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feat/audio-cpp-backend-v2
84 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
mudler's LocalAI [bot]
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878a0d00a1 |
fix(distributed): reaper reaps live backends, ghost model stubs, in_flight leak, sidecar staging runaway (#11142)
* fix(distributed): stop the probe reaper from orphaning busy backends
The reconciler's liveness probe is a 1s gRPC HealthCheck, and a single
failed probe deleted the model's node_models row. A backend that is
merely busy cannot answer it: single-threaded Python backends (video and
avatar generation) block for minutes inside one request, so the reaper
was deleting registry rows for backends that were alive and mid-request.
The model then vanished from the nodes page while it was still
generating, and because the row was gone the in-flight decrement had
nothing to decrement ("DecrementInFlight: no matching row or already
zero"). Every subsequent request re-routed and re-staged the full model
from scratch.
Two guards:
- Replicas with in-flight requests are excluded in SQL. A row that is
actively serving is proof of life, and the running request is
exactly what stops the backend from answering the probe.
- Idle replicas must miss three CONSECUTIVE probes before removal, so
a transient blip cannot orphan a live replica. A successful probe
resets the streak.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-5 [Claude Code]
* fix(distributed): drop the local model stub when its last replica goes
In distributed mode every routed model leaves an in-process stub in the
frontend's ModelLoader, and DistributedModelStore.Range reports local
stubs UNION the registry rows. Every registry removal path deletes only
the DB row, so the stub outlived the replica and the model was reported
as loaded forever.
That is the "loaded on the home page, absent from every node" ghost:
/system reads the union and still sees the stub, while /api/nodes/models
reads the registry and correctly sees nothing. It never self-healed,
and both frontend replicas showed it independently.
The replica-removed chokepoint could not fix this as it stood, because
it held a SINGLE hook that the prefix cache already owned, and it was
registered only when the prefix cache was enabled. Registering a second
listener would have silently displaced the first.
- Turn replicaRemovedHook into a list (AddReplicaRemovedHook), so
independent subsystems can each register without displacing others.
- Add NewLocalStubInvalidator, which drops the local stub once no
healthy replica of the model remains anywhere in the cluster, and
wire it unconditionally in startup.
The stub is kept while another node still serves the model: the
frontend is right to consider it loaded, and each request re-routes
through SmartRouter to pick a live replica anyway.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-5 [Claude Code]
* fix(distributed): stop staging checksum sidecars back to workers
The file transfer server writes a "<file>.sha256" sidecar next to every
file it accepts. The sender walked the model directory with no filter,
so it staged those sidecars too, and the receiver duly wrote a sidecar
for each sidecar. Every staging pass multiplied the tree:
config.json -> config.json.sha256 -> config.json.sha256.sha256 -> ...
One LongCat snapshot had grown to 498 files, 466 of them chained, up to
29 levels deep, and the staged file count climbed on every pass. This
inflates each transfer and grows disk without bound on both ends.
Skip hash sidecars in stageDirectory, and mirror the skip in
countStageableFiles so the progress bar still reaches 100%. The check is
"a sidecar sitting next to a real file" rather than a blanket suffix
ban, so a model that genuinely ships a .sha256 payload with no
corresponding base file is still transferred.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-5 [Claude Code]
* fix(distributed): classify the liveness probe instead of gating on in_flight
The previous commit excluded replicas with in-flight requests from the probe
reaper. That was the wrong guard, and could invert the bug it fixed.
in_flight has no decrement guarantee: track() balances its increment with a
defer, but a frontend killed mid-request never runs it, and the load-time
reservation is released only when the first inference completes. Nothing
resets a leaked counter. Gating the reaper on it therefore meant a leaked
counter would shield a genuinely dead replica from ever being reaped.
Nor was patience alone a fix: three misses at the default interval is ~90s of
silence, while the generation that triggered this blocks for 15+ minutes.
The real conflation was in the probe itself. A gRPC HealthCheck against the
backend's serving port measures "is it idle enough to answer", not "does the
process exist", and probeLoadedModels discarded the error that tells them
apart. Because the gRPC client is lazy, the status code is decisive:
- DeadlineExceeded: transport fine, nothing serviced the RPC. Busy.
- Unavailable: nothing is listening. Gone.
ModelProber now returns a ProbeOutcome, and only ProbeUnreachable counts
toward the reap threshold. ProbeBusy clears the streak: it is evidence of
life. A blackholed network reads as busy too, deliberately, since whole-node
failure is the health monitor's job.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-5 [Claude Code]
* feat(distributed): reconcile replicas against worker-reported processes
Probing a backend's own serving port cannot distinguish "busy" from "gone"
without inferring it from an error code. The worker can answer directly: it
spawned the process, holds the handle, and its reply is not blocked by
whatever that backend is doing.
Adds a models.running request-reply subject. The worker answers out of its
in-memory process table, reporting each live process as (modelID,
replicaIndex, address) — the supervisor's process keys are `modelID#replica`,
which is isomorphic to a NodeModel row, so the reconciler can diff the two
directly.
reconcileNodeProcesses runs before the port probe and reaps rows for models
the worker is not running. Models the worker vouches for get updated_at
bumped, which takes them out of the port prober's stale set entirely: that is
what keeps a backend deep in a long generation away from the probe in the
first place, rather than relying on classifying its silence after the fact.
A worker that does not answer is skipped, not assumed empty. A messaging
failure says nothing about the processes, and assuming the worst would delete
a node's rows on a transient NATS blip; the port probe stays as the fallback
for those nodes. Rows younger than probeStaleAfter are ignored so a freshly
created row is never judged against a process table that has not caught up.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-5 [Claude Code]
* fix(distributed): stop in_flight leaking and pin replicas against eviction
A leaked in_flight counter is not cosmetic. FindLRUModel,
FindGlobalLRUModelWithZeroInFlight and the router's eviction query all require
in_flight = 0, so a replica whose counter never came back is pinned and its
VRAM is unreclaimable for the lifetime of the process.
Two halves.
The source: routing reserves in_flight = 1 at load time so a freshly loaded
replica is not evicted out from under the request that caused the load. That
reservation was released ONLY by the first inference completing, so a route
torn down before any inference ran (client disconnect, handler error, failure
between load and the backend call) stranded it. newRouteResult now wires the
reservation to a sync.Once fired by whichever comes first, the first inference
or route teardown, and replaces three copies of the old wiring.
The backstop: a sweeper for counters leaked by paths that cannot run a defer
at all, such as a frontend killed mid-request.
Identifying a leak by elapsed time alone is unsafe. IncrementInFlight stamps
last_used at request START and nothing moves it while the request runs, so a
long generation is indistinguishable from a leak by age, and resetting there
would expose a serving model to eviction. The probe supplies the missing bit:
a backend that answers a health check promptly is not inside a request,
because that is precisely what a busy one cannot do. Requiring the row to also
be idle for 30 minutes covers backends that serve in parallel and can answer
while working, since those keep last_used fresh through each new increment.
Two existing tests asserted the old behaviour ("No decrement on Release").
That assertion was the leak, so both now pin the release instead.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-5 [Claude Code]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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mudler's LocalAI [bot]
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5d57c08c6e |
feat(distributed): cache staged-artifact hashes and publish the model load lifecycle (#11121)
feat(distributed): cache staged-artifact hashes and publish load lifecycle Every load request re-hashed every staged artifact on the controller (probeExisting and the upload path both re-read the full file), which for a large multi-file model on NAS-backed storage is minutes of pure re-reading per request even when nothing changed - observed as ~9 minutes of "Upload skipped (file already exists with matching hash)" before every avatar generation. Cache the local hash in the same .sha256 sidecar the worker-side transfer server already maintains, invalidated whenever the sidecar is older than the file. The whole staging+loading phase was also invisible: the NodeModel row was only written after LoadModel succeeded, so /api/nodes and the UI showed nothing while a cold load spent 10+ minutes staging - indistinguishable from nothing happening. Publish the lifecycle instead: "staging" as soon as the node is chosen, "loading" when the checkpoint load starts, and the existing "loaded" on success, with the row removed on any failure so a dead load does not leave a phantom replica. The early row also reserves the replica slot against concurrent schedulers. The nodes view already renders non-loaded states on model chips; style "staging" like "loading". Audited every state-filtered registry/router query: eviction, routing, reconciler and idle-model queries all filter state='loaded' explicitly, so the new transitional rows are visible to observability surfaces but inert to scheduling decisions (except slot occupancy, intentionally). Co-authored-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Claude Fable 5 <noreply@anthropic.com> |
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mudler's LocalAI [bot]
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6584db992f |
fix(nodes): never schedule a model onto a node that cannot store it (#11054)
* fix(nodes): never schedule a model onto a node that cannot store it
A worker whose models filesystem was 100% full kept advertising
`status: healthy`, stayed a scheduling candidate, was picked to host a
70 GB video model, accepted the staging request, transferred ~17 GB and
only then failed:
staging .../whisper-large-v3/model.fp32-00001-of-00002.safetensors:
upload to node b7bacbf4-... failed with status 500:
writing file: /models/longcat-video-avatar-1.5/...: no space left on device
The node was at 937G/937G/0-avail. Total elapsed before the truth
surfaced: 16 minutes, for a decision that could never have succeeded.
The worker health signal only ever proved liveness. `/readyz`
(WorkerReadiness/NATSReadiness) checks the NATS link; `status: healthy`
in the registry is driven by heartbeat recency. Node capacity carried
VRAM and RAM but no disk figure at all, and the router compared model
size against VRAM only — nothing anywhere looked at free space on the
filesystem that staging actually writes to.
Report it, then use it:
- Workers now measure the filesystem backing their MODELS directory
(not `/` -- staged weights land in the models path, and that mount is
very often separate) and report `total_disk`/`available_disk` on
registration and on every heartbeat. Free disk moves faster than VRAM
under staging traffic, so the per-heartbeat refresh matters.
- The SmartRouter drops nodes that cannot store the model before it
picks one. The requirement comes from `modelPayloadBytes` -- the same
local paths `stageModelFiles` uploads, already computed for the
size-derived load budget -- plus a 5% / 1 GiB margin, rather than a
fixed percentage of the node's disk. A percentage threshold would take
a small-but-usable node out of rotation for models it could hold, and
on a homogeneous cluster would strand every node at once.
- When no node fits, scheduling fails immediately with an error naming
the requirement and each node's free space, instead of picking one and
discovering it mid-transfer.
Two deliberate non-changes. Low disk does not mark a node `unhealthy`:
the check is per model, so a node too small for one model stays a valid
target for smaller ones. And `total_disk == 0` means "does not report
disk" (pre-upgrade worker, or a failed stat), not "full" -- such nodes
pass through untouched so a rolling upgrade never empties the candidate
pool. A genuinely full node is distinguishable: non-zero total, zero
available. Registry read failures are logged and scheduling continues
unfiltered; a database hiccup must not wedge a cluster.
Free space is surfaced on the node detail page next to VRAM, since the
incident's signature was a node that looked entirely healthy.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude Code:claude-opus-4-8[1m] [Read] [Edit] [Bash]
* feat(nodes): make the disk-headroom check operator-controllable
The admission check added in the previous commit had no off switch. A
scheduler-side veto with no escape hatch is a liability: our size
estimate can be wrong (deduplicating or compressing filesystems, a
backend that fetches its own weights rather than loading the staged
copy), and an operator who hits that has no way out but a downgrade.
Add one knob with two surfaces that share a single source of truth:
- `--distributed-disk-headroom-check` / `LOCALAI_DISTRIBUTED_DISK_HEADROOM_CHECK`
(default true), following the `--distributed-prefix-cache` pattern for
a default-on distributed feature.
- `distributed_disk_headroom_check` in the runtime-settings registry, so
it can be flipped without a restart from `POST /api/settings` and from
Settings -> Distributed in the WebUI.
Both write `DistributedConfig.DiskHeadroomDisabled`, and the SmartRouter
reads that member LIVE on every scheduling decision through a closure
over the application config rather than a value snapshotted at
construction. Env/CLI sets the boot value, the runtime setting overrides
it live, last write wins, and there is exactly one member to read.
Snapshotting would have made the runtime toggle a no-op until restart.
Disabled means WARN, not SKIP. Selection goes back to ignoring free disk
-- byte for byte the pre-check behaviour -- but the check still runs, and
when it would have rejected every node it says so, naming the knob that
suppressed it. Going quiet when switched off would reproduce the exact
condition that made the original incident expensive: a cluster doing
something that could not work and saying nothing. Disabling is also
logged once at startup. Warning only on the total-rejection case keeps
it actionable rather than chatty on a heterogeneous cluster.
Also fixes a false positive in the check itself: shared-models mode
(LOCALAI_DISTRIBUTED_SHARED_MODELS) stages nothing at all -- every node
already mounts this models directory at this path -- so demanding the
full checkpoint size of free space per node would have rejected a
cluster that needs no new bytes. The check is skipped there entirely.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude Code:claude-opus-4-8[1m] [Read] [Edit] [Bash]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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mudler's LocalAI [bot]
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01fca9c9b2 |
fix(distributed): scale the remote model-load deadline with checkpoint size (#11030)
The gRPC deadline for the remote LoadModel call was a fixed 5m. It starts
only after the backend install and file staging have completed, so it
covers the worker's checkpoint read and pipeline init alone - work whose
duration is proportional to the bytes on disk. A fixed value is therefore
a model-size cliff, not a timeout.
Measured in production: a 70 GB video checkpoint (longcat-video-avatar-1.5)
on an NVIDIA Jetson Thor worker failed reproducibly with
"rpc error: code = DeadlineExceeded" after 953.5s of wall clock. Backend
install plus staging consumed ~11m, then LoadModel got its 5m and expired.
The load never had a chance, and the operator saw only a generic
DeadlineExceeded with no hint that a config value was the cause.
Raising the constant does not fix this. It moves the cliff to the next
larger model - the cluster has to support 600 GB checkpoints - and it makes
a genuinely wedged SMALL model hang for the whole inflated duration before
anyone notices, which is a real regression in failure latency.
So derive the budget from the checkpoint size instead:
budget = 5m + 20s/GiB, capped at 6h
2 GiB -> 5m40s, 70 GiB -> 28m20s, 600 GiB -> 3h25m. The per-GiB rate is
deliberately pessimistic (~54 MB/s of weight read) because the errors are
not symmetric: too long costs only failure latency on a load that was going
to fail anyway, too short is a guaranteed false failure on a healthy load.
The size is measured from the frontend's local model files, over the same
path set stageModelFiles uploads. When those files are not present locally -
a backend handed a bare HuggingFace repo id fetches its own weights on the
worker - there is nothing to measure and the budget stays at today's 5m.
An explicit LOCALAI_NATS_MODEL_LOAD_TIMEOUT still wins outright, in both
directions: a shorter override is honoured, so an operator who wants fast
failure is not silently extended by the heuristic.
The cold-load hold needed widening to match. It extends on staging progress,
but LoadModel reports none, so once the last byte lands the hold expires a
stall window later and would cancel a load still well inside its own budget.
scheduleAndLoad now extends the hold by the load budget plus the staging
margin as it enters the load phase; ModelLoadCeilingFor stays the hold's
starting budget rather than its maximum.
