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Commits
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bda169c900 |
feat(workers): pin work to the chosen GPU, and say when its model is missing
Three phases that only make sense together: a job that names a worker, a worker that reports honestly what it can actually run, and the small defects that made both lie. **Pinning** (Phase 1). `pinned_worker_id` is now honoured in both places that choose a worker — `eligible_workers` and `select_worker` build independent lists, so applying it to one silently leaked work onto whichever machine was least busy. The pin persists across a restart via an additive column, deliberately not alembic (justified in the code, per the precedent already in db.py): quitting mid-render used to drop it without a word. `max_attempts=1` was rejected as the mechanism — it makes the FIRST failure terminal, including the penalty-free ones a stale advisory view produces routinely. Cancel now actually reaches the worker. `WorkerServicer.cancel` had zero callers, so cancelling released the slot while the GPU thread kept running, and a late result could resurrect the task as COMPLETED — `commit_result` assigned that state directly, bypassing the transition table where CANCELLED is terminal by construction. **Honest capabilities** (Phase 4). A worker now probes whether weights are actually present, and a job stops BEFORE dispatch with a typed 409 naming the model and the machine, instead of failing mid-task. The probe fails OPEN: `is_cached`/`cache_is_complete` cannot see a user-managed clone outside the HF layout, so only a positive "absent" refuses. Refusing an engine that works today would break the compatibility promise. `pool.supports` deliberately still ignores `downloaded` — had it not, the scheduler would drop the worker and answer with a terminal NO_CAPABLE_WORKER, which tells the user to check their install when the truth is one download away. The frontend no longer offers "Report this bug" for that state; it offers the download. Catalog tags resolve against the TARGET's OS/arch/backend, not this machine's. From a Mac control plane, a CUDA worker's model list was showing the mlx-community repos it cannot run and hiding the ones it needs. **And the quiet ones** (Phase 0 leftovers): a model's human label rides its own proto field so renaming it cannot orphan breaker history; an empty model_id no longer forks the capacity slot key into two slots for one model; the idle sweep cannot evict an engine out from under a live LOCAL render. Verified on real hardware, which is the only verification that has ever caught anything here: 2025 characters, default settings, routed to an RTX 4090 over the wire — 100% GPU utilisation on the remote box, 119.6 s of 24 kHz audio returned in 16.6 s, 5.7 MB delivered out of band through the artifact path rather than the control stream. Backend 5259 passed, frontend 1808 passed. |
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c643706d07 |
feat(workers): make a remote GPU actually run a task, end to end
Selecting a remote worker repainted a badge and nothing else. The cause was not subtle: `scheduler.submit` had no production caller, and `routing.decide()` was read only by the status endpoint that paints the header. Remote execution was a complete, tested pipeline with no producer at its head. This adds the producer and fixes the defects that made the pipeline unable to carry a real job: - Nothing routed to the scheduler. Adds `POST /workers/tasks` (loopback-gated, **development-only** until the gateway lands) and `Scheduler.wait`, backed by per-task futures rather than the unregisterable `on_change` listener list. - Every task over two minutes died. No worker ever sent `TaskProgress`, so the 120s progress lease expired mid-render — including during the cold model load, which happens after `TaskStarted`. Workers now report progress and emit a keepalive, bounded by the phase's absolute budget so it renews the lease without deleting the only enforced bound in the system. - The executor rebuilt its engine per task (`return cls()`), so every job paid a cold load. Engines now share one instance cache with the router, resolved by the assignment's engine — never `get_active_tts_backend()`, which returns the worker machine's own Settings preference and would silently run the wrong engine. - One lease expiry took a worker offline permanently: parked slots were never reclaimed. Parks now expire on a TTL, and are deliberately NOT reconciled against the worker's own load report — at a ceiling of one the only task such a worker can report is the wedged one, so "busy" would drop the park and the next idle heartbeat would hand out a slot with a live GPU thread (#730/#1190). - A worker that dropped and reconnected mid-render had every liveness frame discarded: task frames were fenced on the live session epoch, which bumps on every reconnect, while the worker echoes the ref stamped at dispatch. The control plane then expired a task whose GPU was still rendering, and swallowed the failure report when it went wrong. Fenced per attempt instead. - A result from one worker could commit another's task, after which the owner's real delivery arrived as a duplicate and its audio was discarded. "Unknown attempt" and "another worker's attempt" are no longer the same answer. - An oversized result was a poison pill, re-sent identically on every reconnect and permanently disconnecting the worker. It is now a terminal `RESULT_TOO_LARGE`, which is also classified — it was falling through to TRANSIENT and retrying a re-render that could never fit. - `_store_inline` joined the artifact directory with worker-supplied ids, and `os.path.join` discards its prefix on an absolute component. Paths are now minted control-plane-side and resolved through `core.path_security`. - Remote synthesis bypassed `mark_synthetic`, and the guard that exists to