api.py for now —
unused but small; future cleanup.
Smoke testing in the asset_engine consumer surfaced an
UnboundLocalError 500 from ace-step (althing thread
01KRCJF7NGMXYE9F62Q1A6KFD4 msg 3). Root cause: this catalog had
invented enum values for scheduler_type and cfg_type that don't
exist in the upstream pipeline.
Read pipeline_ace_step.py inside the running container:
scheduler_type dispatch:
if == "euler": scheduler = FlowMatchEulerDiscreteScheduler(...)
elif== "heun": scheduler = FlowMatchHeunDiscreteScheduler(...)
elif== "pingpong": scheduler = FlowMatchPingPongScheduler(...)
# no else -> "linear" / "squared" / "sqrt" leave scheduler unbound
cfg_type dispatch:
accepts: apg | cfg | cfg_star
Catalog had:
scheduler_type: [linear, squared, sqrt] / default linear <- all invalid
cfg_type: [none, cfg, cfg_rw] / default cfg <- only cfg works
Fixed:
scheduler_type: [euler, heun, pingpong] / default euler
cfg_type: [apg, cfg, cfg_star] / default cfg
Bumped ace-step version 2 -> 3. Existing assets generated under v2
with scheduler_type=linear cannot reproduce (the value is now invalid);
v2 assets with the accidentally-valid cfg_type=cfg + a corrected
scheduler can be regenerated under v3 by mapping linear -> euler.
catalog_version stays at 1 (no schema change).
Verified end-to-end against live ace-step on irv-ml1:
POST /generate { scheduler_type: euler, cfg_type: cfg, ... }
-> 200, output_path returned, ~8s wall time
Lesson: OpenAPI introspection isn't enough for accurate catalog
authoring. Upstream OpenAPI returns bare `string` for both fields.
Reading the actual dispatch code is the only way to capture the
allowed values. Will sweep the other 11 service entries against
their implementations before P2 (scale to all services) lands.
asset_engine consumer (althing thread 01KRCJF7NGMXYE9F62Q1A6KFD4)
needed structure for ace-step's 27-field form. Two additive Pydantic
changes — backward-compatible, no catalog_version bump per the
policy table:
- CatalogField.section: str | None = None
- CatalogService.section_groups: list[CatalogSectionGroup] = []
- new CatalogSectionGroup model: {id, label, hint?}
Validator: every Field.section value must reference a declared
section_groups[].id within the same service; section_groups[].id
values are unique. CATALOG-CONTRACT.md updated with both the new
service-fields row and a versioning-policy row covering
"add optional Field/Service keys -> no bump."
ace-step entry rewritten to use the new schema:
- bumped version 1 -> 2
- declared 6 section groups (basic / generation / conditioning /
a2a / lora / output) with hints
- tagged every field with a section
- added previously-missing checkpoint_path (required: true,
default: "/app/checkpoints" — the container's mount path).
Wrapper-side cleanup (default in infer-api.py) queued as
follow-up.
- changed lyrics from optional: true -> required: true with
default "" to match upstream's `lyrics: str` shape (empty
string satisfies it).
JSON Schema regenerated.
Pydantic-model side of this change lives in asset_engine at
src/asset_engine/catalog.py — committed there separately.
Consumer-side renderer for the JSON-envelope + timestamps shape
shipped (althing thread 01KRCF4W66X3, msg 5). Smoke + regression
clean. Per the contract on the entry's notes block, flipping to
ready now that the renderer is in place.
asset_engine consumer needed to render kokoro-captioned, whose wire
shape is a JSON envelope carrying base64-encoded audio plus a
structured timestamps array. Modeling it as response.type=json
would force either a per-service-id renderer (forbidden by
brief §1.7) or extending the closed response-type vocabulary
(forbidden by brief §2.2 without a coordinated bump).
Resolution (per althing thread 01KRCF4W66X3): keep response.type
closed at the existing six values and decompose at the response
*field* level instead — the same flexibility seam already used by
mime / mime_from_field / output_field. Adds three optional keys:
- audio_field: JSON key holding base64-encoded audio bytes
- audio_format_field: JSON key holding the decoded audio MIME
- timestamps_field: JSON key holding a structured timestamps array
(independent of type, declared by any service emitting time-
aligned markers)
Validators in CatalogResponse enforce sane combinations:
- audio_field requires response.type=audio
- audio_field forbids mime_from_field
- audio_format_field requires audio_field
This is additive and backward-compatible — no catalog_version bump,
existing services parse unchanged. CATALOG-CONTRACT.md updated with
the new rows in the response-field table and a versioning-policy
row codifying that adding optional keys to response: doesn't bump.
kokoro-captioned re-shaped to use the new schema:
response:
type: audio
audio_field: audio
audio_format_field: audio_format
timestamps_field: timestamps
And marked status: experimental until the asset_engine consumer's
audio-with-timestamps renderer ships.
JSON Schema regenerated to reflect the new Pydantic shape.
Pydantic-model side of this change lives in the asset_engine repo
at src/asset_engine/catalog.py — committed there separately.
Per a request from the asset_engine consumer (althing thread
01KRCF4W66X3N24B01FF2Y7V3D), and verified against the live kokoro
OpenAPI + exercised endpoints:
* kokoro: version 1 → 2; adds three fields surfaced by the upstream
schema but not previously declared:
- speed (slider 0.25–4.0, default 1.0)
- volume_multiplier (slider 0.5–2.0, default 1.0; UI-bounded
since upstream is unbounded — noted in description)
- lang_code (text, optional override of the voice-name-derived
language hint)
* kokoro-captioned: new service entry wrapping
/dev/captioned_speech. Same model + image as kokoro proper but
separate catalog entry because the response shape is structured
JSON (audio inline as base64 + word-level timestamps), not raw
audio bytes. Verified shape captured in reproducibility.notes
so future consumers don't have to re-discover it. response.type
= json (consumer renders custom: player + subtitle overlay).
* reproducibility_audit: row added for kokoro-captioned.
Deferred (separate from this commit):
- kokoro-blend-voice. /v1/audio/voices/combine returns 403 on the
default config (allow_local_voice_saving=False); even with the
flag flipped it writes to a temp dir, not /worktank/kokoro/user_voices.
The persistent blend mechanism in this fleet is
playbooks/blend-kokoro-voice.yaml. Ad-hoc blending already works
through /v1/audio/speech via the inline syntax voice="a(w)+b(w)";
consumer can surface that as a UI affordance without any
catalog change.
catalog_version stays at 1 (no field-type vocabulary changes).
JSON Schema regeneration produced byte-identical output.
Adds the supporting infra around the service catalog now that it
has external consumers (the asset_engine UI being the first; CLIs,
monitoring, other services may follow):
- CATALOG-CONTRACT.md: the consumer-facing contract. Defines
versioning policy (catalog_version vs per-service version),
closed field-type and response-type vocabularies, recommended
vendor+drift-check sync workflow, known-consumers list, service
authoring notes.
- services.schema.json: JSON Schema (draft 2020-12) for the
catalog. Generated from the Pydantic model in
~/development/asset_engine/src/asset_engine/catalog.py via
`uv run scripts/dump_schema.py --publish`. Lets non-Python
consumers validate against the same shape.
- services.yaml: adds catalog_version: 1 at the root and reframes
the file's header to call out its first-class-contract status.