Finally, a deadline that does expire now names the budget, the checkpoint
size it was derived from, and the knob that overrides it, instead of
surfacing a bare "context deadline exceeded".
Assisted-by: Claude Code:claude-opus-4-8[1m] [Read] [Edit] [Bash]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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mudler's LocalAI [bot]
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a4a181d2f7 |
fix(distributed): count staging verification as progress, not as a stall (#11026)
Testing the progress-based cold-load deadline on the live cluster surfaced a false positive. The stall window observed UPLOAD bytes only, but the staging path has a phase that does real work while moving zero upload bytes: the resumable-upload verify phase. When a shard is already present on the worker from an earlier attempt, the frontend HEADs it, hashes the local copy to confirm it matches, and skips the transfer. Staging a 70 GB model with 56 GB already staged: 17:27:34 INFO Upload skipped (file already exists with matching hash) ... 17:28:20 INFO Upload skipped (file already exists with matching hash) ... 17:29:07 INFO Upload skipped (file already exists with matching hash) ... ... six-plus consecutive minutes, no bytes uploaded at all ~45s per skipped ~4 GB shard. That is correct and desirable - it is what makes resume work - but it was indistinguishable from a stall. At 45s per shard it sits inside the 5m window, so the run in flight was fine; the problem is the 600 GB scale this machinery exists to enable, where one shard can plausibly hash for longer than the window. The guard would then fire during verification of a transfer that is working perfectly. Verified mechanism: probeExisting() HEADs the worker and then calls downloader.CalculateSHA(). The staging progress callback is only consulted inside doUpload(), which the skip path never reaches, so observeLoadProgress was called zero times for the whole verify phase. Verification exposed a second, worse bug in the same path: CalculateSHA consults no context at all. An expired cold load kept hashing to completion, compared the hashes, and returned success - reporting a file as staged on a dead load. The failure only surfaced on the NEXT file, whose HEAD died immediately. That is exactly the shape of the red test here, which fails on shard 3. Fix: hash in 1 MiB chunks via hashFileWithActivity(), ticking the cold-load deadline per chunk and checking ctx per chunk. A successful HEAD also counts, since a 200 with a content hash proves the worker is serving right now. Counting hash progress does not make a dead transfer look alive: hashing is bounded, terminating work proportional to file size, in probeExisting it runs only after a HEAD proved the worker was up, and the 24h absolute cap still bounds the whole hold. The alternative of simply widening the window was rejected - it would reintroduce the size cliff this work removes. Assisted-by: Claude Code:claude-opus-4-8[1m] [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler's LocalAI [bot]
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b700a78ae4 |
fix(distributed): make the cold-load hold scale with progress, not wall-clock (#11019)
A 70 GB video checkpoint (longcat-video-avatar-1.5) could not be loaded on a
distributed cluster. The request failed with HTTP 500 after 1499.98s - exactly
the 25m00s cold-load ceiling - while staging was demonstrably healthy: 26 of 57
files and 39 GB transferred at a sustained ~26 MB/s, zero errors, no stalls. It
was not wedged, it was killed by a timer.
ModelLoadCeilingFor covers node selection, backend install, file staging and the
remote LoadModel. Install and load carry their own budgets; staging was covered
only by a FIXED 5-minute margin. But staging time is bytes over bandwidth, not a
constant: 70 GB at 26 MB/s needs ~45m against a 25m ceiling, so the failure is
deterministic for any sufficiently large model rather than a flake. Simply
raising the constant moves the cliff to the next model size - the deployment
target here is checkpoints of 600 GB and beyond.
The ceiling's real purpose is that "a wedged worker can never pin the lock
indefinitely". Progress, not elapsed time, is what distinguishes a wedged worker
from a large one. The hold is now a deadline that extends whenever the transfer
reports bytes and expires a 5-minute stall window after they stop:
- A large model transferring fine continues, for hours if needed.
- A worker that died mid-transfer still fails within the stall window.
Progress is observed at byte level on the transfer itself, via the existing
staging progress callback. Per-file completion would be too coarse - a single
600 GB shard would be indistinguishable from a stall for hours. The observation
point is back-pressured by the socket, so it reflects the network rather than
local disk reads. Observation is coarsened to one timer touch per stall/20 so
the per-read callback stays cheap.
The base budget (unchanged, and still derived from the install and load
timeouts) continues to cover the steps that report no progress, so
LOCALAI_NATS_MODEL_LOAD_TIMEOUT keeps working exactly as before. An absolute
cap of 24h bounds the hold even while progress keeps arriving, so a peer
trickling bytes forever cannot pin the advisory lock; 600 GB at the measured
26 MB/s is ~6.5h, so the cap sits far above any legitimate transfer.
Also fixes the incoherent layering the same error exposed: the resumable upload
carried a 1h retry budget nested inside the 25m ceiling, so the inner budget was
unreachable and the message still blamed it ("failed after 1 attempts within
1h0m0s budget") while the 25m parent was the actual killer. The upload now
adopts the caller's deadline when there is one, and applies its fixed budget
only when nothing above bounded it - which also stops a fixed 1h from
reintroducing the size cliff under the now-extendable parent.
This is the successor to #10968, where a hardcoded 5-minute LoadModel gRPC
timeout was replaced by this derived ceiling. Fixing the inner timeout exposed
the outer ceiling as the new binding constraint.
Assisted-by: Claude Code:claude-opus-4-8[1m] [Read] [Edit] [Bash]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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mudler's LocalAI [bot]
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0eb8a1188d |
fix(worker): give the worker a real health endpoint and a mode-aware HEALTHCHECK (#10999)
fix(worker): give the worker a real health endpoint (#10987) The image bakes in a single HEALTHCHECK that curls http://localhost:8080/readyz, but the same image also runs `local-ai worker`, which serves HTTP on the gRPC base port minus one and never binds 8080. Every worker container was therefore permanently `unhealthy` (43 consecutive failures observed on a production node), which is worse than having no healthcheck: a genuinely broken worker and a perfectly good one both report `unhealthy`, so the signal carries no information and orchestration that keys on it misbehaves. The worker already served /readyz on that port via the file-transfer server, but as a constant 200 — it only proved the listener was bound, which is precisely the failure mode at issue. Readiness now tracks the live NATS connection: all of a worker's actual work (backend lifecycle events, inference dispatch, file staging) arrives over NATS, so a worker whose link is dead is up and useless. Registration is already implied, since the server only starts after registration succeeds. This reports something the controller cannot already see. The node registry's status/last_heartbeat is fed by an HTTP heartbeat to the frontend, a different network path from NATS — a worker can keep heartbeating while its NATS connection is dead and still look healthy in the registry. /healthz stays a constant 200: liveness must not follow readiness, or a NATS blip becomes a cluster-wide restart storm. The HEALTHCHECK is now a script that derives its endpoint from the mode the container is actually running plus the env vars that configure the bind address, so a frontend moved off 8080 with LOCALAI_ADDRESS (broken the same way) and a worker on a non-default base port are both probed correctly. Modes with no HTTP surface (agent-worker, one-shot commands) report healthy rather than false-unhealthy. HEALTHCHECK_ENDPOINT remains as an explicit override, so the workaround shipped in docker-compose.distributed.yaml keeps working; both overrides in that file are now unnecessary and have been removed. Also fixes the latent --start-period gap. Since #10949 a frontend's startup preload materializes HuggingFace artifacts before the HTTP server binds (31 GB observed on a live cluster), so a healthy replica can legitimately fail probes for a long time. --start-period is Docker's knob for exactly this: failures inside it leave the container `starting` instead of burning retries, and it ends early on the first success, so a generous 60m costs a fast-starting container nothing. --timeout drops from 10m to 10s — it is a per-probe deadline, and a localhost curl that has not answered in 10s is itself the fault being detected. Assisted-by: Claude Code:claude-opus-4-8[1m] [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler's LocalAI [bot]
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465d488c90 |
fix(distributed): reject wrong-model requests at the backend (#10970)
fix(distributed): reject wrong-model requests at the backend (#10952) In distributed mode the controller caches a NodeModel row naming a backend's host:port. A worker can recycle a stopped backend's gRPC port for a different model's backend, and probeHealth verifies liveness rather than identity, so the probe succeeds against whatever now occupies the port and the request is dispatched to the wrong backend. The caller gets a silent wrong-model answer. Nothing in the request could catch this: PredictOptions had no model field, so model identity crossed the wire only in ModelOptions.Model at LoadModel time, and the cached-hit path issues no LoadModel. Every backend's "model not loaded" guard checks a nil handle, which a process holding a different model passes, so the stale row was never dropped either. Add PredictOptions.ModelIdentity and enforce it at the point of use: - The controller populates it in gRPCPredictOpts from ModelConfig.Model, the same expression ModelOptions feeds to model.WithModel and therefore the same value the backend received as ModelOptions.Model. Both are read from one config value in one function, so they are equal by construction and the comparison cannot false-reject. - Backends compare it against what they loaded and return NOT_FOUND with a fixed sentinel. Enforced in pkg/grpc/server.go (27 Go backends), an interceptor in backend/python/common (all 36 Python backends, no per-backend change), and the llama-cpp / ik-llama-cpp / ds4 C++ servers. That is every backend with real exposure: kokoros answers all four RPCs with unimplemented and privacy-filter implements none of them. - The router's reconcile drops the stale replica row on a mismatch, so the next request reloads somewhere correct. Empty means "skip the check" on both sides: a controller that predates the field sends nothing, a backend loaded by such a controller has nothing to compare, and the C++ server synthesizes PredictOptions internally for ASR. That keeps upgrades working in both directions. Scoped to the four PredictOptions RPCs. TTSRequest.model and SoundGenerationRequest.model are deliberately NOT validated: FileStagingClient already rewrites them to worker-local absolute paths, so in distributed mode they already differ from the load-time value and comparing them would reject valid requests. IsModelMismatch requires both the NOT_FOUND code and the sentinel, unlike the neighbouring helpers which accept either. insightface's Embedding returns NOT_FOUND "no face detected" on a PredictOptions RPC, and a code-only check would drop a healthy replica row on every faceless image. Assisted-by: Claude Code:claude-opus-4-8[1m] [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler's LocalAI [bot]
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f735cb24c0 |
fix(worker): reap deleted backends and stop models that live on a worker (#10956)
* fix(worker): reap deleted backends and stop models that live on a worker
Three related backend-lifecycle defects, all reachable from the same
production incident on a Jetson/Thor worker: a deleted backend's gRPC
process survived ~40 minutes with its directory removed from disk, a later
model load was routed to that orphan and failed with a certifi path pointing
into the deleted directory, and the admin could not stop the model because
the frontend reported it as not loaded.
1. backend.delete orphaned the process it claimed to delete
------------------------------------------------------------
s.processes is keyed by `modelID#replicaIndex` (buildProcessKey), so the
backend name never appeared in a key and was recorded nowhere on the
process. backend.delete resolved its target via isRunning/stopBackend, whose
prefix path only matches a bare *modelID* - a delete keyed on a backend name
resolved to zero keys, the stop silently no-op'd, and the files were removed
out from under a live process.
The install fast path then handed that orphan back out: it returns any live
process for the (model, replica) slot without checking which backend started
it, so a reinstalled variant inherited the deleted backend's port.
- Record backendName on backendProcess, threaded installBackend ->
startBackend.
- Add resolveProcessKeysForBackend, matching the recorded name and resolving
alias <-> concrete via ListSystemBackends *before* DeleteBackendFromSystem
erases the metadata that carries the alias. Alias resolution failure
degrades to name-only matching so a delete never fails on it.
- backend.stop goes through resolveStopTargets, which accepts a backend
name, a model name, or an exact modelID#replica key. Its payload field is
named "backend" but is published with all three meanings: the admin UI
sends a backend name, UnloadRemoteModel sends a model name, and the
router's abandoned-load reap (#10948) sends an exact replica key.
Narrowing it to backend names alone would strand the latter two.
backend.delete stays strict - its identifier is unambiguously a backend.
- Gate the install fast path on processMatchesBackend so a slot held by a
different backend is restarted rather than reused. Processes with no
recorded name (pre-upgrade) are accepted, so rollout does not restart
every running backend.
- stopBackendExact reports a real stop failure - the process still being
alive afterwards, which is precisely what finishBackendStop already
detects to keep the entry and its port reserved - and backend.delete no
longer replies success when it knew about a process and could not kill it.
"No process was running" stays a success but is logged, so the orphan case
is visible rather than silent.
2. /backend/shutdown reported a running model as missing
---------------------------------------------------------
ModelLoader.deleteProcess short-circuits on a miss in this replica's
in-memory store. In distributed mode the authoritative record of "is this
model loaded" is the shared node registry: a frontend replica that never
served the model itself (load balancer picked a peer, or the replica
restarted) has no local entry. The remote unload path that pkg/model
documents ("when ShutdownModel is called for a model with no local process,
UnloadRemoteModel is called") sat behind that short-circuit, unreachable in
exactly the case it exists for. #10865 reworked this function but kept the
short-circuit at the top, so the gap survived that refactor.
- deleteProcess consults the remote unloader on a local-store miss, via a
shared unloadRemote helper so this branch and the existing
no-local-process branch both prefer #10865's RemoteModelContextUnloader,
preserving force propagation across the distributed boundary.
- UnloadRemoteModelContext reports ErrRemoteModelNotLoaded when no node has
the model; it previously returned nil, making a no-op stop
indistinguishable from a real one. The converse case (nodes have it, none
could be stopped) already errors since #10865 joined the per-node
failures, so that half of the original fix was dropped as redundant.
- Only when the model is absent locally AND cluster-wide does the endpoint
report not-found, now 404 naming both scopes rather than a bare 500.
- modelNotFoundErr becomes the exported ErrModelNotFound so the HTTP layer
can map it without string matching; watchdog's identity comparison becomes
errors.Is.
3. Coverage for the bounded Free() that #10865 shipped untested
----------------------------------------------------------------
The original branch also bounded the pre-stop Free(), but #10865 landed that
fix first (workerBackendFreeTimeout, applied in both stopBackendExact and
handleModelUnload). That production change is therefore DROPPED here as
superseded - master's version is strictly better, since it also releases the
supervisor mutex across the call and keeps the port reserved until
termination completes.
What #10865 did not ship is a test, and the bound is load-bearing: the
router-side reap in #10948 sends backend.stop for an abandoned load, and
against a wedged backend an unbounded Free would swallow that stop before it
reached the process. Nothing failed if the bound regressed.
The spec stands up a real gRPC backend server whose Free handler never
returns - what a Python backend looks like when its single worker thread
(PYTHON_GRPC_MAX_WORKERS=1 on 37 backends) is occupied by a stuck LoadModel.
A stub socket is not sufficient and was tried first: without a completed
HTTP/2 handshake, gRPC's own ~20s connect timeout ends the call, so that
version passed against the very bug it targets. With the connection READY,
only the caller's deadline can end it, so the spec hangs to its 60s limit if
the timeout is removed and passes with it.
