After five hotfix iterations on the WhisperX install (v0.12.0:0–:4) we
never got a working docker build. The fundamental constraint isn't
patchable from outside NVIDIA: NGC PyTorch on ARM64 (the only base that
runs on Spark 2's GB10 Blackwell) ships a custom-versioned torch
2.10.0a0+b558c98 that has no pre-built torchaudio match anywhere.
WhisperX → pyannote → torchaudio is a hard dependency chain we couldn't
satisfy without rebuilding torchaudio against torch 2.10's alpha API.
Walking away cleanly is better than another night of chasing.
Removed from the codebase:
- image/whisperx_container/* (Dockerfile + requirements + app/main.py)
- image/app/whisperx_install.py (install manager + SSH ship-context logic)
- image/Dockerfile COPY whisperx_container
- WHISPERX_* config keys in config.py
- whisperx service entry in services.py
- WhisperX-preferred branch in audio_proxy.py
- /api/whisperx/* endpoints in server.py
- install banner + progress dialog in index.html
- render + handlers in app.js
- .whisperx-install styles in style.css
Spark 2 cleaned in tandem (user-authorized): container removed,
~/whisperx-build/ removed, 5.4 GB of dangling image layers + 1.3 GB of
builder cache reclaimed. parakeet-asr and magpie-tts unaffected and
healthy throughout.
The audio path is back to exactly what shipped in v0.11.0:3:
POST /api/audio/transcribe-with-speakers
→ Parakeet (transcription) + Sortformer (diarization) in parallel
→ merged by timestamp into speaker-labeled blocks
v0.13.0:1+ will add the actually-needed fixes that the WhisperX detour
was meant to address:
1. memory cap on the parakeet-asr container so a long-audio crash
can't swap-thrash Spark 2 again
2. a chunking proxy in /api/audio/transcribe-with-speakers that
splits inputs >10 min before Sortformer
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Replaces the manual rsync+build+run with a proper spark-control feature.
First in the audio path that doesn't require shell access on Spark 2.
What's in the box
─────────────────
* image/whisperx_container/ - the build context (Dockerfile, requirements,
app/main.py FastAPI wrapper). Mainline pipeline: faster-whisper for STT +
pyannote 3.1 for diarization + wav2vec2 forced alignment. Single endpoint
/v1/audio/transcribe-with-speakers returns the exact same shape spark-
control's existing endpoint does, so the recap-relay PR spec needs no
changes when we cut over.
* image/app/whisperx_install.py - install manager. ships build context to
Spark 2 over SSH, runs `docker build`, runs `docker run` with 40 GB
memory cap (vs Sortformer's unbounded which thrashed Spark 2 on a 90-min
file), polls /health until both Whisper + pyannote report loaded.
* Audio proxy: /api/audio/transcribe-with-speakers now prefers WhisperX
when its /health reports diarizer_loaded=true, falls back to the legacy
Parakeet + Sortformer path otherwise. Same response shape either way.
Clean cutover, easy rollback (`docker rm whisperx-asr`).
* Dashboard (Audio / Speech tab):
- "Add WhisperX" banner appears when not installed, with a primary
"Install WhisperX" button. One click triggers the install.
- Build progress dialog with phase + elapsed timer + live build log via
SSE (`/api/whisperx/install/{job_id}/stream`).
- After install, WhisperX auto-registers as a managed service alongside
Parakeet and Magpie (Start/Restart/Stop, deep-check, auto-restart).
- Banner self-hides once /api/whisperx/status reports healthy.
New endpoints
─────────────
GET /api/whisperx/status
POST /api/whisperx/install
GET /api/whisperx/install/{job_id}
GET /api/whisperx/install/{job_id}/stream (SSE phase + log)
Config additions (env)
──────────────────────
WHISPERX_HOST (defaults to spark2_host)
WHISPERX_USER (defaults to spark2_user)
WHISPERX_CONTAINER (default: whisperx-asr)
WHISPERX_PORT (default: 8002)
WHISPERX_MODEL (default: medium; tiny/base/small/medium/large-v3)
Dockerfile
──────────
Added COPY whisperx_container /app/whisperx_container so the runtime
install manager can read the build context from inside the spark-control
image and ship it over SSH.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three UX improvements, all client-side; no backend or behavior changes.