Quotes a vibevoice label that contained an unescaped colon
(caught by the asset_engine's strict YAML parser on first sync).
services.yaml: form-generator contract for the forthcoming
asset-generation UI. 13 inference services on irv-ml1 (TTS, ASR,
SFX, music) catalogued with field schemas extracted from Pydantic
models, response types, reproducibility audit, and license
warnings. ComfyUI flagged catalog-deferred (workflow-DAG API
doesn't fit a form-based UI without a per-asset-type wrapper).
design-brief.md: the prompt to give a frontend-design agent before
any pixels. Locks in the data-model decisions whose later cost is
asymmetric (asset-as-first-class entity, content-addressed output
storage, reproducibility hard requirement, job table, auth as a
no-op DI seam, API surface ≠ UI surface, schema versioning,
tags/collections plumbed in v1 with no UI). Defines a closed
field-type vocabulary (8 types) and response-renderer vocabulary
(6 types) — agent isn't allowed to extend them. Pre-decides the
required UI surfaces; leaves IA, library-nav pattern, long-job
UX, and big-form ergonomics open for the agent to opine on.
StableAudioPipeline isn't reentrant — concurrent requests share the
scheduler's step_index counter and corrupt each other mid-run
(observed: IndexError in cosine_dpmsolver_multistep when two requests
overlap). Wrap the pipeline call + audio decode in a single
asyncio.Lock created at startup, and run the (sync, GPU-bound)
pipeline call via asyncio.to_thread so the event loop stays
responsive. Concurrent requests now queue cleanly instead of racing.
Verified: 5 parallel POSTs at steps=50 all return 200, clear ~4s
serialization spacing (4, 8, 12, 16, 20s wall time), distinct
output hashes per seed.
server.py accepted cfg_scale in the request schema and the README
documented its 0–20 range, but the pipeline call never received it
— so changing cfg_scale between requests silently produced identical
output (the pipeline ran at its own default of 7.0 every time). Add
guidance_scale=req.cfg_scale to the pipe(...) call.
Verified: (prompt, seed, steps) held constant, cfg_scale=3.0 vs 15.0
now produce different SHA256s; same triple at cfg_scale=7.0 is
deterministic across repeated calls.
Wrapper only enumerates one voice directory (settings.voices_dir,
default /app/api/src/voices/v1_0 — inside the container's writable
layer, not bind-mounted). Override via VOICES_DIR=/app/user_voices
(host bind mount) and add a command shim that cp -r's built-ins from
the in-image v1_0 into user_voices on every start. Built-ins re-seed
fresh from the image (so upgrades that add voices propagate); custom
.pt files in user_voices are preserved (cp -r is additive).
Also adds scripts/blend_kokoro_voice.py + a playbook around it that
mirrors the wrapper's request-time voice="a(w)+b(w)" math but writes
the result as a named .pt to user_voices, making it discoverable via
GET /v1/audio/voices and persistent across recreate. Defaults to
athena = af_bella(2)+af_aoede(1) normalized.
Same shape as task-board: build-on-host from vh/vor, bind-mounted
persistence for sessions/ and responses/ (the user-published markdown
files), exposed at port 7879 (adjacent to task-board's 7878 since both
are claude-tooling sidecars).
Workflow template assumes the same DEPLOY_SSH_KEY + MGMT_REPO_TOKEN
secrets at user scope; nothing new to provision. Playbook accepts SHA
or branch refs (same fix as deploy-task-board.yaml) so manual runs
and CI runs share the same code path.
Centralized vs upstream-local: README documents the trade. Claude
fetches response markdown via /api/sessions/{id} JSON instead of a
local file read — the only API-flow change from the upstream README.
Runner is now re-registered with `:docker://node:20-bookworm-slim`
schema in its labels, so workflows targeting `pfi-fleet` get that
image automatically. Saves a few lines per workflow and gives us one
place (the runner config) to bump the default image when a new
node/debian release lands.
CI passes --var ref=<github.sha> (a full SHA), but the playbook
hardcoded `git reset --hard origin/{{ ref }}` which only works for
branch names — `origin/<sha>` is invalid syntax. Resolve ref via
git rev-parse with `^{commit}` (try origin/<ref> first for branch
names, fall back to bare <ref> for SHAs/tags) so manual runs (ref=main)
and CI runs (ref=<sha>) both work.
Same fix applied to the changed_when comparison so no-op reruns still
report ok instead of changed.
Runner's .runner registration cached :host mode at first start; env-var
label updates aren't sticky once the runner is registered. Until we
re-register with docker-schema labels, workflows must declare their
own container. node:20-bookworm-slim has node (for actions/checkout)
and apt (for python3-yaml + openssh-client install).
debian:bookworm-slim lacks node, so actions/checkout@v4 (a JS action
running dist/index.js) fails with `exec: "node": executable file not
found in $PATH`. Dropping the explicit `container:` directive lets
the runner use its label-default — node:20-bookworm-slim has node +
git out of the box. Install step shrinks to python3 + pyyaml +
openssh-client.
Central runner on ana-docker (gitea is local; existing fleet tooling
already SSHes from there). Playbook is parameterized so future
site-local runners (nh3-docker, esh-docker-vm) drop in via --var
overrides instead of copy-paste.
Includes a workflow template for vh/task-board that calls the existing
deploy-task-board.yaml playbook — keeps the playbook as the single
source of truth for "how task-board is deployed", manual or automated.
Labels embed `:docker://node:20-bookworm-slim` schema; without it,
act_runner v0.6+ silently falls back to host-mode and runs job steps
inside the Alpine runner container (no apt/python/node), breaking any
real workflow. node:20-bookworm-slim is small + has git + node so
actions/checkout works out of the box.
The applet outgrew "stack alongside the infra-management workspace" —
it has its own pyproject, multi-tenant deploy story, separate
release cadence, and isn't actually about managing infrastructure.
Lives at https://gitea.phasefinal.com/vh/nevermore now, with
provenance noted in its initial commit.
This commit removes:
stacks/news-digest/ (full stack tree)
playbooks/deploy-news-digest.yaml
scripts/add-digest-user.sh
The existing ana-docker deployment continues running on its baked
local/news-digest:v5 image — nothing changes for the live install
until you choose to redeploy from the new repo. Migration steps
(rename data dir, redeploy, retire old compose dir) are in
nevermore's README.
Updated:
README.md — Current stacks listing now points at the new repo
STATUS.md — milestones entry for the extraction
Replaced the 9-month-stale 'Last Updated: 2025-07-14' line with a
status disclaimer — this is KB-derived advisory material, not
something we maintain in lockstep with code. Tracer-date claim was
implying a freshness contract we don't fulfill. Found via
/tend-docs.
Same anti-pattern as the deleted model-list.md — a hardcoded snapshot
of mutable config that's guaranteed to drift. Replaced the 15-row
table with a one-liner pointing at stacks/llama-swap/conf/config.yaml
(the canonical source) and the live /v1/models HTTP endpoint. Found
via /tend-docs.
The compose-side default was still pinning qwen3.5-35-a3b — broken
on launch since its GGUF stopped working months ago. Real .env on
ana-docker overrides to granite-4-small so live deploys are unaffected,
but the default was misleading for anyone forking the stack. Found
via /tend-docs.