Its fixture process is deliberately never started. go-processmanager v0.1.1
writes Process.pid from readPID() without synchronization, so a live process
races its own monitor goroutine under -race - reproducible with a bare
Run()+Stop() and unrelated to this spec. Since
scripts/model-lifecycle-conformance.sh runs this package with -race and is
fail-closed, starting one would turn that gate red on an upstream defect. An
unstarted process still proves the point: the stop is reached and the slot
released, which is exactly what an unbounded Free prevents.
Verified: make lint (new-from-merge-base origin/master) reports 0 issues;
scripts/model-lifecycle-conformance.sh passes all three stages including the
FizzBee liveness check (1458 states, IsLive: true).
Assisted-by: Claude:claude-opus-4-8 golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(distributed): keep remote unload idempotent, ask presence separately
|
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mudler's LocalAI [bot]
|
fb4c61d1c9 |
fix(distributed): configurable remote model-load timeout, and reap the load when it times out (#10948)
* fix(distributed): make the remote LoadModel deadline configurable
The router hardcoded a 5 minute gRPC deadline for the remote LoadModel
call. Staging finishes before the timer starts, so those five minutes
cover only the worker backend's own checkpoint load and pipeline init.
A cold load of meituan-longcat/LongCat-Video-Avatar-1.5 (~83 GB) on an
ARM64 Thor worker fails at exactly 302s with DeadlineExceeded while the
backend process is still making progress (CPU time accumulating, RSS
moving as weights are mapped), so the load was cut short rather than
wedged.
Add LOCALAI_NATS_MODEL_LOAD_TIMEOUT / --model-load-timeout mirroring the
existing backend-install timeout knob, defaulting to 5m so unset
clusters keep today's behaviour.
The cold-load hold ceiling (which bounds how long one load may hold the
per-model advisory lock) was derived from the install timeout alone, so
raising the load deadline past it would have been silently clipped.
Derive it from both budgets via ModelLoadCeilingFor:
max(install + load + 5m staging margin, 25m)
With the defaults that is 15m + 5m + 5m = 25m, identical to the previous
constant, and the 25m floor means shrinking either budget can never
tighten the ceiling below what clusters relied on before.
Assisted-by: Claude:claude-opus-4-8 golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(distributed): reap the abandoned replica when a remote load times out
The gRPC deadline on the remote LoadModel call only cancels the client
side. A backend blocked in a synchronous weight load never observes its
cancelled handler context, so when scheduleAndLoad gave up it left the
worker loading with nobody waiting for the result.
Observed on an ARM64 Thor worker loading LongCat-Video-Avatar-1.5: the
client returned DeadlineExceeded at 302s, and the backend process was
still alive 30 minutes later having pulled ~57GB from HuggingFace. Every
retry stacked another multi-GB loader on the worker; they had to be
reaped by hand via POST /api/nodes/:id/models/unload.
Send backend.stop for the exact `modelID#replicaIndex` process key we
just abandoned. The exact key matters: a bare model ID stops every
replica on that node, including healthy ones serving traffic.
Only a deadline or cancellation triggers the reap. Any other LoadModel
failure is the backend answering, which means its handler returned and
the process is idle - stopping it there would discard a warm process and
its downloaded weights. The reap is best-effort and never replaces the
load error the caller is waiting on.
The `modelID#replicaIndex` format was already hand-rolled in two places
(the worker's buildProcessKey and pkg/model's log store). Rather than add
a third, export model.BackendProcessKey from pkg/model, the lowest common
dependency of both sides.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 golangci-lint
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
|
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mudler's LocalAI [bot]
|
626ae4d51e |
fix(model-artifacts): materialize longcat-video on the controller, and support companion repos (#10949)
* fix(model-artifacts): materialize longcat-video checkpoints on the controller longcat-video loads a checkpoint directory: its backend.py takes request.ModelFile when os.path.isdir(request.ModelFile) and otherwise falls back to snapshot_download. That places it in the same class as transformers/vllm/diffusers/sglang, but the allow-list added in #10910 did not enumerate it, so PrimaryArtifactSpec returned no managed artifact for a bare HuggingFace repo id. The consequence in distributed mode: nothing was acquired on the controller, ModelFileName fell through to the raw repo id, and staging skipped the resulting phantom /models/<owner>/<repo> path. The worker received a blank ModelFile, fell back to request.Model, and downloaded ~83GB from HuggingFace inside the remote LoadModel deadline - so the load could only ever fail with DeadlineExceeded while an abandoned backend process kept downloading. Note this materializes the full repository. The backend restricts its own snapshot_download with allow_patterns, and the avatar repo ships both base_model/ and base_model_int8/ where only one is ever loaded; inferred specs have no way to carry patterns today. Tracked separately. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): warn when staging skips a non-existent model path stageModelFiles logs "Staging model files for remote node" up front, then silently drops any path field that does not exist on the controller. The skip itself is legitimate and must stay: a backend outside managedArtifactBackends that takes a bare HuggingFace repo id gets an optimistically constructed path (ModelFileName falls through to the raw model reference) that was never materialized, and sources its own weights on the worker. Erroring would break those configs. But at debug level the operator is left with a reassuring staging line and no trace of the skip, so a genuine controller-side acquisition gap is indistinguishable from a healthy pass-through - it surfaces much later as a remote LoadModel timeout, on a worker that is quietly downloading tens of gigabytes. Raise the skip to warn and name the field, path, node and tracking key. Behavior is unchanged. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(model-artifacts): allow a config to declare companion artifacts A composed pipeline needs more than one HuggingFace snapshot. LongCat-Video-Avatar-1.5 loads its own transformer but takes the tokenizer, text encoder and VAE from the separate LongCat-Video base repo, so a single-artifact config cannot express it and the backend is left to fetch the second repo itself at load time. Widen the artifact model to target: model plus any number of named target: companion entries. Normalize accepts the new target and constrains a companion name to [a-z0-9][a-z0-9_-]{0,63} because that name is the option key the backend later receives; a companion may not claim primary_file, which only means anything for a load target. ModelConfig.Validate requires exactly one primary and requires it first, since Artifacts[0] is what ModelFileName, size estimation and staging all resolve from. Both acquisition paths now loop instead of touching index 0 alone: preloadOne for an already-installed config, bindPrimaryArtifact for a gallery install. Failure policy differs by provenance. An inferred primary keeps its warn-and-fall-back, because the legacy download path still exists for it. Companions are explicit by construction, so they are all-or-nothing: a config naming one is asserting the backend needs it, and failing at the acquisition boundary is far more legible than a missing-weights error surfacing later inside the backend. The cache key is deliberately unchanged. It hashes source identity only, never name or target, so every already-installed managed model still hits its existing snapshot instead of silently re-downloading. Two specs pin that: one proving a companion and a primary with identical sources agree on the key, and one pinning the digest of a known primary outright. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(model-artifacts): hand resolved companion snapshots to the backend A materialized companion is useless until the backend can find it, and its location is a content-addressed cache key that does not exist until the artifact resolves. A static gallery override cannot carry that, and persisting it into the config YAML would rot the moment a re-resolve produced a new key. Synthesize it instead at load time: each resolved companion becomes "<artifact name>:<snapshot path>" in ModelOptions.Options, reusing the key:value convention backends already parse for options like attention_backend. The value stays relative to the models directory so a remote worker can resolve it under its own ModelPath once staging has rewritten the model root. An option the author set explicitly always wins, so pinning a companion to a local checkout still beats the managed snapshot. longcat-video resolves base_model through ModelPath, the same convention qwen-tts, voxcpm, outetts and ace-step already use for companion assets. Its sibling-directory heuristic is deleted: it looked for a LongCat-Video directory next to the model, which cannot exist under the content addressed .artifacts/huggingface/<key>/snapshot layout, so it was dead code the moment the model became managed. The gallery entry declares both repositories and restricts each with allow_patterns. The avatar repo ships base_model/ and base_model_int8/ and only ever loads one, so fetching the whole repo would roughly double the download. The patterns match the entry's own options (use_distill true, use_int8 default false); enabling use_int8 here also requires adding base_model_int8/**, which is called out in the entry. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): stage managed artifact trees from the models root Staging anchored the worker's models directory on the primary snapshot whenever a model was managed, so a companion snapshot could not reach the worker at all. frontendModelsDir was derived by stripping the Model relative path off the end of ModelFile. For a managed artifact nothing matches: ModelFile is .artifacts/huggingface/<key>/snapshot while Model stays a bare HuggingFace repo id, so the strip was a no-op and the "models directory" came out as the snapshot itself. Two consequences, both silent. Staging keys lost the .artifacts/huggingface/<key>/snapshot prefix, so two snapshots of one model were indistinguishable on the worker. And a companion, which lives in a sibling snapshot directory outside the primary, fell outside that directory entirely: StagingKeyMapper.Key collapsed its files to bare basenames and resolveOptionPath could not resolve the relative option at all, so it was skipped without a word. Derive the models root from the artifact tree instead when the path runs through it, and compute the worker's ModelPath from the file's path relative to that root rather than from the Model field. The legacy layout is unaffected: where Model really is the relative path, the new derivation reduces to the old one, which a regression spec pins. This deliberately changes an invariant that router_dirstage_test.go pinned: for a managed primary, ModelFile and ModelPath were both the snapshot directory, and staging keys were relative to it. Now ModelFile is the snapshot, ModelPath is the models root above it, and keys keep the full relative path. That spec is updated rather than accommodated, with the reasoning recorded inline, because the old invariant is exactly what made a sibling companion unreachable. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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|
mudler's LocalAI [bot]
|
b19afb192a |
fix(distributed): backend discovery hid GPU-only backends behind the controller's capability (#10947)
* fix(backends): list backends runnable on worker nodes in distributed mode GET /backends/available filtered the gallery against the system state of the host serving the request. In a distributed deployment that host is the controller, which typically has no GPU, while the GPUs live on worker nodes. Any meta backend whose capabilities map lacks a "default" (or "cpu") key was therefore dropped from the listing entirely — longcat-video, vllm-omni, ltx-video, parakeet, edgetam and qwentts were invisible in the UI even though installing them by name on a GPU worker worked fine. Workers now report their own meta-backend capability at registration and the controller persists it on the node row. The controller cannot derive it: OS-dependent capabilities (metal, darwin-x86, nvidia-l4t) and the CUDA runtime refinements are only observable on the worker. Nodes registered before this field existed fall back to a coarse capability derived from their GPU vendor and VRAM. Backend discovery then evaluates compatibility as the union over healthy backend nodes, so a backend runnable on any node is offered while one no node can run stays hidden. Each remote capability is evaluated through a capability-pinned system state, otherwise a forced capability on the controller image (LOCALAI_FORCE_META_BACKEND_CAPABILITY or /run/localai/capability) would silently override every worker's verdict. With no registered nodes the listing is byte-for-byte what it was, so single-node deployments are unaffected. Also fixes the same-root-cause misclassification in /api/operations, which used the capability-filtered listing to decide whether an operation was a backend or a model install. A GPU-only backend installing on a worker is still a backend operation on the controller, so that lookup is now unfiltered. Assisted-by: Claude:claude-opus-4-8 golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(backends): union worker capabilities in backend discovery Implementation for the specs added in the previous commit, plus the two remaining discovery endpoints. Capability-filtered backend discovery evaluated compatibility against the system state of the host serving the request. In a distributed deployment that host is the controller, which typically has no GPU, while the GPUs live on worker nodes. Any meta backend whose capabilities map lacks a "default" (or "cpu") key was dropped entirely — longcat-video, vllm-omni, ltx-video, parakeet, edgetam and qwentts were invisible in the UI even though installing them by name on a GPU worker worked fine. Workers now report their own meta-backend capability at registration and the controller persists it on the node row. The controller cannot derive it: OS-dependent capabilities (metal, darwin-x86, nvidia-l4t) and the CUDA runtime refinements are only observable on the worker. Nodes registered before this field existed fall back to a coarse capability derived from their GPU vendor and VRAM. Discovery then evaluates compatibility as the union over healthy backend nodes, so a backend runnable on any node is offered while one no node can run stays hidden. Each remote capability is evaluated through a capability-pinned system state, otherwise a forced capability on the controller image (LOCALAI_FORCE_META_BACKEND_CAPABILITY or /run/localai/capability) would silently override every worker's verdict. With no registered nodes the listing is byte-for-byte what it was, so single-node deployments are unaffected. Four surfaces shared this root cause and are all routed through the same helper now: - GET /backends/available - GET /api/fine-tuning/backends - GET /api/quantization/backends - /api/operations backend-vs-model classification, which additionally had no reason to filter by capability at all: a GPU-only backend installing on a worker is still a backend operation on the controller, so that lookup is now unfiltered. Assisted-by: Claude:claude-opus-4-8 golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
|
9c43b2da8f |
fix(model): make backend shutdown model-scoped (#10865)
Avoid holding the global loader lock across backend lifecycle waits and propagate forced shutdown through distributed workers. Track parallel requests with in-flight counters and reserve worker ports until process termination. Add focused race tests and an authoritative FizzBee lifecycle model with a fail-closed conformance target. Assisted-by: Codex:GPT-5 [FizzBee] [Ginkgo] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]
|
8cec22c3b7 |
feat(vram): per-node VRAM allocation budget (LOCALAI_VRAM_BUDGET) (#10833)