catch exactly that walked only `backend/api` and `backend/services` — so it stayed green while a fourth unmarked producer shipped. Marking moved to the worker's tensor stage; the guard now walks `backend/worker` too. Also adds pre-rendered voice previews (`services/gallery.py`), so browsing the gallery no longer needs a GPU or a downloaded model. The manifest is verified against the updater's release key already baked into the binary; a fresh install hears voices without downloading 2.4GB first, and everything falls back to local rendering when the gallery is unreachable. Verified on hardware, not just in CI: 1728 characters submitted to an RTX 4090 returned 105.94s of 24kHz audio in 23.9s, committed and served from the artifact store. Not yet done, and deliberately not claimed: the keepalive fix cannot be exercised end-to-end on fast hardware, because any job long enough to reach the 120s lease produces audio past the 8MiB inline cap. Chunked `UploadResult` has to land first. Pinning to the worker the user chose is also still absent, so "Remote" reaches a remote GPU but not necessarily the one on the badge. |
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9eb1ec7591 |
feat(workers): choose where jobs run, and show whether that machine is well
Adds a GPU target picker to the header: Local, or one of the machines you enrolled. Exactly one is active at a time; other connected workers are standby and receive nothing. The selection is the user's, not the scheduler's. The engine underneath can rank many workers and the hosted platform will need that, but a desktop app is better served by a choice you can predict and explain: "your worker is offline, this ran locally" is a sentence, "least-busy ranking preferred the laptop" is not. Picking an offline machine is allowed on purpose — you choose your desktop, then go and switch it on. `routing.decide()` is the single answer to "where does the next job run", shared by the badge and (soon) the generation path, so the badge cannot claim something the router will not do. It shows the RESOLVED answer rather than the stored choice: pick your desktop, let it sleep, and the chip reads Local with the reason, while the menu still shows your desktop selected. Connection latency is now real. `latency_ms` existed but nothing measured it — the protocol had Ping with no reply — so it was always zero. Adds Pong (additive, field 12) and times the round trip on the control plane's MONOTONIC clock, so an NTP step or a sleep/wake cannot produce a nonsense reading, and no worker timestamp is trusted. Reported as a median of five samples and withheld until a second sample exists: the first round trip after connect lands while the worker is still importing torch, which measured 139 ms on loopback and, averaged, carried that for a minute. This is CONNECTION latency, not time-to-result. It is shown as information, never as a routing input — RTT is milliseconds where inference is seconds, so ranking on it would optimise noise. Also fixes a bug the picker exposed: worker config was read from the pool, which caches the row handed to it at connect time. Renaming a CONNECTED worker updated the database and the API kept serving the old name until it reconnected — same for priority and enable/disable. Config now comes from the database and liveness from the pool, never the reverse, and writers refresh the live copy so the scheduler's logs do not use a stale name. Adds worker rename (the backend already supported it; no UI called it), worker address as seen by the control plane rather than self-reported, and ready/busy/offline status behind the header dot. |
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43de1c794c |
feat(workers): remote GPU workers over a versioned gRPC protocol
Send individual jobs to GPUs on your other machines while everything else stays local. Opt-in, off by default: with the toggle off there is no listening socket, no certificate and no background loop. Design follows remote/goal_v2.md, the council-revised goal doc. The decisions that shaped the code, and why: * A disconnect is an unknown outcome, not a failure. The original design reassigned on disconnect while also describing the case where the worker had already finished — following both guarantees duplicate execution. An attempt now holds a grace window; a worker returning inside it commits its result and no second attempt is ever made. * At-least-once execution, exactly-once result commit. The result is persisted BEFORE it is acknowledged, so a crash between the two cannot silently lose a finished render. * Deadlines are phased (accept -> model load -> execute -> deliver) and liveness is a progress lease. The old fixed 30s execution budget was two orders of magnitude below what this product actually does; silence is the failure signal, not slowness. * Capacity is derived from free VRAM, never configured: a static value corrupts output under torch.compile thread affinity (#315) and aborts the process on small cards (#567). * A circuit breaker replaces the reliability-score/quarantine machinery, which had no recovery path (no probation workload exists in a TTS product) and penalised consumer networks for existing. * Identity is a keypair the worker generates and never sends. A server-assigned id is a name, not an authenticator, so revocation of one would be theatre. Enrollment tokens are single-use and carry the control plane's certificate fingerprint for pin-on-first-use. Adds the domain core, scheduler, durable task store, gRPC transport, worker agent, management API, Settings panel, and docs. Protobuf reserves the tenant/trace/usage fields a hosted control plane would need, since adding them later means upgrading a whole fleet. Includes tests for the failure paths that matter: duplicate delivery, stale-session fencing, reconnect reconciliation, grace expiry, breaker attribution, and a real end-to-end TLS round trip. |