The original default model in .env.example was changed to
granite-4-small months ago when qwen3.5-35-a3b's GGUF file started
exiting on launch, but the README still named the old one as
"current". Also bumped the summarization-style description from
"one sentence" to "2-3 sentences" to match the post-trafilatura
prompt rewrite. Found via /tend-docs.
Bumped "Last updated" to 2026-04-29 and added a milestones section
for the audio-gen + news-digest applet + task-board + tooling work
that landed since 2026-04-24. Found via /tend-docs.
These four ana-docker stacks were missing from the discoverability
index — readers landing on the README couldn't find them without
walking stacks/. Found via /tend-docs.
Self-reported "Synchronized with llama-swap config.yaml on 2025-07-18"
— 9 months stale. Canonical model truth is stacks/llama-swap/conf/config.yaml;
the static snapshot was guaranteed to drift. No historical value (not
an ADR, post-mortem, or migration runbook).
Found via /tend-docs.
Stock neosmemo/memos:stable, port 5230, SQLite at
/opt/docker/conf/memos/data/. Joins traefik-net and ships homepage
labels (group=Notes) so it auto-appears on the dashboard via docker
discovery — no edit to configs/homepage/services.yaml needed.
First-run bootstrap is via the UI: visit http://10.250.50.70:5230
and create the Host account through the sign-up form.
Playbook idiom note: docker compose pull lines need the literal
block scalar (|) when the grep pattern contains colons — bare-string
shell value made YAML parse the colon as a mapping separator and
elway choked on first try.
Mirrors the reusable tooling (scripts/, generic playbook templates,
.gitignore, conventions section of CLAUDE.md) into a new directory
and strips everything fleet-specific: servers/, stacks/, configs/,
fleet-named playbooks (deploy-*, decouple-*), runbooks, status,
host-pinned scripts.
Output is an empty conventionally-organized workspace with fresh git
history, ready to populate with a different fleet. Skeleton
CLAUDE.md / README.md / STATUS.md are written with the new fleet
name baked in but no server table or placement rules pre-populated.
Deliberately does NOT create a remote or push — the user picks the
namespace + name explicitly via tea / git remote add.
scripts/fork-fleet.sh ~/development/acme-prod-management
scripts/fork-fleet.sh /tmp/test-fork test-fleet
The task-board v0.1.11 orange-while-waiting treatment depends on the
assistant pushing the pending-shells list — hooks can't enumerate
Claude Code's background tasks externally (confirmed against the
hook docs: no payload field, no state file, no lifecycle event, no
JSON form of /tasks). So the discipline lives in CLAUDE.md.
Adds a "Customizing the run schedule" section (DIGEST_CRON_AM/PM env
vars, edit-and-recreate flow) and a "Multi-tenant: one instance per
teammate" section covering scripts/add-digest-user.sh end to end:
what it does, the per-user file layout on ana-docker, idempotent
schedule/password updates, and the teardown path.
Updated the stale "two editions per day" intro line to note the
schedule is now configurable.
Hardcoded crontab → render at container start from
DIGEST_CRON_AM + DIGEST_CRON_PM. Defaults match the original
0800 / 2000 so existing deploys are no-ops.
scripts/add-digest-user.sh learns --am and --pm flags so each
teammate's stack can fire on their hours:
scripts/add-digest-user.sh bob --am "0 6 * * *" --pm "0 17 * * *"
scripts/add-digest-user.sh carol --pm "30 18 * * 1-5" # weekdays only
Standard 5-field cron syntax; busybox crond honors the container's
\$TZ. Removed the now-unused stacks/news-digest/crontab file and
the matching COPY in the Dockerfile.
Two pieces:
1) Multi-tenant onboarding via scripts/add-digest-user.sh
Shared miniflux + per-user digest stack. Onboarding a teammate
takes one command (plus a one-time sudo for dir creation):
scripts/add-digest-user.sh <username>
What the script does:
- Reads miniflux admin creds from ana-docker
- Allocates next free port (scans existing digest-*/.env)
- Generates a random password (or accepts one as 2nd arg)
- Creates the miniflux user via the admin API
- Materializes a per-user .env at /opt/docker/compose/digest-<user>/
(inherits NEWS_DIGEST_TAG from the canonical stack so all
tenants run the same image)
- Brings up `docker compose -p digest-<user> up -d`
- Seeds default world/local feeds in the new user's miniflux
- Triggers a first digest run
compose.yaml now uses ${DIGEST_PROJECT:-news-digest} to namespace
container_name + homepage labels. Default keeps backward-compat
for the singleton install — existing stacks unaffected.
2) Masthead overlap on phone widths
Desktop CSS pinned .masthead-edition to grid-row 1, which collided
with .masthead-brand once the mobile media query collapsed both
to grid-column 1. Result: "MORNING EDITION" badge stacked on top
of the "DAILY DIGEST" hero. Reset grid-row to `auto` for all
three masthead children in the ≤720 px breakpoint so they
auto-flow vertically.
Three things were broken on phones:
1. The collapse button I added to .desk-head had no grid placement,
so it auto-flowed into the desk-sub row and looked like a floating
chevron. Made the desk-head grid 4 columns explicit (num | title |
count | collapse) and pinned the button to col 4 row 1.
2. The 720px breakpoint was the only one — everything inherited
tablet rules at iPhone widths. Added a true-phone tier at
≤480 px that hides the section number badge and the rail
gutter, floats chips inline above the title, makes the jumpnav
horizontally scrollable for narrow widths, drops the edition
number, and bumps touch targets.
3. Long URLs / unbroken tokens could push horizontal overflow.
Added overflow-wrap: anywhere on titles + tldrs and overflow-x:
hidden on body as a belt-and-suspenders catch.
Two upgrades to make the digest actually readable:
1) Article-grounded 2-3 sentence summaries (everywhere)
The old prompt got just the title + miniflux's content excerpt,
which for HN/Lobsters/wire feeds is barely more than the title
itself — so summaries paraphrased the title and added nothing.
Now every URL gets fetched and main-content-extracted via
trafilatura on a parallel pre-pass (10 workers, ~15s for ~50
URLs). Extracted text caches to /output/.article-cache.json with
a 7-day TTL so repeat runs in the same window don't re-pull.
Headlines also get summarized now — one batched LLM call per
category (world / local). Rendered as a paragraph below the
title with source + time on the right rail.
Prompt rewrites tell the model to pull names/numbers/places
from the body and explicitly forbid restating the title.
Result: real specifics ("71% saw no pay increase globally",
"third time in less than two weeks", "Islamabad and Moscow
intermediaries") instead of title paraphrase.
2) Per-desk collapse buttons
Chevron next to .desk-count toggles a .is-collapsed class.
Collapsed state is per-device (localStorage by section id) since
collapse is a viewing preference, not content state.
Browsers were serving stale frontend assets after rebuilds, which hid
the new world/local headline desks: the OLD app.js's refreshCounts()
only counted .item children (not .headline), so the new headline desks
came up with visibleItems=0 and got the .is-empty class which is
display:none. Hard refresh fixed it but only for the user who knew
to do that.
Append ?v=<generated_at strftime> to both link/script tags in
digest.html.j2 and archive.html.j2 so every digest run produces a new
asset URL. Works with the existing entrypoint.sh static-asset sync —
no other infra needed.
Two new dense headline rails above the existing reddit/tech cards.