* feat(vram): add vrambudget primitive for per-node VRAM caps Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): apply default VRAM budget in xsysinfo aggregate getters Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): wire LOCALAI_VRAM_BUDGET flag to xsysinfo default budget Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): persist VRAM budget via runtime settings with live apply Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(vram): reset process-global VRAM budget after runtime-settings spec Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): add VRAM budget field to Settings page Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): store and enforce per-node VRAM budget in the node registry Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): apply per-node VRAM budget in router hardware defaults Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): report worker VRAM budget in node registration The distributed worker now reports its operator-set VRAM budget string (LOCALAI_VRAM_BUDGET) to the server on registration. The worker keeps reporting RAW total/available VRAM and never sets the xsysinfo process-global budget (that stays standalone-only); the server resolves and enforces the budget uniformly (Task 6). Also closes a Task 6 gap: on re-registration, a struct Updates zero-skips an empty budget, so a worker that dropped LOCALAI_VRAM_BUDGET left the stale cap in place. For non-admin-override nodes the budget columns are now force-written (map Updates) even when empty, so removing the env var clears the cap; admin overrides are preserved unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(vram): drop em dash from worker-clear comment Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): add node VRAM budget admin endpoints Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): add node VRAM budget control to the node UI Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): expose set_node_vram_budget MCP admin tool Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(vram): document LOCALAI_VRAM_BUDGET and node VRAM budget UI Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vram): avoid double-applying VRAM budget in GetResourceAggregateInfo The GPU-branch aggregate returned by GetResourceInfo is sourced from GetGPUAggregateInfo, which already caps total/free/used against the process-wide VRAM budget. GetResourceAggregateInfo then applied the budget a second time. For an absolute budget this is idempotent, but for a percentage budget b.Apply resolves the ceiling as a fraction of its input total, so a second pass yields P*(P*T) instead of P*T and distorts UsagePercent (read by the memory reclaimer in pkg/model/watchdog.go). Remove the redundant second application so the budget is applied exactly once, against the raw physical totals, upstream in GetGPUAggregateInfo. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vram): implement SetNodeVRAMBudget on mcp assistant test stub The LocalAIClient interface gained SetNodeVRAMBudget; the stubClient in core/http/endpoints/mcp used by the assistant tests is a separate implementer and needs the method too (broke golangci-lint typecheck and both test jobs). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
|
3601174ce0 |
fix(distributed): make per-node backend upgrade actually upgrade (#10838)
* test(core/http): make the suite's HTTP port overridable app_test.go and openresponses_test.go hardcoded 127.0.0.1:9090. When another service already listens on 9090 the suite does not fail fast: the server goroutine logs the bind error and the specs then poll whatever is squatting the port until Eventually times out. On machines where 9090 is permanently taken this makes the pre-commit coverage gate impossible to pass. Introduce testHTTPAddr, defaulting to 127.0.0.1:9090 (what CI has always used) and overridable via LOCALAI_TEST_HTTP_PORT for local runs. Assisted-by: Claude:claude-fable-5 golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): make per-node backend upgrade actually upgrade The node detail page's Upgrade button reused the node-scoped install path (POST /api/nodes/:id/backends/install). That fires NATS backend.install with force=false, and the worker's install handler is deliberately "ensure installed": when the backend binary already exists on disk it short-circuits without touching the gallery. Since only an installed backend can be upgraded, the whole chain was a guaranteed successful no-op - the UI then toasted "backend upgraded" without even waiting for the async job. Route upgrades through the real force-reinstall path instead: - BackendManager.UpgradeBackend now receives the ManagementOp (like InstallBackend already did) so implementations can honor op.TargetNodeID. - DistributedBackendManager.UpgradeBackend scopes the backend.upgrade fan-out to op.TargetNodeID when set, and errors when the target node does not report the backend as installed. - New POST /api/nodes/:id/backends/upgrade endpoint enqueues an Upgrade=true node-scoped op (async 202 + jobID, mirroring install). - NodeDetail UI calls the new endpoint and reports the dispatch ("Upgrading ... on this node...") instead of claiming success; the Operations panel tracks the actual job. Verified against a live local cluster (NATS + Postgres + two workers): the target worker stops the running process, force-reinstalls from the gallery and re-downloads the OCI image; the second worker receives no backend.upgrade event; upgrading a backend missing from the target node fails the job with a clear error. Assisted-by: Claude:claude-fable-5 golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
|
bcc41219f7 |
feat: materialize Hugging Face model artifacts (#10825)
* feat(config): add model artifact source contract Assisted-by: Codex:GPT-5 [Codex] * feat(downloader): add authenticated raw-byte progress Assisted-by: Codex:GPT-5 [Codex] * feat(huggingface): resolve immutable snapshot manifests Assisted-by: Codex:GPT-5 [Codex] * feat(models): add artifact storage primitives Assisted-by: Codex:GPT-5 [Codex] * feat(models): materialize pinned Hugging Face snapshots Assisted-by: Codex:GPT-5 [Codex] * feat(models): bind managed snapshots at runtime Assisted-by: Codex:GPT-5 [Codex] * feat(gallery): materialize model artifacts during install Assisted-by: Codex:GPT-5 [Codex] * feat(gallery): declare managed Hugging Face artifacts Assisted-by: Codex:GPT-5 [Codex] * feat(models): preload managed model artifacts Assisted-by: Codex:GPT-5 [Codex] * fix(gallery): retain shared artifact caches on delete Assisted-by: Codex:GPT-5 [Codex] * feat(models): report artifact acquisition progress Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): load managed models from ModelFile Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): load staged speech model snapshots Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): use staged snapshots in engine backends Assisted-by: Codex:GPT-5 [Codex] * test(distributed): cover staged artifact snapshots Assisted-by: Codex:GPT-5 [Codex] * docs: explain managed model artifacts Assisted-by: Codex:GPT-5 [Codex] * docs: add product design context Assisted-by: Codex:GPT-5 [Codex] * feat(ui): show model artifact download progress Assisted-by: Codex:GPT-5 [Codex] * Eagerly materialize Hugging Face artifacts Materialize HF-backed model references as managed GGUF artifacts during load, with lazy download retained only as fallback. Assisted-by: Codex:GPT-5 [shell] * Refactor HF downloads through a shared executor Assisted-by: Codex:GPT-5 [shell] * drop Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
|
b224c96db6 |
fix(config): only inject llama.cpp serving options on the llama.cpp path (#10822)
SetDefaults injected the llama.cpp server options cache_reuse (ApplyServingDefaults) and parallel (ApplyHardwareDefaults, re-applied per selected node by the distributed router) onto every model config regardless of backend. Every other backend ignores options it does not understand, so this was harmless until longcat-video, which strictly validates its options and fails LoadModel with "unknown model option(s): cache_reuse, parallel". Gate both injections behind a new UsesLlamaCppServingOptions allow-list (llama-cpp plus the empty/auto-detect case that resolves to llama.cpp from a GGUF file, mirroring how llamaCppDefaults is registered). This follows the existing UsesLlamaSamplerDefaults precedent for llama-only defaults. The typed NBatch field is deliberately left alone: it is a proto field every backend simply ignores, which is why batch never triggered the error. Also harden the longcat-video backend to warn-and-ignore unknown model options and request params through a testable select_known_options helper, matching the other LocalAI Python backends, so a future server-injected option cannot break loading again. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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4056283aa4 |
[voice] feat: add managed voice cloning profiles (#10799)
* feat(ui): add voice library workflow Give administrators a production-ready flow to record or upload consented reference audio, manage reusable profiles, inspect API usage, discover compatible models, and hand a saved voice directly to text-to-speech. Assisted-by: Codex:gpt-5 * feat(voice): add managed voice cloning profiles Make reusable reference voices manageable through the admin API instead of requiring model-directory and YAML edits. Discover compatible installed and gallery models from server-side backend capabilities, retain explicit model configuration controls, and stage saved references for supported backends. Expose profile management through REST and MCP, document backend-specific behavior, and cover the workflow from profile creation through real Qwen3-TTS synthesis. Harden the agent-job HTTP test against completion racing cancellation. Assisted-by: Codex:gpt-5 --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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b00422e45f |
feat(backends): add LongCat video and avatar generation (#10792)
* feat(backends): add LongCat video and avatar generation Assisted-by: Codex:GPT-5 [apply_patch] [exec_command] [web] * refactor(config): declare model I/O modalities Make model configs declare input and output modalities so capability discovery no longer branches on backend or checkpoint names. Complete the LongCat gallery and user documentation, make the SDPA patch apply to the pinned upstream revision, and stabilize the Agent Jobs race exposed by the required hook. Assisted-by: Codex:GPT-5 [web] --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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29001a88c1 |
fix(distributed): don't let a dead worker pin the model-load advisory lock (#10600)
* fix(distributed): don't let a dead worker pin the model-load advisory lock In distributed mode a chat request could fail with: failed to route model with internal loader: routing model ...: loading model ...: advisorylock: acquiring lock <id>: ERROR: canceling statement due to lock timeout (SQLSTATE 55P03) Root cause is two independent defects in the cross-replica model-load path: 1. SmartRouter.Route holds a per-model PostgreSQL advisory lock for the whole cold-load sequence, which includes installBackendOnNode -> InstallBackend, a NATS request-reply with a 15m deadline (DefaultBackendInstallTimeout) that ignored ctx. When the chosen worker died mid-install, the holder sat on the lock for up to 15m. The detached loadCtx (WithoutCancel) had no deadline, so nothing capped the hold. 2. The acquiring statement, pg_advisory_lock(), is subject to any deployment global lock_timeout. A common operator setting (e.g. 10s) aborts the wait with SQLSTATE 55P03, so every other replica's request for that model hard -errored instead of waiting for the in-progress load and reusing it. For the ~15m window the model was effectively unroutable. Fixes: - advisorylock.WithLockCtx (postgres): SET lock_timeout = 0 on its dedicated connection (RESET before it returns to the pool) so the Go context, not a deployment-wide GUC, governs how long we wait. Waiters now block and then re-check, reusing the model another replica just loaded. - SmartRouter: bound the detached loadCtx with a single ModelLoadCeiling so the lock is always released in bounded time even if a sub-step wedges. Default is the configured backend.install deadline + 10m (staging + LoadModel margin), so a legitimately slow load is never cut. - installBackendOnNode: use singleflight.DoChan + select on ctx.Done() so the install wait honors cancellation; the ceiling can then actually free a caller pinned behind a dead worker. The shared install still coalesces via singleflight. Reproduced both defects as failing tests first (a real 55P03 against a testcontainer with a short lock_timeout; a wedged install that blocks Route) and confirmed green. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(distributed): bound advisory-lock wait instead of disabling lock_timeout Setting lock_timeout = 0 to override a deployment's short global lock_timeout meant "wait forever" server-side. Safe for SmartRouter.Route (its loadCtx now carries the model-load ceiling) but unsafe for the schema-migration callers that pass context.Background(): a holder whose session never releases would hang them indefinitely. Derive the server-side lock_timeout from the caller's context instead: its remaining budget plus a margin (so the Go context's cancellation still wins with a clean error and the server bound is only a backstop), or a finite 30m backstop when the context has no deadline. Never zero - "wait forever" is no longer possible, while a deployment's hostile short lock_timeout is still overridden so legitimate cross-replica waits don't fail with 55P03. Added a spec proving a deadline-less waiter gives up at the (shrunk) backstop rather than hanging. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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Richard Palethorpe
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5d0c43ec6e |
feat(realtime): Semantic VAD EOU token (#10444)
* feat(realtime): EOU-driven semantic_vad turn detection Add a `semantic_vad` turn-detection mode to the realtime API that feeds the transcription model live and decides "the user finished speaking" from the `<EOU>` end-of-utterance token rather than from silence alone. When EOU fires the turn commits immediately (~0.3s); otherwise it falls back to an eagerness-scaled silence threshold (low/med/high = 8/4/2s). Plumbing, bottom to top: - proto: `AudioTranscriptionLive` bidirectional RPC (config-first oneof, mono float PCM @16k, ready-ack / Unimplemented degrade signal) plus `TranscriptResult.eou` for the unary retranscribe gate. - pkg/grpc: client/server/base/embed scaffolding for the bidi stream, modeled on AudioTransformStream; release stream conns on terminal Recv. - parakeet-cpp: live transcription RPC with per-C-call engine locking (one live stream per turn, finalize+free at commit); bump parakeet.cpp to ABI v5 — incremental StreamingMel (no more quadratic per-feed mel recompute that delayed EOU on long turns) and the <EOU>/<EOB> split; strip the literal <EOU>/<EOB> from offline text and set Eou. - core/backend: LiveTranscriptionSession wrapper + pipeline `turn_detection:` config block (type/eagerness/retranscribe). - realtime: semantic_vad integration — live input captions streamed as transcription deltas while the user speaks, EOU-immediate commit with eagerness fallback, optional retranscribe gate (batch re-decode must also end in <EOU> to confirm), clause synthesis off the LLM token callback, and per-turn live-transcription / model_load telemetry. - UI: show the realtime pipeline components as a vertical list. Docs and tests included; opt-in via the pipeline YAML or per-session `session.update`. Non-streaming STT backends degrade to silence-only. Assisted-by: Claude Code:claude-opus-4-8 [Read] [Edit] [Write] [Bash] Assisted-by: Claude Code:claude-fable-5 [Read] [Edit] [Bash] Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): explicit formally-verified state machines + parakeet streaming driver The realtime API had several implicit state machines whose state was inferred from scattered booleans, channels, and five separate mutexes, leaving illegal/inconsistent states reachable. Make them explicit and keep the implementation in step with a formal design; rework the parakeet streaming backend along the same lines. Realtime state machines (M1-M5). Each is a sealed sum-type State/Event/Effect with a total, pure Next(state,event)->(state,[]effect) behind a single-writer Coordinator: M1 conncoord connection lifecycle: VAD toggle + once-only teardown (replaces vadServerStarted + a `done` channel closed from two sites). M2 turncoord turn detection: collapses speechStarted and the live-stream "turn open" flag into one state, so discardTurn can no longer desync them and suppress the next onset. M3 respcoord response coordination: serializes the dual-writer start/cancel so at most one response is live; one response.done per response.create. M4 compactcoord conversation compaction: single-flight (replaces the `compacting atomic.Bool` CAS). M5 ttscoord TTS pipeline: open->closing->closed, idempotent wait(), rejects enqueue-after-close (was a silent drop). The Coordinator/Sink/Next plumbing — only the sealed types and Next differed per machine — is extracted once into core/http/endpoints/openai/coordinator as a generic Coordinator[S,E,F]; each machine keeps its public API via type aliases, so no sink, call-site, or test moved. Hierarchy. session_lifecycle.fizz models M1 as the parent region with its children (M2/M3/M4) as one statechart and asserts ChildrenDieWithParent (conn torn => all children terminal, none start after teardown). respcoord and compactcoord gain an absorbing Terminated state + Shutdown event; conncoord's teardown drives the children terminal. This closes a compaction teardown gap: a fire-and-forget compaction could outlive a torn session — compactionSink now takes a session-scoped cancellable context + WaitGroup and joins the in-flight summarize+evict on shutdown. Formal verification. formal-verification/ holds one authoritative FizzBee spec per machine plus the composition spec, each with an always-assertion and a documented one-line edit that makes the checker fail (verified non-vacuous). scripts/realtime-conformance.sh is fail-closed: all Go conformance suites under -race AND a model-check of every .fizz spec; a missing FizzBee is a hard error (only the loud REALTIME_CONFORMANCE_SKIP_FIZZBEE=1 bypasses it, never in CI). FizzBee is pinned by sha256 and installed via scripts/install-fizzbee.sh into .tools/ (gitignored). Wired as make test-realtime-conformance, a CI workflow, and a pre-commit path filter. Go conformance tests are Ginkgo/Gomega (per the repo's forbidigo lint): transition tables + fixed-seed property walks + concurrent/-race specs, no rapid dependency. Design map: docs/design/realtime-state-machines.md. Parakeet streaming backend. The same treatment applied to the parakeet-cpp streaming paths: - AudioTranscriptionStream returns codes.Unimplemented for non-streaming models instead of decoding offline and emitting it as one delta + final. A client that asked for streaming learns the model cannot stream rather than receiving a batch result shaped like a stream. New grpcerrors.StreamTranscriptionUnsupported carries that signal; the HTTP /v1/audio/transcriptions stream path surfaces it as an SSE error event. Mirrors AudioTranscriptionLive, which already did this. - utteranceBoundary (boundary.go): a single definition of the end-of-utterance latch, replacing three open-coded finalEou toggles. Modelled as a two-valued type so illegal states are unrepresentable. - Shared decode driver (driver.go): streamFeedResult (one per-feed event) + feedChunk (hides the ABI v4 JSON vs text-only split) + feedSlices + flushTail. The feed loop is written once. - AudioTranscriptionLive becomes a bidi adapter: it streams the per-feed {delta,eou,eob,words} the realtime turn detector consumes and a terminal FinalResult carrying only Text. Segments/duration/eou are offline-only and no longer produced (nor read) on the live path; liveTraceState drops the terminal eou and keeps the per-feed eou_events count. - AudioTranscriptionStream + streamJSON merge into one driver-based function; streamSegmenter is generalized to the unified event with a text-only fallback that preserves the legacy (no-words) library's per-utterance segmentation. Verified: build/vet/gofumpt clean, golangci-lint 0 issues, all coordinator and parakeet packages under -race, the fail-closed conformance gate green, and make test-realtime (12 e2e WS+WebRTC). Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]