1. LLM / Audio tabs under the hardware section. The single long column got
split into two tabbed views:
* LLM -> model swap + download panel + spark-vllm-docker updates
* Audio -> Parakeet/Magpie services + speech-model patches
Selection persists in localStorage; default is LLM. The swap-panel
(in-flight LLM swap) sits ABOVE the tab strip so it stays visible
regardless of which tab is active.
2. Collapsible OpenAI-compatible Endpoint card. New chevron in the card
header collapses everything except the title. State persists per browser
via localStorage. Defaults to collapsed since you rarely need the URL/
model details visible (and the same info is one tab swap away).
3. Unified pill sizing. The .sm-pill class in speech-models was rendering
subtly larger than .tag pills on model cards. Dropped .sm-pill entirely
and reused .tag with semantic color modifiers (.tag.ok / .tag.warn /
.tag.bad). Same 11px / 2px×8px footprint everywhere now. Also added
explicit line-height: 1.5 + display: inline-block to .tag to lock down
vertical sizing.
No new endpoints, no new dependencies. Tested locally with node --check
and ast.parse(). Verified the tab DOM structure wraps the right sections
and the speech-models panel still self-shows/hides on data load.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Folds the image/parakeet_patches/apply.sh script into a one-click
dashboard action and adds drift detection so you can see at a glance
whether the parakeet-asr container has the latest Sortformer overlays
that spark-control ships.
Backend:
* image/app/speech_models.py - SpeechModelsManager: reads /health from
Parakeet, sha256s the local overlay files inside spark-control's
Docker image (/app/parakeet_patches), sha256s the same files inside
the parakeet-asr container via `docker exec ... sha256sum`, surfaces
in_sync / drift / missing status per file.
* GET /api/speech-models - status payload
* POST /api/speech-models/reapply - copies overlays into container,
verifies python syntax, restarts,
polls /health for ~120s, returns
step-by-step result
* POST /api/speech-models/restart - plain `docker restart parakeet-asr`
Dockerfile: now COPY parakeet_patches into the image at /app/parakeet_patches
so the runtime can read them. Future spark-control releases auto-carry
newer overlay versions; the panel surfaces drift after upgrade.
Frontend: new "Speech model patches" section on the dashboard with
* Status pill (in sync / drift / missing)
* Per-file SHA comparison (local vs container)
* Loaded-models pills (ASR + diarizer)
* Reapply + Restart buttons (both with confirmation modals)
* Live progress display during reapply with per-step ✓/✗
Verified post-install against the running cluster:
GET /api/speech-models shows both files in_sync (SHAs match) and both
models loaded ready on Spark 2.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
When a model's weights aren't on disk, the green "Switch to this"
button on the card is replaced by a blue "Download" button that
calls /api/download directly with the model's repo and the right
mode (solo -> spark1, cluster -> both). One-click re-install of a
previously-deleted model, no more pasting the repo into the manual
download form.
Also adds a confirmation dialog showing the model name, size, and
target Spark(s) before kicking off the download — and disables the
button when another download is already in flight.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Each model card now shows whether its weights are present on disk
(with GB size) or not yet downloaded. When present and the model
isn't currently loaded, a trash icon appears; clicking it pops a
confirmation showing exactly how many GB will be freed and on
which Spark(s), then runs rm -rf on the HF cache directory via SSH.
Cluster-mode models are removed from both Sparks; solo-mode from
Spark 1 only. Safety rails: refuses to delete the currently-loaded
model, refuses during an in-flight swap or download, and the
catalog entry stays intact so it can be re-downloaded anytime.
Backend:
- new image/app/disk.py: probe_disk + delete_from_disk over SSH
- GET /api/models/disk-status — parallel probe across all catalog models
- DELETE /api/models/{key}/disk — guarded rm -rf, logs to connectivity events
Frontend:
- on-disk / not-downloaded pills on every card
- trash icon-btn in card-actions row (hidden when not on disk)
- confirmation dialog showing per-host bytes-to-free
- disk-status re-checked every 60s
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
deep_health.py:
- Synthetic probes per service, all payloads generated in-memory (BytesIO), never written to disk:
- Parakeet: 1s of digital silence via in-memory WAV → POST /v1/audio/transcriptions
- Magpie: short 'hi' text → POST /v1/audio/synthesize (multipart form-data, real TTS API endpoint discovered via openapi.json)
- vLLM: 1-token completion against currently-loaded model
- Background loop runs every 5 minutes (configurable). Best-effort: exceptions in the loop never kill it.