Designed for high-volume "what happened" coverage where the title
is the deliverable — no LLM summarization, ~15 items per section,
6-column-collapsing grid (title / source / time).
Digest pipeline:
* fetch_miniflux_headlines(category) — flat list per category, dedup
by lowercased title (different feeds syndicate the same wire stories)
* 8h look-back window (vs 12h for tech/reddit) since headlines move
faster
* cap of 15 per section (DIGEST_MINIFLUX_HEADLINES_MAX)
Frontend:
* .headline element parallels .item for the hide-button machinery
(both have data-id, both honored by app.js)
* dense 3-col layout collapses to 1-col on narrow screens
* jumpnav now numbers world=01, local=02, reddit=03, tech=04
Setup:
* seed-headlines.py — one-shot script (lives in the image at
/app/seed-headlines.py). Creates the World + Local categories in
miniflux, subscribes a curated feed list, and renames each feed
to a short display title (BBC vs "BBC News", "LA Times" vs "California").
Idempotent — reruns only add new feeds.
* Default world: BBC, NPR, Al Jazeera. Default local: LA Times Local,
LA Times CA, Voice of OC. (OC Register blocks miniflux; left out.)
* entrypoint.sh now syncs templates/{style.css,app.js,favicon.svg}
to /output on container start so frontend asset updates land
without a manual copy after rebuild.
Three upstream gaps surfaced once /generate was actually exercised:
1. infer-api.py builds an 18-arg positional tuple but the pipeline
expects 24 — first missing arg is `format`, so audio_duration
shifts into format's slot and the pipeline calls len() on an
int. Ship a patched copy of infer-api.py and COPY over upstream's
in the Dockerfile. Also handle empty lora_name_or_path -> "none"
(empty string trips HF Hub's repo-id validator).
2. torchcodec + ffmpeg are required by the WAV save path but neither
is in upstream requirements.txt. Without them every /generate
runs to completion and then 500s at write-time.
3. ACE-Step caches checkpoints at /root/.cache/ace-step/checkpoints
(HARDCODED, not honored by HF_HOME). Mount our persistent dir
there so the ~7 GB model survives container recreates.
Bench on A6000 (cached model, lo-fi hip hop, 60-step euler/apg):
10s @ 27 steps -> 9.4s (0.94x)
30s @ 60 steps -> 11.2s (0.37x, ~2.7x realtime)
60s @ 60 steps -> 14.8s (0.24x, ~4x realtime)
Two new audio-generation stacks alongside the TTS slate:
ace-step :8210 — Apache 2.0 music generation foundation model
(hybrid diffusion + LLM). Lyric-aware multi-minute songs. ~10-12 GB
VRAM during inference, A6000-pinned. Custom Dockerfile patches
upstream's torch/cu126 resolution bug (--extra-index-url cu126 was
falling back to pypi-default cu13 wheels, mismatching torchvision).
stable-audio-open :8211 — Stability AI 1.21B latent-diffusion SFX +
ambience. Up to 47s clips at 44.1 kHz. ~6 GB VRAM in fp16,
A6000-pinned. Custom FastAPI shim around diffusers' StableAudioPipeline
(no upstream HTTP server). Dockerfile pins torchsde explicitly —
diffusers doesn't pull it as a hard dep but
CosineDPMSolverMultistepScheduler needs it.
Three deploy iterations + four backend attempts (subprocess CUDA,
resident-server CUDA, Vulkan rebuild) all failed to deliver speedup
over fish-s2:
* CUDA path: ggml_cuda_init succeeded, weights loaded onto GPU per
s2's logs, but nvidia-smi showed 0% utilization during synthesis.
Wall time 20s/long phrase vs fish-s2's 7.5s. The "CUDA get_rows
unsupported for type q6_K" warning hints at incomplete op coverage
in s2.cpp's alpha CUDA backend for fish-speech architecture.
* Vulkan path: vk::IncompatibleDriverError on container init. NVIDIA
Vulkan ICD not accessible inside the container despite
NVIDIA_DRIVER_CAPABILITIES=compute,utility,graphics. Would need
host-side nvidia-utils-vulkan installation or manual ICD bind
mount. Didn't pursue.
Both are fixable — CUDA needs op coverage upstream (author actively
working on it; "selective embedding dequant" commit landed 16 days
ago), Vulkan needs host-side ICD setup. Neither is a config-flip,
both are real work for marginal-or-zero return. Better to delete the
stack and revisit when s2.cpp matures or when we tackle FP8
quantization on ana-ml2's RTX 6000 Ada (sm_89, native FP8 hardware).
Local image rmi'd, /opt/docker/compose/fish-cpp removed on irv-ml1.
/worktank/fish-cpp left for user-side sudo cleanup.
Future Fish acceleration paths (in order of decreasing certainty):
1. Wait for s2.cpp CUDA op coverage to mature (track upstream commits).
2. Quantize Fish BF16 → FP8 via TransformerEngine, deploy on
ana-ml2's RTX 6000 Ada (Ada has native FP8 tensor cores, A6000
doesn't). ~2x speedup if it works.
3. vLLM port of Fish (no upstream support today).
CUDA backend confirmed broken for fish-speech ops on s2.cpp v0.x — alpha,
incomplete op coverage, GPU stays at 0% during generation despite
ggml_cuda_init succeeding. Vulkan was the original README example
(`-v 0`), so likely the more battle-tested path.
Build the image with BOTH backends so we can flip via env without
rebuilding:
* libvulkan-dev + glslc in the build stage (GGML's Vulkan backend
compiles its shaders with glslc at build time; without it the
cmake configure silently disables Vulkan).
* libvulkan1 + the libggml-vulkan.so copy in the runtime stage.
* compose env NVIDIA_DRIVER_CAPABILITIES=compute,utility,graphics —
default nvidia-container-toolkit only mounts compute libs; Vulkan
needs the graphics ICD (libGLX_nvidia + nvidia_icd.json) too.
* entrypoint reads FISH_CPP_BACKEND (cuda/vulkan/cpu) and selects
the appropriate -c/-v/no-flag invocation.
* Default backend = vulkan.
Subprocess-per-request architecture forced CUDA + model load on every
/v1/tts call (~10-20s init, then 5-15s generation). Even though CUDA
is now actually being used (`-c 0` fix landed), 32s for "Verify."
proved per-request init was the bottleneck.
s2.cpp ships a built-in HTTP server (`--server -H -P`) that keeps the
model resident on the GPU. Refactor:
* entrypoint.sh — backgrounds `s2 --server -P 3030 -c 0 -m ... -t ...`,
waits for it to bind 3030, then foregrounds uvicorn. tini supervises
via `wait -n` so either child dying takes down the container.
* server.py — drops subprocess.run; instead httpx-POSTs Fish-shaped
/v1/tts JSON to s2's localhost:3030/generate (multipart form: text
+ optional prompt_text/prompt_audio for cloning). Model load + CUDA
init now happen once at container start, not per-request.
* Dockerfile — added httpx (shim dep), curl (entrypoint readiness
probe), and the entrypoint.sh COPY+chmod. CMD now invokes
entrypoint.sh instead of uvicorn directly.