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f3d829e2ef |
feat(distributed): add LOCALAI_DISTRIBUTED_SHARED_MODELS to skip staging on shared volumes (#10556) (#10566)
In distributed mode, even when the frontend and workers share the same models directory via a shared volume mount, starting a model on a worker re-staged (re-downloaded) it: stageModelFiles always uploads model files into a tracking-key-namespaced subdir on the worker, and the staging probe only checks that staged location, so a file already present on the shared volume at the canonical path was never reused. Add a config switch LOCALAI_DISTRIBUTED_SHARED_MODELS (default false). When enabled, the operator asserts that all nodes mount the SAME models directory at the SAME path, so staging is unnecessary: the frontend's absolute model paths are already valid on the worker. In that mode stageModelFiles returns the cloned opts unchanged without uploading, leaving the path fields pointing at their canonical absolute paths so the worker loads them directly from the shared volume. The value is plumbed from DistributedConfig through SmartRouterOptions into the SmartRouter. Docs and docker-compose.distributed.yaml updated. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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56600eec3e |
fix(nodes): show a node's existing labels on the detail view (#10529)
fix(nodes): return labels in single-node GET so the detail view shows them The node detail view (/app/nodes/:id) reads `node.labels` to render a node's existing labels, but the single-node GET endpoint returned a bare BackendNode whose Labels live in a separate table - so the list was always empty and operators could only add labels, never see what was already set (#10527). The same response also lacked in_flight_count and model_count. Add NodeRegistry.GetWithExtras, mirroring the existing List vs ListWithExtras split: bare Get stays cheap for the routing hot paths and existence checks, while the detail endpoint uses the enriched variant to attach the labels map and live counts. No frontend change is needed - the UI already renders existing labels once the data is present. Closes #10527 Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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79783120dd |
fix(config): gate parallel-slot default on per-device VRAM too (#10485) (#10507)
The first #10485 fix (#10494) made the Blackwell physical-batch boost per-device/context-aware, which neutralized the big compute-buffer OOM, but the reporter's 2x16 GiB consumer Blackwell still OOM'd. Tracing the post-fix log: the model now loads its weights, builds the main context and warms up fine, and dies only on the *last* allocation — the MTP draft context's 800 MiB KV cache on the tighter device. #10411 changed only two defaults: the physical batch (now gated) and a VRAM-scaled parallel-slot count. The KV cache is unified (n_ctx_seq == full context proves slots share the budget, so parallel doesn't multiply KV), but n_seq_max=4 still adds per-slot compute-graph / context-checkpoint / output scratch. On a device packed ~99% by a 27B model spanning both cards, that overhead is the few-hundred-MiB straw — which is why reverting #10411 (and only #10411) restores a working load. Gate the parallel-slot default on the same per-device headroom predicate as the batch boost: when a large context already fills a single card (largeContextForDevice), keep n_parallel=1. A user running one big-context model that barely fits across two consumer GPUs is not serving four concurrent tenants. Small contexts and large unified-memory devices (GB10) keep full concurrency. Applied on both the single-host path and the distributed router. Also make the auto-tuning visible and reversible (the debugging here needed DEBUG logs and a git bisect): - Log the effective performance-relevant runtime options at INFO once per model load ("effective runtime tuning …": context, n_batch, n_gpu_layers, parallel, flash_attention, f16) so an admin can see what will run and pin or override any value in the model YAML. - LOCALAI_DISABLE_HARDWARE_DEFAULTS=true skips the hardware auto-tuning entirely (mirrors LOCALAI_DISABLE_GUESSING) for stock llama.cpp behavior. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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0d6de15ae9 |
fix(config): per-device VRAM headroom for Blackwell defaults (#10485) (#10494)
The hardware-tuned defaults from #10411 were measured on a GB10 / DGX Spark (128 GiB unified memory) and over-provisioned multi-GPU consumer Blackwell (e.g. 2x16 GiB RTX 50-series) into CUDA OOM during model init: - The Blackwell physical batch (512 -> 2048) sets both n_batch and n_ubatch. The compute buffer scales ~n_ubatch * n_ctx and is allocated PER DEVICE (it can't be split across GPUs), so a large context turns ub2048 into multi-GiB of scratch that must fit one 16 GiB card. - The VRAM-scaled parallel-slot default tiered off TotalAvailableVRAM(), which SUMS all GPUs (2x16 -> "32 GiB" -> 8 slots), but the allocations are per-device. Make both decisions per-device and context-aware: - xsysinfo.MinPerGPUVRAM() reports the smallest device's VRAM; localGPU() uses it so the parallel tier and batch guard reason about one card. - PhysicalBatchForContext(gpu, ctx) raises the batch only when the extra compute buffer fits VRAM/4 at this model's context (16 GiB crosses over ~174k ctx, 32 GiB ~349k; GB10 reports system RAM so it still clears it). - Apply hardware defaults AFTER runBackendHooks in SetDefaults so the GGUF-guessed context is resolved before the batch decision. - The distributed router gates the node batch the same way. Unified-memory devices (GB10, Apple) report system RAM as their single device's VRAM, so they keep the prefill win. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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e5620989dd |
refactor(distributed): make in-flight tracking coverage a compile-time contract (#10476)
PR #10475 fixed SoundDetection in-flight tracking, but the underlying trap remains: InFlightTrackingClient embedded the whole grpc.Backend interface "for passthrough of untracked methods", so any newly added inference method is silently satisfied by the embedded passthrough and never wrapped with track(). That leaves onFirstComplete unfired and in-flight stuck at 1 - the exact SoundDetection bug, waiting to recur for the next backend method. Close the gap at the type level instead of relying on reviewers to remember: - Split grpc.Backend into two composed sub-interfaces: InferenceBackend (methods that are one discrete inference call and must be tracked) and ControlBackend (control-plane calls plus the streaming constructors whose work spans the returned stream, safe to pass through). The classification now lives next to the interface it documents. - InFlightTrackingClient embeds only grpc.ControlBackend and implements every InferenceBackend method explicitly, delegating to an inner InferenceBackend. A `var _ grpc.Backend = (*InFlightTrackingClient)(nil)` assertion makes the package fail to compile if any inference method is left unwrapped. Now adding a method to InferenceBackend is a build error (at the assertion and every call site: "does not implement grpc.Backend (missing method X)"), not a silent runtime leak - and the obvious fix is to copy a neighbouring wrapper, which calls track(). No runtime guard or reviewer vigilance required. Pure refactor: the composed Backend interface is identical to the old flat one, so all implementers and consumers are unaffected (verified with a full `go build ./...`). Behaviour is unchanged; the existing nodes suite passes. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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fc618dcee6 |
fix(distributed): track in-flight for SoundDetection requests (#10475)
The distributed router wraps backend clients in InFlightTrackingClient so the eviction logic knows which replicas are actively serving. Every inference method must be wrapped: track() increments in-flight on entry and decrements (plus fires onFirstComplete, which releases the load-time reservation) on return. SoundDetection was added after the tracking client and never got a wrapper, so its calls fell through to the embedded passthrough Backend. The increment/decrement never ran and, critically, onFirstComplete never fired, so the reservation set at model load was never released - leaving in-flight stuck at 1 and the replica permanently ineligible for eviction. Wrap SoundDetection like the other non-LLM methods and cover it in the "non-LLM inference methods track in-flight" table test. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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569d9bbd9e |
fix(distributed): broadcast file-staging progress across replicas (#10440)
File-staging progress lived only in the SmartRouter's in-memory
StagingTracker on the replica performing the transfer. In a multi-replica
deployment behind a round-robin load balancer, a /api/operations poll
that lands on any other replica saw no staging row, so the progress
("processing file ... Total ... Current ...") flickered in and out as
polls rotated between frontends.
Mirror the pattern already used for gallery-install progress: the origin
replica broadcasts staging ticks over NATS (SubjectStagingProgress, a
new staging.<model>.progress subject), and peers merge them via
ApplyRemote (SubscribeBroadcasts on the wildcard). Byte-level ticks are
leading-edge debounced (~1/s); Start/FileComplete/Complete always
publish. A locally-owned op stays authoritative so the origin's own echo
and stray peer events can't clobber it, and mirrored remote ops expire
after a TTL so a missed Done event can't leave a phantom row. The UI read
path (StagingTracker.GetAll) is unchanged.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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LocalAI [bot]
|
682fb2718c |
fix(distributed): detach cold-load staging from the request context (#10438)
A model not yet loaded on a worker is staged lazily on the inference request path. Staging a multi-GB model takes minutes - far longer than any client keeps its HTTP request open - so a browser refresh, an ingress/LB idle-timeout, or a round-robined retry landing on another frontend replica cancels the request context and aborts the upload with "context canceled" mid-transfer. Large models then never finish staging, so they never load (observed in a 2-replica deployment: both frontends repeatedly failed to stage a 15.7 GB GGUF, each attempt dying at a different offset). Bind the cold load (staging + LoadModel + the per-model advisory lock) to context.WithoutCancel(ctx): it keeps the request's values (prefix chain) but drops cancellation/deadline. Each long step keeps its own bound (the file stager's resume budget, LoadModel's 5m timeout), and the advisory lock still de-dupes concurrent loaders across replicas. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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600dafd20b |
feat(ced): sound-event classification backend (CED audio tagger) (#10425)
* feat(ced): sketch sound-classification backend (CED audio tagger) Wires ced.cpp (CED, 527-class AudioSet sound-event tagger; baby cry, footsteps, glass, alarms, dog bark) into LocalAI as a Go/purego backend. SKETCH (backend skeleton real; core REST wiring + CI/gallery is a checklist in DESIGN.md): - backend/backend.proto: new SoundDetection rpc + SoundClass messages (run `make protogen-go` to regenerate pkg/grpc/proto). - backend/go/ced: main.go (purego dlopen libced.so + ced_capi.h), goced.go (Ced gRPC backend: Load + SoundDetection), Makefile (clone-at-pin CED_VERSION, ggml static-PIC shared build), run.sh, package.sh, .gitignore. - DESIGN.md: REST /v1/audio/classification wiring (handler/route/capability registration checklist), gallery/index + CI registration, and a scoping note for the realtime/websocket live-recognition path (sliding-window classify over the existing ws transport + voicegate; the ced C-API per-PCM entry point is already window-friendly). Backend code does not compile until protogen-go regenerates the pb types and a libced.so is built (Makefile clones+builds it). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): REST /v1/audio/classification endpoint + capability registration Wires the ced sound-event classification backend (AudioSet audio tagger) end to end through the REST surface, mirroring the transcription path. - Handler: core/http/endpoints/openai/sound_classification.go parses the multipart audio upload, temp-files it, resolves the model config and calls the SoundDetection RPC; returns {model, detections[]} JSON. - Backend wrapper: core/backend/sound_classification.go (ModelSoundDetection) loads the model and normalizes the proto response into schema types. - Schema: core/schema/sound_classification.go (SoundClassificationResult). - gRPC layer: SoundDetection wired through the LocalAI wrapper (interface, Backend client, Client, embed, server, base default) so the loader-typed client exposes the RPC; proto regenerated via make protogen-go. - Route: POST /v1/audio/classification (+ /audio/classification alias) with the audio/multipart default-model middleware in routes/openai.go. - Capability surfaces: swagger @Tags/@Router on the handler; FLAG_SOUND_ CLASSIFICATION usecase flag + UsecaseSoundClassification + UsecaseInfoMap + GuessUsecases + ModalityGroups + GetAllModelConfigUsecases; meta usecase option; /api/instructions audio area updated; auth RouteFeatureRegistry + FeatureAudioClassification (APIFeatures, default ON) + FeatureMetas; UI usecaseFilters, capabilities.js CAP_SOUND_CLASSIFICATION, Models.jsx filter + i18n; docs page features/audio-classification.md + whats-new + crosslink. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): realtime sound-event detection over the websocket API When a realtime pipeline configures a sound-classification model, each VAD-committed utterance (the same window the transcription path produces) is also run through the CED sound-event classifier and the scored AudioSet tags are emitted as a new server event. No new backend rpc is needed: the SoundDetection gRPC method already exists on this branch. - config: add Pipeline.SoundDetection (yaml/json sound_detection,omitempty) beside Transcription/VAD. - realtime: add Model.SoundDetection(ctx, audio, topK, threshold) to the ModelInterface; implement it on wrappedModel and transcriptOnlyModel by calling backend.ModelSoundDetection with the session's sound-classification model config (mirrors how Transcribe dispatches). Load the optional config in newModel / newTranscriptionOnlyModel; nil config keeps it additive. - types: add ConversationItemSoundDetectionEvent (item_id, content_index, detections[]{label,score,index}) with type conversation.item.sound_detection, its ServerEventType constant and MarshalJSON, mirroring the transcription completed event. - realtime: add emitSoundDetection (unary path: classify the committed window, build the event, t.SendEvent) and wire it at the utterance-commit hook right after emitTranscription; gated on session.SoundDetectionEnabled (resolved from Pipeline.SoundDetection at session setup, defaults top_k=5, threshold=0). Its error is logged via xlog but never aborts the turn. - test: Ginkgo specs for emitSoundDetection (tags emitted, empty detections, classifier error) plus a SoundDetection method on the fakeModel double. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ced): implement SoundDetection in nodes backend test doubles The SoundDetection method added to the grpc backend interface left two test doubles (fakeBackendClient, fakeGRPCBackend) incomplete, so core/services/nodes failed to compile under `go vet`/`go test` (go build missed it: the doubles live in _test.go). Add the method to both, mirroring their existing Detect mock. Repairs CI for the nodes package. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): decouple realtime sound detection from VAD (sound-only sessions) Sound-event detection must activate on sounds, not speech, so it no longer runs through the voice VAD/transcription path. A sound-detection-only pipeline (sound_detection set, no transcription/LLM) now: - is accepted by prepareRealtimeConfig (sound_detection counts as a pipeline stage), - builds a lightweight model via newSoundDetectionOnlyModel (no VAD/STT/LLM/TTS loaded), and - defaults the session to turn_detection none (no VAD) with no transcription stage, so the client drives windowing via input_audio_buffer.commit (option A: client-side sliding window). The per-PCM C-API already supports arbitrary windows. commitUtterance gains a sound-only branch: it emits the conversation.item.sound_detection event (scored AudioSet tags) and stops - no transcription, no LLM response. generateResponse is now guarded on a transcription stage being present, so a sound-only turn never invokes the LLM. Existing transcription/VAD sessions are unchanged (additive). Added a commitUtterance sound-only Ginkgo spec asserting it emits the sound event and neither transcribes nor generates a response. go vet + golangci-lint (new-from-merge-base) clean; openai suite green. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): register sound-classification backend in gallery + CI Mechanical backend-image registration for the ced sound-event classifier, mirroring the parakeet-cpp Go/purego backend everywhere it is wired up. - .github/backend-matrix.yml: add the ced build matrix, field-for-field copies of the parakeet-cpp entries (cpu amd64/arm64, cublas cuda 12/13 amd64, l4t cuda-13 arm64, l4t-jetpack cuda-12 arm64, sycl f32/f16, vulkan amd64/arm64, rocm hipblas, and the metal darwin entry), changing only backend and tag-suffix. dockerfile stays ./backend/Dockerfile.golang. - backend/index.yaml: add the &ced meta anchor (capabilities map per platform) plus ced-development and the per-arch image entries, each uri/mirror tag-suffix matching the matrix exactly. The model gallery (GGUF) entry is intentionally deferred pending the HuggingFace publish (TODO note inline). - scripts/changed-backends.js: add an explicit item.backend === "ced" branch in inferBackendPath mapping to backend/go/ced/, same mechanism and ordering as the parakeet-cpp branch (before the generic golang fallthrough). - .github/workflows/bump_deps.yaml: register mudler/ced.cpp -> CED_VERSION in backend/go/ced/Makefile so the daily bot bumps the pin. - swagger/{docs.go,swagger.json,swagger.yaml}: regenerated via make swagger so the existing /v1/audio/classification annotations land in the generated spec. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): server-side windowing for realtime sound detection (option B) Adds an optional server-driven sliding-window classifier so a sound-only realtime client only has to stream audio (no input_audio_buffer.commit): - Pipeline.sound_detection_window_ms / sound_detection_hop_ms config knobs. When both > 0 on a sound-only session, the server classifies the last window of streamed audio every hop and emits a conversation.item.sound_ detection event; the input buffer is trimmed to one window so a long stream stays bounded. When unset, the session stays client-driven (option A). Runs independent of VAD (sound events are not speech). - handleSoundWindow (ticker) + classifySoundWindow (one tick, extracted so it is unit-testable) + writeWindowWAV, which declares the true InputSampleRate (NewWAVHeaderWithRate) so the classifier resamples correctly. Goroutine is started after toggleVAD and torn down with the session (close + wg.Wait). - Register pipeline.sound_detection (+window_ms/hop_ms) in the config meta registry; the earlier realtime commit added pipeline.sound_detection without a registry entry, failing TestAllFieldsHaveRegistryEntries. This fixes that and covers the two new knobs. Tests: classifySoundWindow emits an event + trims the buffer to one window, no-ops on too-little audio; writeWindowWAV declares the given sample rate. go build/vet + golangci-lint (new-from-merge-base) clean; config + openai suites green. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): add ced-base GGUF model gallery entries (f16 + q8_0) The ced-base weights are now published at mudler/ced-base-gguf (Apache-2.0, converted from mispeech/ced-base). Adds gallery/ced.yaml (backend: ced + known_usecases: sound_classification) and two gallery/index.yaml entries (ced-base-f16 default, ced-base-q8 smallest) with sha256-pinned files, and removes the now-resolved TODO from backend/index.yaml. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): add tiny/mini/small GGUF model gallery entries Publishes the rest of the CED family (same architecture, metadata-driven port verified end-to-end on ced-tiny) to mudler/ced-{tiny,mini,small}-gguf and adds their f16 + q8_0 gallery entries: ced-tiny (5.5M, edge/Pi-class) f16 11MB / q8_0 6MB ced-mini (9.6M) f16 19MB / q8_0 11MB ced-small (22M) f16 42MB / q8_0 23MB All sha256-pinned. ced-base remains the accuracy default. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(ced): point gallery entries at the consolidated mudler/ced-gguf repo All CED quantizations (tiny/mini/small/base, f16/q8_0) now live in a single HuggingFace repo, mudler/ced-gguf, instead of per-model repos. Repoint the 8 gallery model entries' urls + file uris accordingly. sha256 and filenames are unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(ced): bump CED_VERSION to the short-clip fix Pin the ced backend to ced.cpp 99c6ed3, which fixes a crash on any clip shorter than target_length (~10.11s): time_pos_embed was added at its full 63-frame grid instead of being sliced to the clip's actual time grid, tripping ggml_can_repeat in ggml_add. Surfaced by the live realtime e2e (sub-10s windows) and gated with a short-clip parity test upstream. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(ced): list ced.cpp as a LocalAI-team engine + backend-guide directive - README.md: add ced.cpp to the "native C/C++/GGML engines developed and maintained by the LocalAI project" table. - docs/content/features/backends.md: add a Sound Classification backend category (sound-event classification / audio tagging) listing ced.cpp. - .agents/adding-backends.md: add a "Documenting the backend" section and two verification-checklist items requiring new backends to be documented in the backends.md category list, and in-house native engines to be added to the README maintained-engines table. This directive was missing. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(ced): repin CED_VERSION to the v0.1.0 release commit ced.cpp history was squashed into a single release commit (tagged v0.1.0), so the previous pin (99c6ed3) no longer exists upstream. Pin to c04ac14, the v0.1.0 release commit, so the backend builds against a commit that exists. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ced): silence gosec G304/G103 + govet unsafeptr on audited paths - sound_classification.go: os.Create(dst) where dst = temp dir + path.Base of the upload (no traversal). #nosec G304, matching the depth-anything-cpp handler. - goced.go: reading a NUL-terminated C string from a libced-owned buffer. #nosec G103 (gosec) + //nolint:govet (golangci-lint's unsafeptr check), since the uintptr is a C-owned malloc'd buffer, not Go-GC memory. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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b081247d95 |
feat(config): hardware-tuned defaults — Blackwell batch + VRAM-scaled concurrency (#10411)
* feat(config): node-aware hardware defaults — larger physical batch on Blackwell A larger physical batch (n_batch/n_ubatch) materially lifts MoE prefill on NVIDIA Blackwell consumer GPUs (sm_120/121, incl. GB10 / DGX Spark) — measured on a GB10 with Qwen3-Coder-30B-A3B, the prefill ceiling rises (ub512 ~2994 -> ub2048 ~3316 t/s) and saturates around 2048. The heuristic lives in core/config alongside the other config overriders (ApplyInferenceDefaults, guessDefaultsFromFile/NGPULayers) — they all fill the ModelConfig from heuristics, so hardware tuning is the same domain and stays in one place. It is parameterized on a GPU descriptor (not direct detection) so it works in both deployment shapes: - Single host: SetDefaults applies it with the LocalGPU. - Distributed: only the worker sees the GPU, so the worker reports its compute capability on registration (gpu_compute_capability -> BackendNode), and the router re-applies the SAME core/config heuristic for the SELECTED node before loading — fixing the case where the frontend has no GPU at all. Explicit `batch:` always wins (only managed default values are touched). xsysinfo gains NVIDIAComputeCapability() (detection only); all interpretation lives in core/config. Tests: core/config, pkg/xsysinfo, core/services/nodes. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(config): injectable local-GPU seam + single-instance coverage Make local GPU detection an injectable package var (localGPU) so the single-instance path (SetDefaults -> ApplyHardwareDefaults) is deterministically testable without a real GPU, mirroring the distributed override's coverage. Adds specs asserting SetDefaults sets the Blackwell physical batch, leaves it unset on non-Blackwell, and never overrides an explicit batch. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(config): default concurrent serving (n_parallel) by GPU VRAM The llama.cpp backend defaults n_parallel=1, which serializes multi-user requests and leaves continuous batching off (it auto-enables only at n_parallel>1). Fold a VRAM-scaled parallel-slot default into the hardware-config path so multi-user serving works out of the box: >=32GiB->8, >=8GiB->4, >=4GiB->2, else unchanged. With the backend's unified KV the slots SHARE the context budget, so this adds concurrency without multiplying KV memory. Explicit parallel/n_parallel always wins. EnsureParallelOption is shared by the single-host path (ApplyHardwareDefaults with the local GPU) and the distributed router (per selected node's reported VRAM, since the frontend may have no GPU). LocalGPU now also reports VRAM. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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294170d3ed |
feat(backend): add depth-anything (Depth Anything 3) C++/ggml backend + gallery (#10352)
* feat(backend): add depth-anything (Depth Anything 3) C++/ggml backend + gallery Mirrors the locate-anything-cpp backend to register a new depth-anything backend that wraps the Depth Anything 3 ggml port (depth-anything.cpp) via purego (cgo-less, no Python at inference). - backend/go/depth-anything-cpp/: gRPC backend (Load + Predict + GenerateImage), purego binding to the da_capi_* C ABI, CMake/Makefile/run/package/test scripts building depth-anything.cpp's DA_SHARED static .so per CPU variant. - backend/index.yaml: depth-anything backend meta + all hardware-variant capability entries (cpu/cuda12/cuda13/intel-sycl-f32+f16/vulkan/nvidia-l4t). - gallery/index.yaml: 8 Depth Anything 3 GGUF models (base q4_k/q8_0/f16/f32, small, large, giant, mono-large). - .github/backend-matrix.yml: one build entry per hardware variant. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(depth): typed Depth RPC + REST endpoint exposing full DA3 data Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(depth): pin depth-anything.cpp to e0b6814 (ABI 3 dense C-API) The Depth RPC handler calls da_capi_depth_dense / da_capi_points (C-API ABI 3); pin the native build to the commit that exports them. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(depth): pin depth-anything.cpp to v0.1.0 release (b515c31) Repoint the native version from the now-orphaned e0b6814 to the b515c31 release commit, kept alive by the upstream v0.1.0 tag. C-API is unchanged (da_capi_abi_version == 3). Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(depth): wire depth-anything-cpp into build, CI bump, and importer The backend dir, gallery index, and CI build-matrix were present but the backend was never wired into the integration points that adding-backends.md requires: - root Makefile: add to .NOTPARALLEL, the test-extra chain, a BACKEND_* definition, the docker-build target eval, and docker-build-backends (mirrors parakeet-cpp; the backend's own Makefile already documented that its `test` target is driven by test-extra). - bump_deps.yaml: register the DEPTHANYTHING_VERSION pin so the daily auto-bump bot tracks mudler/depth-anything.cpp master (it cannot see an unregistered Makefile pin). - import form: add a preference-only KnownBackend entry so depth-anything is selectable at /import-model (mirrors sam3-cpp; no reliable GGUF auto-detect signal, so pref-only per the doc's default). changed-backends.js needs no entry: the generic golang suffix branch already resolves backend/go/depth-anything-cpp/. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(depth): auto-detect importer for depth-anything GGUFs Replace the preference-only entry with a real auto-detect importer (mirrors parakeet-cpp / locate-anything): - DepthAnythingImporter matches a .gguf whose name carries a depth-anything token (depth-anything-<size>-<quant>.gguf), so /import-model recognises mudler/depth-anything.cpp-gguf repos and direct GGUF URLs without an explicit backend preference. preferences.backend= "depth-anything" still forces it. - Registered before LlamaCPPImporter so its GGUF bundles aren't claimed by the generic .gguf importer; the narrow name match means it cannot claim arbitrary llama GGUFs or the upstream safetensors PyTorch repos. - Multi-quant repos pick the smallest quant by default (q4_k -> ... -> f32, depth stays >0.998 corr even at q4_k); quantizations preference overrides. - Drops the now-redundant knownPrefOnlyBackends entry (importer-backed backends are not listed there, matching parakeet-cpp). - Table-driven Ginkgo test covers detection, negative cases (llama GGUF, upstream safetensors), default/override/fallback quant pick, and direct URL import. 10/10 specs pass. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(depth): check conn.Close error in grpc Depth client (errcheck) The new Depth() client method used a bare `defer conn.Close()`. golangci-lint runs with new-from-merge-base, so although the 39 sibling methods use the same bare form (grandfathered), the newly added line trips errcheck. Drop the result explicitly to satisfy the linter. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 * fix(depth): bump depth-anything.cpp to v0.1.1 (embeddable CMake) v0.1.0 (b515c31) used ${CMAKE_SOURCE_DIR} for its include dirs, which points at the parent project when built via add_subdirectory() as this backend does, so the container build failed with missing stb_image.h / da_gguf_keys.h. v0.1.1 (2d42897) switches to project-relative paths. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 * fix(depth): resolve gosec findings in the backend wrapper The code-scanning gate flagged three new failure-level alerts in godepthanythingcpp.go (gosec runs with -no-fail; GitHub gates on new alerts): - G301: export dirs were created with 0o755. Tighten to 0o750 (no world access needed for backend-written export output). - G304: writeDepthPNG creates req.GetDst(). That path is chosen by the LocalAI core as the intended output destination (same pattern every image backend uses), not attacker input, so annotate with #nosec G304 and document why. The remaining G103 "audit unsafe" notes on the unsafe.Slice C-buffer copies are warning-level (the same purego interop whisper/parakeet use) and do not gate the check, per the supertonic exclusion precedent in secscan.yaml. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 * fix(depth): bump depth-anything.cpp to v0.1.2 (CUDA cross-build arch) v0.1.1 forced CMAKE_CUDA_ARCHITECTURES=native, which breaks the GPU-less l4t/cublas CI builds (nvcc "Unsupported gpu architecture 'compute_'" on CMake 3.22). v0.1.2 (442eea4) drops the override and lets ggml pick its default cross-build arch list. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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e5c95e0449 |
fix(distributed): stage backend companion assets to remote nodes (#10330)
A model whose ModelFile is a single file (e.g. sherpa-onnx VITS/piper: the .onnx) failed to load on remote worker nodes because the sibling assets the backend resolves from the model dir — tokens.txt, lexicon.txt, the espeak-ng-data / dict directories, Kokoro's voices.bin — were never staged. Only the declared ModelFile was shipped, so the worker hit "failed to create sherpa-onnx TTS engine" and TTS produced no audio. Lean on the existing option-path staging instead of hardcoding filenames: - stageGenericOptions now also resolves an option value relative to the model's own directory (not just the frontend models dir), so a shared config can declare companions with bare names regardless of whether Model includes a subdirectory; and it expands directory-valued options (e.g. espeak-ng-data) file-by-file rather than handing a directory fd to the stager. - gallery/sherpa-onnx-tts.yaml declares the companion assets as option paths (tokens, lexicon, espeak-ng-data, voices.bin, dict, per-lang lexicons). The backend ignores these keys and keeps resolving siblings from the model dir; they exist only so distributed staging ships them. Absent files are skipped. Adds router_optionstage_test.go covering file + directory companion staging via the model-dir fallback. Co-authored-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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LocalAI [bot]