- Auto-restart on wedge-pattern errors (cudaErrorUnknown / CUFFT_INTERNAL_ERROR / 500 / Engine core init failed): docker restart of the affected container.
- Rate-limited: max 3 restarts per service per 30 min.
- Cooldown: 120 s between consecutive restarts on the same service.
- 60 s startup grace before any auto-restart can fire after the app boots.
- Probe failures + recoveries logged via record_report(source='deep-health') into the connectivity history alongside the polling-based transitions.
API:
- GET /api/deep-health: per-service last result + auto-restart counters
- POST /api/deep-health/{service}/run: manual trigger now
UI:
- Service cards show 'Deep check ok/FAILED <time> <latency>' inline, plus a ↻ button to run-now
- Auto-restart count in 30-min window surfaced on the card when > 0
- Inline error excerpt shown for failed probes
Bug fix: server.py app startup hook was placed before the FastAPI app object was constructed (would crash on import). Moved after.
validate.py:
- Builds the same args list a real swap would pass to 'vllm serve'
- SSHes into Spark 1 and runs vLLM's own argparse layer inside the running vllm_node container, WITHOUT initializing the engine
- Uses FlexibleArgumentParser (from vllm.utils.argparse_utils, with fallback to engine.arg_utils) + make_arg_parser — the exact same parser the 'vllm serve' CLI uses. Earlier attempt with bare argparse.ArgumentParser was too strict (rejected '--moe_backend' with underscore that the real CLI accepts via FlexibleArgumentParser's normalization)
- Returns structured {ok, stage, error, cmd_args, launch_cmd} so the UI can surface the exact failure cause
Endpoint: POST /api/swap/{key}/validate. Cheap (~5s), no engine init, no disruption to the currently-loaded model.
Frontend: 'Test' button on every model card, inline result below the action row (green check or red detailed error). Result stays visible until the user reloads or clicks Test again.
Catches: typos in flag names, deprecated/removed flags after a vLLM upgrade, type mismatches. Does NOT catch runtime-only failures (Mamba block-size assertion, OOM at load, kernel-compat). Ok=true is necessary-but-not-sufficient; ok=false is definitive 'don't bother running it'.
connectivity.py:
- Generalized 'spark' subject to any string; renamed 'spark' field to 'subject'
- Legacy v0.5 events with the old 'spark' field are migrated transparently on read (kind defaults to 'transition')
- New record_report(subject, ok, source, detail, latency_ms): always appends an event with kind='report'; does NOT mutate the current state (only active polling is authoritative)
- summary() returns events normalized to the new schema
Wiring:
- /api/status now calls record_state for vllm/parakeet/magpie (dedup on no-change)
- /api/services calls record_state for each service after its http check
- Result: dashboard observes service-level transitions automatically with no extra polling
Passive endpoint:
- POST /api/health-event with {service, ok, source?, error?, ms?}
- Useful for external apps (e.g. Open WebUI) to surface sub-poll-interval failures the dashboard would otherwise miss
UI:
- Connectivity dialog groups events by subject (hosts ordered first, then services)
- Per-subject summary shows transition count, down count, report count, failed-report count
- Transitions and reports render inline with distinct styling; reports show source app + error + latency
- Legacy v0.5 events render unchanged
Docs:
- README documents /api/health-event with a curl example
Package: bump to 0.6.0:0
wol.py:
- build_magic_packet(): standard 6x0xFF + 16x MAC layout
- send_local_broadcast(): direct from container (ports 9 + 7 for safety)
- send_via_peer(): preferred path; SSHes to the OTHER Spark and runs a Python one-liner there so the packet originates on the target's LAN segment (most reliable)
- MAC validation + normalization
connectivity.py:
- /data/connectivity.json persistence (thread-safe, atomic rename)
- Stores per-Spark current state + last_change timestamp + rolling 200-event log
- Records up/down transitions; computes down_seconds / up_seconds durations
- MAC cache populated lazily during hardware probes
hardware.py:
- Probe now reads MAC via /sys/class/net/<default-route-iface>/address
- After each probe, record_state() emits a transition event if state changed
- record_mac() caches the address so WoL works when the Spark next goes down
Endpoints:
- GET /api/connectivity: macs, current state, last_change, events[]