* deploy-fish-cpp.yaml — uploads entrypoint.sh alongside server.py.
s2.cpp's README example uses `-v 0` which is `--vulkan 0` (Vulkan
device 0), easy to misread as "voice 0". The shim copied that
verbatim, so even after fixing the libcuda.so build problem AND the
libgomp.so runtime dep, every synthesis ran on CPU because the wrong
backend was selected.
Direct verification: `[Model] NPU not compiled, falling back to CPU`
in stderr; nvidia-smi showed no s2 process; bench timed out at 60s
on phrases that fish-s2 (HF, GPU) does in 7s.
s2.cpp's CLI:
-v <id> = --vulkan <device>
-c <id> = --cuda <device>
-M = --metal (Apple Silicon)
Switched the shim to `-c 0`. The CUDA backend IS in the build (-DS2_CUDA=ON
worked, libggml-cuda.so links fine per ldd, libcuda.so.1 mounts at
runtime via NVIDIA container runtime) — just wasn't being told to use it.
Build succeeded after the libcuda.so symlink fix, but the first
/v1/tts request returned HTTP 500 with:
s2 binary failed (rc=127): /usr/local/bin/s2: error while loading
shared libraries: libgomp.so.1: cannot open shared object file
CMake auto-enabled OpenMP during the build (gcc's -fopenmp flag), so
the s2 binary dynamically links libgomp.so.1. The build-stage devel
image had it; the slim cuda:runtime base doesn't ship it by default.
Adding libgomp1 to the runtime image's apt install resolves it.
Second attempt's CMAKE_LIBRARY_PATH + LIBRARY_PATH didn't get picked
up by ggml's nested CMake — same linker errors as the first run.
Robust fix: symlink the stub at /usr/local/cuda/lib64/stubs/libcuda.so
into /usr/local/lib (which ld searches unconditionally) and provide
both libcuda.so AND libcuda.so.1 (the SONAME ggml-cuda's
libggml-cuda.so links against). ldconfig refreshes the cache.
The symlinks live only in the build stage. The runtime image inherits
the real driver-provided libcuda.so.1 via NVIDIA's container runtime
mount, so the stubs never get used at execution time.
Two issues from the first deploy attempt:
1) Build failure (real): linker errors on s2.cpp's CUDA build —
undefined references to cuMemSetAccess, cuDeviceGet, etc. These
are CUDA Driver API symbols (in libcuda.so), not Runtime API
(libcudart.so). The driver lib is provided by NVIDIA's container
runtime at RUN time, not BUILD time.
Fix: nvidia/cuda:devel images ship a stubs library at
/usr/local/cuda/lib64/stubs/libcuda.so that provides the symbols
for linking but is non-runnable. Adding that path via
LIBRARY_PATH + CMAKE_LIBRARY_PATH lets the linker resolve while
leaving runtime unchanged (real libcuda.so comes from the
driver mount).
2) Verify false positive: the /v1/tts verify step's last command was
`rm -f "$out"` — which always exits 0. This made the shell's
final exit code 0 regardless of whether curl/file/grep succeeded,
so verify reported OK even when nothing was running on host_port.
Fix: `set -e` at top + trap-based cleanup. Failures now propagate;
the rm still runs on either path via EXIT trap.
New stack scaffolding for the Fish quantized-realtime experiment. Not
deployed yet — this commit lands the canonical files; deploy follows.
Architecture decisions made in Phase 1:
* CUDA backend, NOT Vulkan. s2.cpp's CMakeLists exposes both
-DS2_VULKAN and -DS2_CUDA; the most recent upstream commit
(2026-04-12) was specifically about CUDA improvements, and CUDA
on the A6000 will be substantially faster than Vulkan for ML
matmul. -DS2_CUDA=ON in the Dockerfile build args.
* Pinned to s2.cpp commit e48ce8e02d8335bd9a0ba94679f605724b31d12
(2026-04-12 HEAD of main). Repo is alpha software per README;
pin tightly so future churn doesn't break our build. Bump
deliberately when wanting upstream improvements.
* Multi-stage Dockerfile: nvidia/cuda:12.6.0-devel for build (needs
CMake + ninja + git + the CUDA toolchain) → nvidia/cuda:12.6.0-runtime
for serve (slimmer; just the s2 binary + GGML libs + a small Python
shim). Cuts image size by ~50% vs single-stage devel.
* FastAPI shim (server.py) wraps s2.cpp CLI in Fish's `/v1/tts`
contract so the same bench harness + clients work against fish-cpp
with no changes. Per-request flow: decode optional reference WAV
from base64 → write to temp → subprocess.run the s2 binary → stream
resulting WAV back. Adds ~50-100ms per-request fork+exec overhead;
negligible vs the multi-second generation cost.
* `streaming: true` accepted in request body but IGNORED — s2.cpp
writes a complete WAV before returning, so chunked output isn't
available. Unlike fish-s2 (HF wrapper) where streaming drops TTFB
to 26ms, fish-cpp's TTFB ≈ total wall time. Speed depends entirely
on raw generation throughput.
* q6_k as default quant — sweet spot per typical GGUF guidance:
near-bf16 quality at ~5GB. Other variants (q4_k_m, q5_k_m, q8_0,
f16) selectable via FISH_CPP_MODEL env.
* Pinned to GPU 1 (A6000) by default to share with fish-s2 for
direct A/B benching. q6_k weights ~5GB + runtime ~3GB ≈ 8GB —
comfortable on either GPU.
* Port 8199 (next free in the irv-ml1 TTS slate).
Phase 2 (next) is the actual deploy + first build. Reserved 30-45 min
for cold-cache build + weights pull.
Voxtral final fix (8th iteration):
* The bundled voxtral_tts.yaml hardcodes gpu_memory_utilization: 0.8
on the language_model stage — overrides the CLI flag. Mounted a
patched copy (0.4) at /etc/voxtral/voxtral_tts.yaml and pointed
--stage-configs-path there.
* With Kyutai stopped to free 5 GB on the 3090, both stages fit
(target 9.4 + 2.4 GB ≈ 11.8 GB; 17 GB free post-kyutai-stop).
* Voxtral now healthy on GPU 0 — bench: 1.9-2.7 s TTFB, real WAV.
Fish s2-pro optimization (per-request sweep, no model swap):
* `streaming: true` in request body drops TTFB from 7.7 s → 0.026 s
(300×). Total time goes up ~1 s (chunked HTTP overhead) but
perceived latency = TTFB. Use stream:true for any interactive use.
* `latency: "balanced"` actually slower than default — bad name; skip.
* `use_memory_cache: "on"` no measurable benefit.
* `chunk_length: 100` (default 200) no TTFB benefit non-streaming.
* Server-side `--half` (fp16 inference) added via compose `command`
override — passes through start_server.sh's $@ unchanged into
api_server.py. Should reduce total time too. Validation pending
the post-restart bench.
Kyutai stopped to free GPU 0 budget — the bench numbers earlier
(3.4 s avg) were unimpressive vs Voxtral's 2.3 s in the same
multilingual slot. Kept the stack files for future re-deploy if
needed; just the running container is gone.
Fourth attempt finally found the right invocation. Voxtral is a
two-stage TTS pipeline (language_model → acoustic_transformer →
audio output), not a flat MistralForCausalLM. Standard `vllm serve`
errored with "no module named 'acoustic_transformer'" because it
loads the model as a vanilla Mistral causal LM.