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7637f8cf1b |
feat(distributed): declarative per-model scheduling via env/args (#10308)
* feat(distributed): add SpreadAll column and authoritative scheduling seeding Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): parse declarative model scheduling config (env/file) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): reconcile spread_all to one replica per matching node Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): wire LOCALAI_MODEL_SCHEDULING env/args and startup seeding Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): expose spread_all on the scheduling API endpoint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): add spread-to-all-nodes mode to the scheduling UI Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): document LOCALAI_MODEL_SCHEDULING env/args Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): clarify replica modes and all-nodes spread in scheduling config Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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fba8c9c498 |
fix(distributed): track in-flight for non-LLM inference methods (VAD, diarize, voice, ...) (#10238)
fix(distributed): track in-flight for non-LLM inference methods InFlightTrackingClient only wrapped a subset of the grpc.Backend inference methods (Predict, Embeddings, TTS, AudioTranscription, Detect, Rerank, ...). Methods like VAD were left as embedded passthrough, so track() never ran for them. In distributed mode every model is loaded with in_flight=1 as a reservation; that reservation is only released by the OnFirstComplete callback, which fires after the first *tracked* inference call completes. A VAD-only model (e.g. silero-vad) never calls a tracked method, so the reservation is never released and in-flight stays pinned at 1 forever - which also blocks the router's idle-eviction logic. Wrap the remaining unary inference methods (VAD, Diarize, Face*, Voice*, TokenClassify, Score, AudioEncode, AudioDecode, AudioTransform) with the same track()/reconcile() pattern. The three bidi-stream constructors (AudioTransformStream, AudioToAudioStream, Forward) are deliberately left as passthrough - their inference spans the stream lifetime, not the constructor call, so track() there would fire onFirstComplete before any data flows. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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92dea961c2 |
fix: distributed backend reinstall/upgrade UI stuck on 'reinstalling' (#10214)
* fix(galleryop): self-evict terminal ops from OpCache.GetStatus The processingBackends map (the UI 'reinstalling' spinner source) only cleared an op when a client polled /api/backends/job/:uid. The Manage-page Reinstall and Upgrade buttons never poll, so completed installs leaked into processingBackends forever and the backend card spun 'reinstalling' even though the install had finished. Evict terminal ops on the list read instead; DeleteUUID already broadcasts the eviction so peer replicas converge. Reproduced on a live 5-node distributed cluster: 5 backends sat in processingBackends with underlying jobs reporting completed:true,progress:100. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(nodes): clear pending backend ops behind offline/draining nodes ListDuePendingBackendOps filters status=healthy, so a backend op queued against a node that went offline (stale heartbeat) or draining (admin action) was never retried, aged out, or deleted - it leaked forever and kept the UI operation spinning. Add DeleteStalePendingBackendOps and run it each reconcile pass: draining nodes are cleared immediately (model rows already purged), offline nodes once their heartbeat is older than a grace window (blip protection). Reproduced on a live cluster: orphaned llama-cpp install rows targeting an offline (nvidia-thor) and a draining (mac-mini-m4) node sat at attempts=0 indefinitely. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(nodes): stream per-node progress during backend upgrade The install dispatch subscribed to a per-op progress subject and streamed per-node download ticks; the upgrade dispatch did a bare 15-minute blocking NATS round-trip with no subscription, so the UI showed progress:0 the whole time (the 'reinstalling but nothing happens' report on a slow node). Thread the op ID through BackendManager.UpgradeBackend -> the distributed manager -> the adapter, and have the adapter subscribe to the per-op progress subject before the request (extracted into a shared subscribeProgress helper reused by install/upgrade/force-fallback). The worker's upgradeBackend now creates the same DebouncedInstallProgressPublisher installBackend uses. An upgrade is a force-reinstall, so it reuses SubjectNodeBackendInstallProgress rather than minting a new subject - no new NATS permission, no new rolling-update compat surface. Reconciler-driven retries pass empty opID/onProgress and stay on the silent path. Reproduced on a live cluster: upgrade of llama-cpp-development on agx-orin-slow sat at progress:0 for 4+ minutes with no per-node feedback. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(galleryop): persist cancellation + periodically reap orphaned ops Two distributed gaps surfaced when a replica was killed mid-upgrade on a live cluster, leaving the backend stuck 'processing' in the UI forever: 1. CancelOperation flipped the in-memory status to cancelled and broadcast a NATS event but never persisted the terminal status. On the next replica restart the still-active row re-hydrated straight back into processingBackends and the UI spun again. It now calls store.Cancel(id) so the cancel survives a restart. 2. CleanStale (which marks abandoned active ops failed) only ran once on startup, so an op orphaned AFTER startup - its owning replica's foreground handler goroutine gone - was never reaped until the next restart. Add GalleryService.ReapStaleOperations and run it on a 15m ticker (CleanStale now returns the reaped count for observability). Neither is covered by the OpCache self-evict fix: an orphaned op never reaches Processed, so it would never self-evict. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(review): address self-review findings on the distributed install fixes Three findings from an adversarial review of this branch: 1. CRITICAL - OpCache.GetStatus crashed under concurrent load. m.Map() returns the live internal map by reference, so deleting from it on the read path was an unsynchronized write to a map four HTTP handlers poll every ~1s -> a 'concurrent map writes' fatal. Rewritten to iterate a Keys() snapshot, build a fresh result map, and apply evictions via the locked DeleteUUID after the loop. Added a -race concurrency regression guard. 2. HIGH - GetStatus evicted failed ops too, hiding them from /api/operations and breaking the dismiss-failed-op flow (the panel keeps Error != nil ops so the admin can read the error and click Dismiss). Eviction now fires only for terminal ops with Error == nil (success/cancelled); failures are retained. 3. MEDIUM - DeleteStalePendingBackendOps missed StatusUnhealthy nodes. A node marked unhealthy on a NATS ErrNoResponders never transitions to offline (health.go skips re-marking it), so its pending ops leaked exactly like the offline case. Unhealthy is now reaped via the same stale-heartbeat grace path (a fresh-heartbeat node is recovering and keeps its op). Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(review-2): don't evict the still-installing soft-path; don't spin on failed ops Second review pass found two issues: 1. MEDIUM (Go) - OpCache.GetStatus evicted the ErrWorkerStillInstalling soft-path op. That op is deliberately Processed=true with no error to show a yellow in-progress state when a worker timed out the NATS round-trip but is still installing in the background; the reconciler confirms the real outcome later. Evicting it (and broadcasting OpEnd + marking the DB completed) hid an install that may still fail. Eviction is now scoped to a clean success (progress 100 + 'completed', matching the job-poll's historical condition) or a cancellation - the soft-path (progress != 100) and failures are kept. 2. MEDIUM (React) - the Backends gallery card rendered ANY operation as an 'Installing...' spinner, so a failed op (now intentionally kept in the list for the OperationsBar error + Dismiss) spun forever. Exclude errored ops from the card spinner, mirroring Models.jsx (isInstalling already excludes op.error). The error + Dismiss still surface in the global OperationsBar. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ui): refresh Manage backends table when an operation settles The Manage backends table fetched installed backends only on mount/after delete and checked upgrades only on tab activation. After a reinstall/upgrade completed neither re-ran, so the installed-version cell and the 'update available' badge stayed stale until the user switched tabs - the op looked like it 'did nothing'. Watch the operations list (via useOperations) and re-fetch installed backends + available upgrades whenever the count settles, mirroring the operations.length watch Backends.jsx already uses. Consolidates the prior tab-activation upgrades check into the same effect. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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73385713ca |
feat(distributed): enforce registration token for worker file transfer (#10183)
The worker HTTP file-transfer server is authenticated by the registration token via checkBearerToken, which fails open on an empty token: every /v1/files, /v1/files-list and /v1/backend-logs request is then served unauthenticated, granting read/write to the worker's models/staging/data directories. The fail-open was also silent (the only auth log sat on the unreachable reject branch), and the worker process never runs DistributedConfig.Validate(), so the existing frontend warning did not cover the component that exposes the server. Mirror the NatsRequireAuth pattern: keep anonymous as the default but make it loud and opt-in enforceable. - Log a prominent warning when the file-transfer server starts tokenless. - Add LOCALAI_REGISTRATION_REQUIRE_AUTH: DistributedConfig.Validate() errors on an empty token (frontend) and the worker refuses to start (fail-fast, before registration), so production can fail closed. Also satisfies the F-003 suggestion to fail Validate() on distributed + empty token. - Add LOCALAI_DISTRIBUTED_REQUIRE_AUTH umbrella switch implying both RegistrationRequireAuth and NatsRequireAuth — one production knob locking down the registration/file-transfer layer and the NATS bus together; the granular flags remain available as single-layer overrides. Wired into the frontend, supervisor worker, and agent worker (vLLM worker has neither a NATS connection nor a file-transfer server, so it is left untouched). - Document in distributed-mode.md (warning callout + flag tables). Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]
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858257eaf0 |
fix(distributed): self-heal stale 'model not loaded' routing (#10181)
* fix(distributed): self-heal stale 'model not loaded' routing In distributed mode the registry can list a model as loaded on a node while the worker has evicted it (autonomous LRU eviction, an out-of-band unload, etc.) yet the backend process survives. The router's cached-node check only verifies the process is alive (probeHealth), so it routes there and inference fails with "<backend>: model not loaded" — and stays broken until the controller restarts and rebuilds its registry. InFlightTrackingClient now reconciles this: when a tracked inference call returns a model-not-loaded error, it drops the stale replica row (RemoveNodeModel) so the next request reloads the model on a healthy node instead of routing back to the evicted one. The original error is returned unchanged; only the registry is corrected. Assisted-by: Claude:claude-opus-4-8 go vet Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(distributed): typed model-not-loaded error via gRPC status code Replace the controller-side error-string match with a shared, code-aware helper. Go error types don't survive the gRPC boundary, so the signal is carried as a status code (FailedPrecondition): - pkg/grpc/grpcerrors: ModelNotLoaded(backend) constructor + IsModelNotLoaded(err) checker (status-code first, message fallback for backends not yet migrated). - InFlightTrackingClient.reconcile now uses grpcerrors.IsModelNotLoaded. - Migrate the Go backends that emit this error (parakeet-cpp, cloud-proxy, rfdetr-cpp) to the typed constructor. Acting on a false positive is harmless (the model is just reloaded). Assisted-by: Claude:claude-opus-4-8 go vet Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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92726f7631 |
fix(distributed): stage directory-based models to remote nodes (#10175)
Distributed file-staging treated every model path field (ModelFile, etc.) as a single regular file: it os.Open'd the path and streamed its fd as the HTTP PUT body. For directory-based models — e.g. qwen3-tts-cpp, whose weights and tokenizer ggufs live under one directory referenced by parameters.model — opening the directory succeeds but reading its fd returns EISDIR, so routing the model to a remote NATS worker failed with "read /models/<model>: is a directory". Single-file models were unaffected, so only multi-file pipelines (e.g. the realtime TTS stage) broke. stageModelFiles now detects a directory path field and stages each contained file individually (via the new stageDirectory helper), preserving structure with the existing StagingKeyMapper and rewriting the field to the remote directory (deriving ModelPath as before). countStageableFiles makes the progress total count a directory's files so the staging tracker stays accurate. Assisted-by: Claude:claude-opus-4-8 go vet Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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c01ed631d6 |
refactor(routing): extract replica picker into pkg/clusterrouting (#10123)
Move ReplicaCandidate and PickBestReplica out of core/services/nodes (which depends on gorm) into a new dependency-light leaf package pkg/clusterrouting, so the p2p federation server can later share the same replica-selection policy without pulling in a database driver. core/services/nodes keeps a type alias and a thin delegator, so every existing reference (the LoadedReplicaStats interface method, the ReplicaCandidate row conversion in registry.go, and the SQL policy-mirror test) compiles and behaves unchanged. This is a pure, behavior-preserving refactor: the full nodes suite, including the policy-mirror spec that pins the SQL ORDER BY to PickBestReplica, stays green. Assisted-by: Claude Code:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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c222161291 |
feat(distributed): resumable file uploads via HTTP Content-Range (#10109)
Large model GGUFs (multi-GB) transferred between master and worker over
flaky / bandwidth-throttled paths (e.g. libp2p relays with byte caps) used
to restart from byte 0 on every transport error. This change adds standard
HTTP Range/resume semantics to the worker's PUT /v1/files/<key> endpoint
and teaches the master-side HTTPFileStager to consult the worker for the
last accepted offset and resume from there.
Server side (file_transfer_server.go):
- PUT now honors Content-Range: bytes <start>-<end>/<total>. The handler
validates that <start> matches the current on-disk size; mismatches
return 416 with the actual size in X-File-Size.
- Mid-upload chunks return 308 Permanent Redirect ("Resume Incomplete")
with the new size, so the client can keep going.
- An optional X-Content-SHA256 request header binds an upload to a target
hash; cross-attempt drift returns 409. On the final chunk the server
re-computes SHA-256 and returns 400 if it doesn't match.
- HEAD now advertises Accept-Ranges: bytes and Content-Length, and exposes
X-Target-SHA256 for in-progress files (so clients can resume only when
the partial bytes belong to the file they want to upload).
- Legacy PUTs with no Content-Range keep the original truncate-create
semantics — zero behavior change on the happy path.
Client side (file_stager_http.go):
- Pre-PUT HEAD probe reads X-File-Size + X-Target-SHA256 to determine the
resume offset.
- doUpload seeks to that offset and sends Content-Range + X-Content-SHA256.
- Retry loop switches from fixed 3 attempts / 5s-10s-20s backoff to an
outer time budget
with exponential backoff (1s -> 30s cap), so a 5GB upload over a flaky
link can outlast many short disconnects.
- 308 and 416 responses are treated as transient: the next iteration
re-HEADs to learn the correct offset.
Tests:
- Two-chunk Content-Range round-trip produces the correct file + sidecar.
- 416 on a Content-Range/file-size mismatch.
- 409 on X-Content-SHA256 drift between chunks.
- 400 on final-hash mismatch.
- HEAD on a partial upload exposes X-Target-SHA256 (not a misleading
hash-of-partial-bytes via X-Content-SHA256).
- Pre-existing finished file with a different hash is transparently
overwritten when a new PUT starts at byte 0.
- End-to-end resume: EnsureRemote against a worker that already holds a
partial file transfers only the remainder.
- Mid-stream connection drop on attempt #1 is recovered by attempt #2
resuming from the partial offset.