- POST /api/spark/{name}/wake: tries via-peer first, falls back to direct broadcast
UI:
- Unreachable hardware card shows the cached MAC + 'Wake (WoL)' button (only if MAC known)
- New 'Connectivity log' button opens a modal with per-Spark transition history (last 25 each), including duration of each prior up/down period
- pollHardware also pulls /api/connectivity so WoL buttons appear without an extra fetch
Package: bump 0.5.0:0; main.ts sets CONNECTIVITY_LOG=/data/connectivity.json
Hotfix (was v0.3.1):
- services.py: cache 'unreachable' per (host,user) for 25s so a dead Spark doesn't hang every /api/services call behind 6s ssh timeout
- ssh_run timeout reduced 10 -> 6s for docker_state probes
- hardware probe: shorter SSH timeout (6s), longer cache TTL for failures (25s)
- JS pollStatus retries loadModels() if state.models is empty (recovers from cold-start proxy timeout)
- Unreachable hardware card now includes troubleshooting steps (Spark Control cannot SSH into an unreachable Spark to restart it)
v0.4 NIM installer:
- nim.py module: curated SUGGESTED_NIMS list (Parakeet, Magpie, Riva) + NimManager that runs docker login nvcr.io + docker pull + docker run -d --gpus all -p PORT:PORT -v VOL:/opt/nim/.cache -e NGC_API_KEY -e ... --restart=unless-stopped + chown the volume to uid 1000 + restart. Streams all output via SSE; redacts the API key from log lines.
- custom_services.py: persists installed NIMs to /data/services-overrides.yaml so they appear in the services panel after install
- services.py: merges custom services into the panel
- /api/nim/catalog GET, /api/nim/install POST + GET/SSE
- /api/services/{name} DELETE for custom services
- UI: '+ Install NIM' button next to 'Always-on services'; modal lists curated images each with a 'Pick' button + a custom-image form; installation runs in a second dialog with phase + elapsed timer + collapsible log
- NGC API key field added to Configure Sparks (masked); injected as NGC_API_KEY env var into the container
Package: bump 0.4.0:0; main.ts adds SERVICES_OVERRIDES + NGC_API_KEY env vars
Hardware dashboard:
- New hardware.py module: SSH probes each Spark for hostname, uptime, load+cores, RAM, disk, GPU (name, util, temp, power) + per-process GPU memory sum
- DGX Spark uses unified memory (nvidia-smi memory.total returns N/A); fall back to per-process compute memory and compute fraction against system RAM. Marks with gpu_unified_memory=true.
- 4s TTL cache in HardwareProbe to avoid hammering
- /api/hardware returns per-Spark snapshot
- UI: 'Spark hardware' section at the top with per-Spark cards (CPU load, RAM, GPU mem (unified), GPU util + temp + power, disk) — bars with warn threshold styling
- Polls every 8s
Knob context (tied to live hardware):
- Each Advanced knob now shows plain-English help text
- 'GPU memory %' shows '~N GB allocated · ~M GB left for OS/buffers' computed from actual Spark RAM
- 'Max context' shows '~N pages of text'
- Toggles show tradeoff descriptions
Explain context:
- '✨ Explain context' button on the update banner
- /api/explain-updates POST: forwards pending commits to the loaded vLLM model and streams its response back as SSE
- Renders into an expandable 'Explained by the loaded LLM' section under Pending commits
- Reasoning tokens shown italicized when the model emits them
Open WebUI integration:
- New 'Open WebUI URL' optional field in Configure Sparks
- /api/config exposes it; UI shows 'Open chat ↗' button in the top bar if set
Downloads:
- Third radio option: Spark 1 only / Spark 2 only / Both Sparks
- Backend picks SSH target based on mode
- HF repo link icon next to the input
- Helper line about NVFP4 for Blackwell
Model cards:
- Repo name is now a clickable link to its Hugging Face page
Package: bump 0.3.0:0
Bug fix:
- config.py: empty PARAKEET_CONTAINER / MAGPIE_CONTAINER env vars (from migrating to v0.2.0+ where the field is optional and saved as '') now fall back to 'parakeet-asr' / 'magpie-tts' via the 'or' idiom. Confirmed live: services classify as 'running' instead of 'unknown'.
UX:
- Replaced text 'Copy' buttons with compact icon buttons (clipboard SVG)
- Endpoint Base URL + Model ID + curl snippet are now click-to-copy themselves (the value AND a separate icon button)
- Service cards: host, base URL, and model are now three separate copyable rows
- Update banner: leading explanatory line — 'Updates to eugr/spark-vllm-docker — the upstream project that orchestrates vLLM on your Sparks. These are not firmware, OS, or model updates.' with a link to the repo.