Pattern from /workspace/vllm-omni/examples/online_serving/
qwen3_tts/run_server.sh (closest in-image analog):
vllm-omni serve <MODEL> \
--stage-configs-path vllm_omni/model_executor/stage_configs/voxtral_tts.yaml \
--host 0.0.0.0 --port 8000 \
--gpu-memory-utilization 0.45 \
--trust-remote-code --omni
Key differences from previous attempt:
* `vllm-omni` binary, not `vllm`
* `--omni` flag activates multi-stage pipeline
* `--stage-configs-path` points at the bundled YAML that maps
stages to GPU + scheduler + worker classes
* Dropped --load-format/--tokenizer-mode/--config-format=mistral
flags — the stage config handles tokenizer_mode internally
* --trust-remote-code is required for the acoustic_transformer
custom code path
Default .env.example now: GPU 0 (3090) with util 0.45 (~10.6 GB
target on 24 GB GPU). The A6000 is fully booked by Fish s2-pro.
Third voxtral attempt: image pulled clean (3 min, v0.18.0), entrypoint
parsed correctly, vLLM started, but engine init failed two ways:
1. HF rate-limited the irv-ml1 IP (38.120.94.3) during the metadata
fetch — 429 Too Many Requests from too many large unauthenticated
pulls today (heretic, 27b, fish-s2, fish-s1-mini, voxtral). Added
HF_TOKEN env passthrough; user generates a token at
https://huggingface.co/settings/tokens and sets VOXTRAL_HF_TOKEN
in .env.
2. Voxtral uses Mistral's native model format (params.json +
tekken.json tokenizer + consolidated.safetensors single file),
NOT HF transformers format (config.json + tokenizer.json + sharded
.safetensors). vLLM errored with "ensure presence of params.json
for Mistral models." Fix: pass --load-format=mistral
--tokenizer-mode=mistral --config-format=mistral to vllm serve.
Confirmed by inspecting the Voxtral-4B-TTS-2603 HF tree:
25 files, ships params.json + tekken.json + consolidated.safetensors.
Both fixes baked into compose. User needs to drop their HF_TOKEN into
.env once and recreate.
Side note discovered while debugging: fish-s2 s1-mini variant uses
the tiktoken tokenizer format; the wrapper can't load it (errors with
"NoneType has no attribute encode" on warmup). So s1-mini isn't a
drop-in optimization for s2-pro — different code path needed. Fish
back on s2-pro for now.
Second voxtral attempt got past the image pull (v0.18.0 published,
~3 min download) but container init failed:
unable to start container process: error during container init:
exec: "--model=mistralai/Voxtral-4B-TTS-2603": stat ...: no such file
vllm/vllm-omni:v0.18.0 has Entrypoint=null AND Cmd=null — there's no
default executable. The compose's `command:` array becomes the full
exec invocation, with --model=... interpreted as the binary name.
Standard vLLM serving CLI is `vllm serve <model> [flags]`. The
binary's at /usr/local/bin/vllm. Set entrypoint: ["vllm", "serve"]
and pass the model as a positional arg.
While we're here: HF cache was empty too (Voxtral 4B BF16 ~8 GB
download on first start) — vLLM auto-downloads from HF on model
load, so no separate pre-pull step needed.
Three fixes from the second-wave deploy attempts:
* voxtral: vllm/vllm-omni doesn't publish a `latest` tag — pull
failed with "manifest unknown". Pinned VOXTRAL_VLLM_TAG to v0.18.0
(released 2026-03-29, the day after the Voxtral 4B TTS release —
first cut with Voxtral support).
* kyutai-tts: NillPointer wrapper exposes ONLY /health (root) and
POST /v1/audio/speech. No /v1/models, no /v1/audio/voices —
those return 404. Verified by /openapi.json against the live
container. Compose healthcheck + playbook wait + verify steps
all repointed at the actual paths. POST /v1/audio/speech is now
smoke-tested with a RIFF WAV assertion (same pattern as fish-s2).
* fish-s2: added FISH_S2_MODEL env var so the model variant is
swappable via .env without rebuilding. Both s2-pro (default) and
s1-mini are pre-pulled into the bind-mount; LLAMA_CHECKPOINT_PATH
+ DECODER_CHECKPOINT_PATH now use ${FISH_S2_MODEL:-s2-pro}.
s1-mini was originally gated on fishaudio's HF org (401), but
niobures/OpenAudio-S1 mirrors the same files openly — pulled
from there via a one-shot snapshot_download.
After getting fish-s2 finally healthy on attempt #5, the playbook's
verify still failed because /v1/audio/voices doesn't exist. Discovery:
the Fish wrapper has a custom API surface, not OpenAI-compatible.
Real endpoints:
POST /v1/tts — synthesis (text body, optional `references`
field for voice cloning, returns audio/wav)
GET /v1/health — liveness (used by Docker healthcheck)
GET /heartbeat — alternate liveness signal
GET / — Swagger Editor UI for the OpenAPI spec
No /v1/audio/speech, /v1/audio/voices, /v1/models — those return 404.
Updated:
* Playbook verify — replaced the JSON-shape /v1/audio/voices check
with a POST /v1/tts smoke that asserts a real RIFF WAV comes back.
* README API section — replaced the OpenAI-compat examples with
Fish's actual {"text":"...","references":[...]} body shape.
* README disk footprint — corrected ~9 GB → ~11 GB (codec.pth was
larger than I estimated; 1.9 GB + 9 GB safetensors).
* README Lessons learned section — recorded the 5-iteration deploy
story so the next time we touch a Fish-style upstream we don't
re-walk the dockerfile / target / pre-pull / API-shape traps.
Fourth fish-s2 attempt got past build + checkpoints, then container
crashlooped silently again. Diagnosis: the upstream docker/Dockerfile
is multi-stage with `webui` and `server` targets; without specifying
a target, docker builds the LAST stage (webui — gradio-only, no
start_server.sh, no API server). start_server.sh is the entrypoint
script that lives only in the `server` stage.
Confirmed by `cat /app/start_server.sh` inside the built image:
"No such file or directory."
Upstream's compose.yml uses target: server on its server service —
doing the same here.
Third deploy attempt got past the build but crashlooped at container
start: Fish's start_server.sh validates checkpoints/s2-pro/ exists
and exits cleanly (rc=0) if missing — no auto-download, no helpful
message. /worktank/fish-s2/checkpoints/ was empty, so the container
exited every ~52s under restart policy.
Added an idempotent pre-pull step using the same one-shot
python:3.12-slim + huggingface_hub.snapshot_download + hf_transfer
pattern we used for the Qwen 3.6 GGUFs earlier today. Pulls the 9
relevant files (~11 GB total: codec.pth + 2 safetensors shards +
config + tokenizer/template) directly into the bind-mount at
/worktank/fish-s2/checkpoints/s2-pro/ — gated by `creates:` on
codec.pth so the pre-pull step is a no-op on reruns.
~83 s wall-clock for the 11 GB pull on first deploy.
Wrapped .masthead-brand in <a href="index.html"> in both digest.html.j2
and archive.html.j2 so the hero is a clickable shortcut to the latest
edition. Useful when reading an archived edition and you want to jump
back to the freshest one without going through the archive list.
CSS: color: inherit + text-decoration: none keeps the visual
identical; hover drops opacity to 0.85 for affordance; focus-visible
gets an accent outline so keyboard nav is discoverable.
Second deploy attempt failed at build time:
failed to fetch anonymous token: ... ghcr.io/fishaudio/fish-speech ... 403 Forbidden
Root cause: dockerfile.dev is a thin two-line wrapper around
`FROM ghcr.io/fishaudio/fish-speech:${VERSION}`, which is a private
GHCR base image. Anonymous pulls 403, and we'd need GHCR auth to use
that path. The dev variant is meant for upstream's CI / fish-speech
contributors, not external consumers.
The REAL production path (from upstream's compose.base.yml) is to
build from `docker/Dockerfile` with build args BACKEND=cuda,
CUDA_VER=12.9.0, UV_EXTRA=cu129, UV_VERSION=0.8.15. That builds
everything from source — slower (15-20 min cold), but fully self-
contained.
irv-ml1's driver (595.58.03, CUDA 13.2 capable) is forward-compatible
with the 12.9 PyTorch wheels.
Took three iterations to find the right Dockerfile because:
1. First try: dockerfile (lowercase) — doesn't exist
2. Second try: dockerfile.dev — exists but pulls a private base
3. Third try: docker/Dockerfile — actual production path
First fish-s2 deploy attempt failed in step 9/11:
failed to read dockerfile: open dockerfile: no such file or directory
Upstream fishaudio/fish-speech ships:
* dockerfile.dev (lowercase, dev/test image)
* compose.yml + compose.base.yml (intended deploy path:
`docker compose --profile server up`)
There is no standalone production Dockerfile. The dockerfile.dev
image is what their own compose.yml builds from anyway, so building
against it directly is functionally equivalent to using their compose
profile — we just keep our own restart-policy / labels / bind-mount
conventions on the outer compose.
Comment in the build block now documents this so future-Claude doesn't
re-walk the path.
Adds the three premier 2026 TTS releases we missed during the original
fleet build-out (early April), all licensed for self-host:
* Fish Audio S2-Pro (port 8195, GPU 1 / A6000) — released 2026-03-09.
4B dual-AR (Slow + Fast) trained on 10M+ hours / 80+ languages.
Headline: 15,000+ paralinguistic / emotion tags via natural language
([laugh] [whispers] [super happy] etc.) — a step-function over
Chatterbox Turbo's 9 fixed tags. 91.61% paralinguistic win rate on
EmergentTTS-Eval. ~150 ms streaming TTFB, voice cloning, MIT-style
open. ~17 GB VRAM.
* Voxtral TTS (port 8197, GPU 1 / A6000) — Mistral, released 2026-03-28.
4B open-weight, 70 ms model latency, 9.7× realtime. 68.4% blind A/B
win rate vs ElevenLabs Flash v2.5 in cloning. 8 languages
(EN/FR/DE/ES/IT/PT/NL/HI). Served via vLLM-Omni (Mistral's partner
serving stack) — published Docker image, no local build. ~16 GB VRAM.
CC BY-NC license — personal/research use only; flagged in README.
* Kyutai TTS (port 8198, GPU 0 / 3090) — kyutai/tts-1.6b-en_fr.
Trained on 2.5M hours from the Moshi/Mimi team. Claimed 220 ms in
solo setup, 32 simultaneous streams under 350 ms on L40. Kyutai's
official deploy is Rust + websockets only; using NillPointer's
community OpenAI-compat wrapper to bridge to /v1/audio/speech so
it slots into the same bench harness. ~4-6 GB VRAM.
Each stack: compose.yaml (build context, env, volumes, healthcheck,
homepage label), .env.example (all tunables documented), README.md
(why it exists, headline numbers, API, deploy + hardware notes).
Playbooks at playbooks/deploy-{fish-s2,voxtral,kyutai-tts}.yaml are
idempotent in the same shape as the existing deploy-vibevoice /
deploy-chatterbox playbooks.
Port allocations on irv-ml1 after this lands: 8188 ComfyUI, 8190
CosyVoice, 8191 Qwen3-TTS, 8192 IndexTTS-2, 8193 Kokoro, 8194
VibeVoice, 8195 Fish, 8196 Chatterbox, 8197 Voxtral, 8198 Kyutai,
8765 Parakeet ASR.
Investigation of the slow (8-12s) qwen3-tts TTFB found the upstream
wrapper has 5 backend options. The advertised path to fast TTFB is
TTS_BACKEND=optimized (torch.compile + CUDA graphs + real-time
streaming). It loads cleanly but crashes the container during its
hardcoded warmup phase — silent exit (ExitCode 0, no traceback,
no OOM kill), repeats every ~22s under restart policy.
TTS_WARMUP_ON_START=false suppresses the factory-level warmup but
the optimized backend has its own internal warmup that fires
regardless and triggers the crash.
Updated the .env.example block to enumerate all 5 backend options
with their actual current behavior so future-Claude doesn't re-walk
this path. official is staying as the default.
The wrapper's `optimized` backend (torch.compile + CUDA graphs +
real-time streaming) reads its model registry from a YAML config:
default path is ~/qwen3-tts/config.yaml inside the container, which
doesn't exist. Without TTS_CONFIG set, the backend boots with an
empty registry and every synthesis request fails with
"Unknown model key: '<name>'. Available: []".
The repo ships /app/config.yaml with all 4 model variants defined.
Pointing TTS_CONFIG at it lets the optimized backend load cleanly.
This is a prerequisite for benching the optimized backend properly
— it's the path to the upstream's claimed 97 ms streaming TTFB. The
default `official` backend uses naive HF transformers autoregressive
generation that pegged GPU at only 27% utilization and gave us 8-12 s
TTFB on bench (no recompile theory needed — same phrase repeated 4x
plateaued at 8.5 s, ruling out shape-specific recompilation).
qwen3-tts: deploy was using the -Base checkpoint, which sounds like
the right one ("supports voice cloning") but the upstream wrapper's
only synthesis path goes through generate_custom_voice. The -Base
variant doesn't expose that, so every request — including ones with
the wrapper's listed built-in voices like Ryan/Vivian — errored with
"does not support generate_custom_voice". The -CustomVoice variant
exposes both the cloning machinery and the preset voices, and is
what the wrapper actually needs.
The .env.example comments had the variant labels backward; fixed in
this commit. Live host already updated to -CustomVoice via direct
.env edit (model downloaded on container restart).
chatterbox README listed [whisper] and [breath] as supported tags —
those are in the base Chatterbox tag set but NOT in the Turbo set
that's actually loaded. Replaced with the canonical 9-tag list
verified against /api/model-info: laugh, chuckle, sigh, gasp, cough,
clear throat, sniff, groan, shush.
New runbook captures the three-phase process:
Phase 1 — Drop --append-only via DSM Container Manager web UI
Phase 2 — sudo resticprofile forget --prune --verbose on each of
nh3-docker, nh3-dev, irv-ml1 (interactive sudo per host)
Phase 3 — Restore --append-only via DSM
Why each phase looks the way it does, what to expect (largely no-op
runs for the first 6 months while no snapshots have aged out of the
keep window), how to verify each phase non-destructively (curl 401
on the rest-server root proves the container's up + serving), what
to do if Phase 2 fails with `repository is configured as append-only`
(skipped Phase 1 / DSM didn't apply), and the path to future
automation (find docker bin path on DSM, NOPASSWD-lock syncuser to
the specific recreate command).