Backend:
- overrides.py: read/write /data/models-overrides.yaml (knobs + custom entries)
- apply_knobs_to_args(): strip matching flags from bundled vllm_args and append knob values, so knob changes properly override bundled defaults
- extract_knobs_from_args(): seed UI knob values from bundled args so the Advanced dialog has correct starting state
- models.py: load_catalog merges overrides on top of bundled yaml
- GET /api/models returns effective_knobs per model
- PUT /api/models/{key}/knobs persists knob changes
- POST /api/models adds a custom catalog entry
- DELETE /api/models/{key} removes a custom entry (bundled models cannot be deleted)
- swap_manager.reload_catalog() called after each mutation so swaps see latest
Frontend:
- New 'Advanced' button on every card opens a modal dialog: max-model-len input, gpu-memory-utilization slider, three optimization checkboxes (fastsafetensors, prefix caching, FP8 KV cache). Save persists; Cancel discards. Custom models also have a Delete button.
- After a successful download, automatically open the 'Add to catalog' dialog pre-filled with the repo, with the same knob defaults — user just enters key, display name, and clicks Save.
- Custom catalog entries are tagged with a blue 'custom' pill on the card.
Package: bump 0.2.3:0; main.ts sets MODELS_OVERRIDES=/data/models-overrides.yaml so overrides persist on the StartOS volume.
Backend:
- updates.py: get_update_status() runs git fetch + git rev-list --left-right --count HEAD...origin/main to learn ahead/behind/dirty, plus git log for pending commits
- UpdateManager class with asyncio.Lock; one update at a time
- POST /api/updates/apply triggers "git pull --ff-only && ./build-and-copy.sh -c" over SSH with streamed log + phase detection (Pulling / Building the vLLM container / Copying to peer Sparks)
- GET /api/updates returns {ok, behind, ahead, dirty, current, log[], branch}
Frontend:
- Persistent banner near footer: hidden when up-to-date, blue when N commits behind, warn (orange) when local dirty changes block update
- 'Show details' expands a list of pending commits
- 'Apply update' triggers the long-running build with phase + elapsed timer + collapsible logs
- Confirmation dialog explains the 5–40 min duration
Package: bump 0.2.2:0
Dashboard:
- New 'Always-on services' section with cards for Parakeet and Magpie
- Each card: host:port, model loaded, status pill (Healthy/Unhealthy/Starting/Not configured)
- Start, Restart, Stop buttons. Buttons disabled when not applicable for current state
- Restart counter shown when > 1 (would have surfaced the old magpie crash loop)
Backend:
- New /api/services GET: docker container state + http health for each support service
- New POST /api/services/{name}/{action} for start | stop | restart
- services.py module: docker_state, run_action via SSH
- config.py: PARAKEET_HOST/USER/CONTAINER and MAGPIE_* env vars, default to spark2_*
- health.py: use per-service hosts (no longer hard-wired to spark2_host)
Package:
- sparkConfig.yaml.ts: add 6 new optional fields
- configureSparks action: optional 'Parakeet host', 'Parakeet container', 'Magpie host', 'Magpie container' fields; descriptions explain they default to Spark 2 when blank
- Handler normalizes nulls to empty strings before merge
- main.ts: pass new env vars to container
- bump to 0.2.0:0
- configureSparks.ts: generic placeholders (e.g. 192.168.1.10), no Alice-specific IPs; descriptions explain the role of each node instead of naming his hardware
- showPublicKey.ts: reads sparkConfig.yaml; emits a ready-to-paste one-liner (KEY='...' followed by 'ssh user@host "echo $KEY >> authorized_keys"' for each configured Spark). Falls back to generic instructions if Configure Sparks hasn't been run yet.
- /api/status now includes vllm.base_url for the OpenAI endpoint
- New endpoint panel in UI: base URL + model ID rows with copy buttons + collapsible curl example
- Bump version to 0.1.0:1
- models.yaml: add 'description' field for all 5 models (generic, anyone-can-use)
- ModelDef gains optional description: str | None field
- UI: render description below meta tags; mute the repo line further
- escapeHtml() for safety in case descriptions/names contain HTML chars
- Update runbook: how to add a new model with description
Keysat