Includes a "last run history" table seeded with today's first
post-pipeline run (no-op, irv-ml1 only had 3 snapshots due to the
04-25→27 CUDA stall).
Cross-referenced from docs/README.md (runbook tree), docs/
orientation.md (where-to-look table), and STATUS.md item 9 (which
now points at the runbook + records the next-round date 2026-07-27).
Both reported (unhealthy) in docker ps. Two distinct root causes:
* news-digest-web: switched from nginx:alpine to python:3.12-alpine
(uvicorn) but kept the wget healthcheck against `localhost`. Alpine's
/etc/hosts maps localhost to BOTH ::1 and 127.0.0.1; busybox wget
tries IPv6 first, hits "connection refused" because uvicorn binds
IPv4-only, and doesn't fall back. Pinned to 127.0.0.1.
* chatterbox: devnen's image is built from a python:3.10 base and
doesn't ship curl, so `curl -fsS http://localhost:8004/api/model-info`
failed with `/bin/sh: 1: curl: not found`. Replaced with a python
urllib one-liner that fetches + asserts `b'"loaded":true' in body`,
also pinned to 127.0.0.1 to dodge the same IPv4/IPv6 race.
Both YAML extractions tested directly inside the running containers
(via `sh < script`) — chatterbox python check returns 0 when the model
is loaded.
Big update for 2026-04-27. Sections added:
* Marked the "🟥 Blocked — irv-ml1 stalled" header as RECOVERED with
resolution notes (driver 595.58.03 / CUDA 13.2 IS working, both GPUs
detected; the original "stall" must have been a one-shot
post-install hiccup that resolved on a later boot).
* New "Session milestones — 2026-04-27" section covering:
- irv-ml1 unstall + 5 pre-existing GPU stacks restored
- Kokoro GPU variant deployed (irv-ml1:8193) with the .env.example
default flipped to gpu now that the driver works
- VibeVoice 1.5B deployed (irv-ml1:8194) after fixing two bugs:
full 40-char SHA required by buildx + verify regex didn't match
the OpenAI list-format response shape
- Chatterbox Turbo deployed (irv-ml1:8196) after fixing three:
upstream moved Dockerfile path (docker/Dockerfile.gpu →
Dockerfile.cu128 at root), pinned to current SHA instead of `main`,
/health doesn't exist (switched all probes to /api/model-info
which is the wrapper's own ready-after-loaded signal)
- llama-swap qwen3.6 ttl removal across non-pinned variants;
qwen3.6-35-a3b unpinned (was OOM'ing other loads via the pinned
group's persistent: true flag); granite-4-small added to the
pinned group to stop it swapping with qwen3.6-27b
- Backup verification: all three layers green (per-host restic,
PBS-ANA, PBS-NH3 mirror — 2026-04-27 snapshots everywhere). Noted
that backrest's empty dashboard is expected (no plans configured;
the actual orchestration is the per-host resticprofile timers).
Symptom: granite-4-small and qwen3.6-27b were evicting each other
when called in alternation. granite is the news-digest curator (fires
twice daily on cron) — being evicted means a cold reload (~5s) on
every digest tick, plus visible churn whenever the user uses 27b
concurrently.
Added granite-4-small to the `pinned` group as a persistent member.
~5-6 GB at Q4_K_M + 120K KV ≈ comfortable inside the existing pin
budget (qwen3.5-9b ~6 GB → ~12 GB total persistent). Single RTX 6000
Ada is 48 GB, leaves ~36 GB headroom for whichever non-pinned model
the user invokes (qwen3.6-27b at ~30 GB fits cleanly).
Updated the pinned group's docstring to capture the current member set
+ VRAM math + the historical context (qwen3.6-35-a3b was here, was
too heavy, got removed yesterday). Marked the granite ttl: 0 with the
matching "pinned — never unloads" comment as the other group members.
Symptom: qwen3.6-35-a3b refused to deload when other models needed
the VRAM, even with the model itself at ttl: 0. The pinning came from
the `pinned` group's `persistent: true` flag, which exempts members
from eviction by the scheduler regardless of memory pressure. The
model's ttl: 0 only governs idle-timeout, NOT scheduler eviction —
those are separate concerns.
Removed qwen3.6-35-a3b from the group's members. Kept ttl: 0 on the
model itself: still no idle-unload, but the scheduler CAN now evict
it when another non-coexistent model is requested. qwen3.5-9b stays
pinned (~6 GB at Q4 — cheap to hold).
Updated the inline comment + the group-header docstring to reflect
the new semantics so future-Claude doesn't undo this.
The base qwen3.6-35-a3b is already ttl: 0 via the `pinned` group.
The three other Qwen 3.6 variants (abliterated, heretic, 27b) had
ttl: 600 → llama-swap auto-unloaded them after 10 min idle, costing
the next request a full reload (~5-15s). Removed so they stay loaded
once warm. Still get evicted by the normal swap when another
non-pinned model is requested — these aren't joining the pinned group,
just losing their idle-unload timer.
devnen/Chatterbox-TTS-Server doesn't expose /health — neither in code
nor OpenAPI. The deploy hung on the playbook's `Wait for /health to
respond` loop indefinitely (each curl -> 404, retry forever) even
though the container was up and the model loaded clean to CUDA at
22:52:21 (~42s after start).
/api/model-info returns `{"loaded":true,...}` only after the model
finishes loading, so it doubles as liveness + readiness. Updated:
* compose.yaml healthcheck — grep for `"loaded":true` from
/api/model-info.
* playbook wait step — same probe instead of /health.
* verify /health → verify /api/model-info reports loaded.
* verify /v1/audio/voices — switched from greping for `voice|alloy|echo`
literals to parsing JSON and asserting the actual response shape:
`{"status":"ok","voices":[...]}` (devnen's shape — note this is NOT
the OpenAI list-format vibevoice uses).
Build + container + /health all came up clean on the re-run; only the
voices-endpoint verify failed. The check greped the response body for
"voices"/"voice"/alloy/Carter — but VibeVoice's actual response shape
is OpenAI list-format `{"object":"list","data":[...]}`, which contains
none of those substrings. On a fresh install the data array is also
empty (voices live at /worktank/vibevoice/voices/ and the user seeds
them).
Switched the check to parse the JSON and assert the shape (object="list",
data is a list). Robust against empty voices, robust against future
schema additions.
Both deploys failed against irv-ml1 today with upstream-changed-on-us
errors:
* vibevoice: VIBEVOICE_SHA=7614c469a145 (12-char short) made docker
buildx report "repository does not contain ref 7614c469a145" — same
commit IS still HEAD of main, but buildx's git source resolver
doesn't accept short hashes even when unambiguous. Now full 40-char.
* chatterbox: dockerfile: docker/Dockerfile.gpu — devnen restructured
the repo to put Dockerfiles at root, renamed by CUDA version
(Dockerfile.cu128, .cpu, .rocm). Switched to Dockerfile.cu128 (GPU
build for CUDA 12.8 toolkit; works on irv-ml1's 595.58.03 driver).
Also pinned CHATTERBOX_SHA to a full 40-char SHA instead of `main`
so future upstream churn doesn't break the deploy without warning.
Live host .env files patched directly (the playbook only seeds .env
when absent, so canonical edits don't propagate to existing installs).
vh