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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LocalAI [bot]
|
a44bdb29d4 |
feat: prefix-cache-aware routing for distributed mode (#10071)
* feat(radixtree): generic prefix tree skeleton with longest-match Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(radixtree): Insert with path recency refresh and entry cap Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(radixtree): TTL idle-expiry and Evict sweep with branch pruning Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(radixtree): recency-weighted per-value Weight Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(radixtree): Remove all entries for a value Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(radixtree): race-free concurrency smoke test Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(radixtree): reclaim empty branches, RWMutex reads, TTL boundary, empty-key guard Address review findings on the generic prefix tree: - Extract a shared pruneWalk helper parameterized by a shouldClear predicate and use it from Evict, Remove, and the MaxEntries path. Previously evictOldestLocked cleared a victim's value but never removed the now value-less node or its childless ancestors, so internal nodes accumulated under sustained churn at the cap. The MaxEntries path now prunes the victim and its empty ancestors. - DRY: pruneWalk replaces the duplicated logic in the former pruneLocked and Remove's inner closure. - Switch Tree.mu to sync.RWMutex; LongestMatch, Weight and Len take the read lock (RLock) while Insert, Evict and Remove keep the write lock. Confirmed race-clean under go test -race. - Document the strict greater-than TTL boundary on Options.TTL and expired: age exactly equal to TTL is still live. - Guard Insert against an empty key (no-op): the root never holds a value. Adds Ginkgo specs covering MaxEntries eviction, ancestor reclamation, the no-growth-past-cap invariant, the TTL boundary, and empty-key behavior for both Insert and LongestMatch. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(prefixcache): RoutePolicy enum with parse/resolve Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(prefixcache): Config with defaults and validation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(prefixcache): deterministic xxhash prefix-chain extractor Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(prefixcache): pure filter-then-score replica selection Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(prefixcache): Provider interface and radix-tree-backed Index Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(prefixcache): gofmt policy enum comment alignment Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(prefixcache): head-first prefix chunking and hoist Weight out of sort Address code-quality review findings in the prefixcache package. Correctness: ExtractChain now chunks from absolute offset 0 with fixed [0,W),[W,2W),... boundaries and caps the chain to the FIRST MaxDepth head blocks. The previous tail-keeping logic shifted the byte offset by a non-window amount once a conversation grew past MaxDepth*WindowBytes, changing every hash each turn and silently breaking cross-turn longest-prefix matching. The reusable KV/prefix cache lives at the head of the prompt, so anchoring at offset 0 makes the chain a true prefix-chain: P and P+suffix share their full leading overlap. Add a regression spec proving cross-turn stability past the cap. Performance: Index.Decide precomputes each candidate's Weight once (decorate-sort-undecorate) instead of calling the O(tree size) Weight inside the O(n log n) sort comparator. Behavior is unchanged. Lint: encode prev with binary.LittleEndian.PutUint64 instead of a manual byte loop, clearing the modernize rangeint finding. Also add a concurrent Decide/Observe/Invalidate spec to exercise Index's documented concurrency safety under go test -race. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(messaging): prefixcache observe/invalidate subjects and payloads Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(prefixcache): NATS sync publish/apply for observe and invalidate Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributedhdr): ctx carrier for prefix-hash chain Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributedhdr): PrefixChainHook indirection for backend-side chain build Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(backend): stash prompt prefix chain on ctx before distributed routing Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(backend): mirror modelID fallback for prefix-chain salt parity Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(nodes): scheduling config columns for prefix-cache routing Add RoutePolicy and per-model balance/prefix-match override columns to ModelSchedulingConfig and include them in the SetModelScheduling upsert DoUpdates list so updates are not dropped on conflict. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(nodes): optional route preference in FindAndLockNodeWithModel Add a RoutePreference type and a new pref parameter so the atomic pick+lock+increment can be biased toward a preferred node without weakening atomicity. A nil preference reproduces the previous ORDER BY behavior exactly. Update the ModelRouter interface, both router.go call sites (pass nil for now; Phase 5 builds the real preference), the test doubles, and the distributed e2e caller. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(prefixcache): make Sync satisfy Provider with Evict Sync.Observe now returns whether the local index treated the assignment as new or extended, and Sync gains an Evict method that delegates to the wrapped index. Together these let SmartRouter hold a single prefixcache.Provider that broadcasts via NATS. Adds a compile-time Provider assertion and an Evict-delegates behavioral test. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(nodes): prefix-cache-aware preference and observe in SmartRouter.Route Add a PrefixProvider + PrefixConfig to SmartRouterOptions/SmartRouter (nil keeps routing byte-for-byte the round-robin floor). On each request Route now calls buildPreference: it reads the prompt prefix chain from ctx (distributedhdr.PrefixChain), resolves the per-model policy/thresholds over the global config, loads candidate replica in-flight via a new registry read LoadedReplicaStats (deduped to one entry per node using the MIN in-flight across that node's replicas), asks the provider to Decide, and runs prefixcache.Select. The chosen node is passed as the RoutePreference to FindAndLockNodeWithModel on all three pick paths (cache hit, locked re-pick, cold scheduleAndLoad), and the served node is recorded via Observe only when the resolved policy is prefix_cache so round-robin models never pollute the tree. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(nodes): invalidate prefix-cache entries on unload and stale removal UnloadModel and both staleness fall-through paths in Route (after a failed gRPC probe and RemoveNodeModel) now call prefixProvider.Invalidate(model, nodeID), guarded by a nil-provider check so the round-robin floor is unchanged. At runtime the provider is the *prefixcache.Sync, so invalidations also broadcast to peer frontends. Adds a test that a previously hot prefix no longer Decides to a node after UnloadModel. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(prefixcache): rolling forced-disturb pressure counter Add a concurrency-safe per-model rolling counter that tracks how many times a request had a usable hot prefix match but the load guard forced it off the warm node. Entries outside the window are dropped lazily on Count so the backing slice stays bounded. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(nodes): autoscale on prefix-cache forced-disturb pressure Wire the rolling forced-disturb counter into the SmartRouter and the ReplicaReconciler. Router: in buildPreference, after Decide + Select, record a forced-disturb when a usable hot prefix match existed (d.HotNodeID != "" and d.MatchRatio >= cfg.MinPrefixMatch) but Select chose a different node (or nothing) because the load guard ruled the warm node out. This is the scale-worthy signal: the cache-warm replica is saturated. It deliberately does not fire for all-unique workloads (no hot match), avoiding false-positive scale-ups. Pressure is optional on SmartRouterOptions; nil keeps the path a no-op. Reconciler: read the same Pressure instance in reconcileModel as an extra scale-up reason, reusing the existing MaxReplicas + ClusterCapacityForModel guards and the UnsatisfiableUntil cooldown that gates the whole method. Pressure never overrides MaxReplicas and never force-evicts; a no-capacity model does not spin. Window and threshold come from prefixcache.Config (PressureWindow default 1m, PressureScaleThreshold default 1) and are configurable via ReplicaReconcilerOptions. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(prefixcache): bound Pressure slice in Record; drop dead reconciler pressureWindow Record now prunes entries older than the rolling window (the same prune Count does), via a shared pruneLocked helper, so a model that takes forced-disturb records but is never Counted (e.g. one with zero loaded replicas the reconciler skips) no longer grows its backing slice unbounded. Also removes the dead pressureWindow struct field and the ReplicaReconcilerOptions.PressureWindow option from the reconciler: they were stored but never read (the window lives inside the *prefixcache.Pressure instance). The scale block now reads pressure.Count once into a local. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(api): prefix-cache fields in scheduling endpoint DTO with validation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): prefix-cache routing controls in node scheduling form Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): wire prefix-cache index, NATS sync, and config Activates prefix-cache-aware routing in distributed mode. Builds the prefixcache Index + NATS-backed Sync + Pressure counter, installs the distributedhdr.PrefixChainHook so core/backend/llm.go attaches a prefix chain per request, subscribes to prefixcache.observe/prefixcache.invalidate to apply peers' events to the local index (no re-broadcast), threads PrefixProvider/PrefixConfig/Pressure into the SmartRouter and Pressure/PressureThreshold into the ReplicaReconciler, and runs a background eviction ticker (every TTL/2) bound to the app context. Enabled by default; --distributed-prefix-cache=false (LOCALAI_DISTRIBUTED_PREFIX_CACHE) opts out and leaves the provider/pressure nil so routing stays round-robin. --distributed-prefix-cache-ttl (LOCALAI_DISTRIBUTED_PREFIX_CACHE_TTL, default 5m) controls entry idle-timeout and eviction cadence. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(nodes): round-robin-floor invariant for prefix-cache routing Drives Select directly: a saturated hot node (in_flight 50 vs 0) is never picked even with a perfect prefix match (round-robin floor holds), while a balanced hot node within the load slack is reused. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(prefixcache): clear branch lint findings and em dashes Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): validate prefix-cache config at startup wiring Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * perf(radixtree): single-walk WeightsFor for batch value weights Add Tree.WeightsFor(values, now) which computes the recency-weighted weight for many values in a single O(N + len(values)) tree traversal, versus calling Weight once per value (O(len(values) * N)). Consumers that score K candidates against the tree under the read lock no longer pay K full walks. Extract the per-entry contribution math into an unexported helper shared by both Weight and WeightsFor so the metric stays identical (DRY). Weight's public behavior is unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(config): add ModelConfig.ModelID() single source of truth The c.Name fallback to c.Model was duplicated in core/backend/options.go (feeding model.WithModelID) and hand-copied into core/backend/llm.go (the prefix-chain salt). These MUST agree or the prefix-cache salt diverges silently from the id the model loader tracks. Consolidate both into a new config.ModelConfig.ModelID() helper and call it from both sites. Behavior is identical. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * perf(prefixcache): reuse one xxhash.Digest in ExtractChain ExtractChain allocated a fresh xxhash.New() Digest per block (up to MaxDepth per call) and grew the chain slice without preallocation. Reuse a single Digest via Reset() before each block and preallocate the chain to min(nBlocks, MaxDepth). xxhash seed 0 is stateless, so Reset()+Write produces the byte-identical value to a fresh New()+Write. Output hashes are unchanged, preserving the cross-process determinism that peers rely on over NATS. Verified by capturing ExtractChain output for the existing test inputs before and after the refactor: identical. Existing extractor tests pass unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(prefixcache): drop hot match when matched node is not a candidate; weigh cold candidates in one walk Index.Decide called radixtree.LongestMatch over the whole tree, so the deepest match could be a node that is offline, unloaded, or simply not in the passed candidate set. Honoring that as HotNodeID produced a false forced-disturb signal upstream (buildPreference records pressure when chosen != HotNodeID), making it look like a warm replica was load saturated when it was actually absent. Build the candidate set once and only set HotNodeID/MatchRatio when the matched node is an actual candidate; otherwise fall back to cold placement. A future refinement could ask the tree for the longest match restricted to the candidate nodes (shallower-but-valid) instead of dropping it. Also replace the per-candidate tree.Weight call in the cold-order sort with a single tree.WeightsFor walk, turning O(K*N) under the read lock into O(N + K). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(prefixcache): remove Select's unreachable deterministic fallback buildPreference always passes ColdOrder as a permutation of the full candidate set, so the cold-order loop hits every eligible candidate. The trailing best/bestIF scan was dead. Replace it with a plain "return """ and document that ColdOrder is guaranteed to cover all candidates, so "" means none were eligible. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(nodes): fetch model scheduling config once per Route GetModelScheduling was read three times per request - in resolveSelectorCandidates, buildPreference, and nodeMatchesScheduling - three DB round-trips for one row that is immutable for the life of the request, and not a consistent snapshot. Fetch it once near the top of Route and thread the *ModelSchedulingConfig (may be nil) into all three helpers. scheduleNewModel keeps its own fetch since it runs outside the Route snapshot. Behavior is identical for nil sched. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(autoscale): add Pressure.Reset to consume forced-disturb signal Pressure.Count is non-draining (it prunes only by age), so a single burst of forced-disturbs stays within the rolling window for the whole window and keeps Count >= threshold on every reconciler tick. The reconciler will use Reset to clear a model's events after acting on the signal so a fresh scale-up requires fresh forced-disturbs to accumulate, rather than one burst driving the model toward MaxReplicas. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(autoscale): at most one scale-up per reconcile tick, consume pressure Two autoscale bugs: 1. Over-scaling: the pressure scale-up block read Pressure.Count but never consumed it. With a non-draining counter a single forced-disturb burst kept Count >= threshold across the whole window, firing scaleUp on every tick and pushing the model toward MaxReplicas off one transient burst. After a successful pressure-triggered scale-up the reconciler now calls Pressure.Reset to consume the signal. 2. Double scale-up in one tick: the all-replicas-busy block and the pressure block could both fire in the same reconcileModel pass, each calling scaleUp(+1) against the same `current` read once at the top, so a model that was both busy and over threshold scaled +2 and could overshoot MaxReplicas by one. A scaledUp flag now enforces at most one scaleUp(+1) per tick: the pressure block is skipped if the busy block already scaled, and scale-down is skipped in any tick that scaled up. MinReplicas enforcement, UnsatisfiableUntil backoff, and capacity guards are unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(nodes): replica-removed chokepoint hook for prefix-cache invalidation Add SetReplicaRemovedHook to NodeRegistry and fire it from both RemoveNodeModel and RemoveAllNodeModelReplicas after a successful delete. This is the single chokepoint every replica-removal path funnels through (router eviction, reconciler scale-down, probe reaper, health-monitor node-down reap, RemoteUnloaderAdapter), so the prefix-cache index can be invalidated by construction rather than wiring each call site individually. The hook is stored in an atomic.Pointer so the startup wiring (setter) and the request/reconcile-time fire are race-free; it is nil-safe when unset. GORM Delete reports no error for a no-op delete, so the hook also fires when nothing was removed; the consumer's Invalidate(model, node) is idempotent so this is harmless. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): invalidate prefix-cache on any replica removal via registry hook Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(prefixcache): single source of truth for threshold bounds Extract ValidateThresholds into prefixcache/config.go so the per-model override validation (nodes.go endpoint) and Config.Validate share one implementation of the numeric bounds (min_prefix_match in [0,1], balance_abs_threshold >= 0, balance_rel_threshold == 0-or->= 1) instead of hard-coding them in two places. The route_policy allow-list stays explicit (not ParsePolicy, which maps typos to Default). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(nodes): preserve prefix-cache settings on partial scheduling update A scheduling POST that omitted route_policy/thresholds (e.g. a min_replicas-only update) full-replaced every column and silently reset the model's previously-configured prefix-cache settings to empty/zero. Make the four prefix-cache request fields pointers so omitted is distinguishable from explicit zero, and merge PATCH-style in SetSchedulingEndpoint: a provided pointer wins, an omitted one preserves the existing config value (zero default when none). Non-prefix fields keep their full-replace PUT semantics. Validation now runs on the resolved values via prefixcache.ValidateThresholds. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(prefixcache): make Invalidate a no-op for uncached models and skip empty broadcasts A registry chokepoint fires Sync.Invalidate(model, nodeID) for every replica removal of every model, including round-robin models that never used the prefix cache. Index.Invalidate previously called tree(model), which lazily created and permanently retained an empty radix tree for any model that ever lost a replica, growing the trees map without bound. Sync.Invalidate also published a NATS PrefixCacheInvalidateEvent on every call, amplifying no-op removals across the cluster. Index.Invalidate now looks the tree up read-only via existingTree and returns without allocating when none exists. The Provider interface is unchanged; Sync gates the broadcast through an optional invalidateExisting(bool) capability type-asserted from the wrapped Index, falling back to the prior always-broadcast behavior for other Provider implementations. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * perf(prefixcache): derive Decide candidacy from WeightsFor and skip trivial sort WeightsFor already returns a map keyed by every requested candidate, so the separate candidates set built to validate the hot match was redundant: a node is a candidate iff it is a key in the weights map. Drop the extra map and gate the hot-match check on weights membership. Also skip the sort when there is at most one candidate, since the input order is already the cold order. Behavior is unchanged. Deferred follow-up: skipping the WeightsFor walk entirely when a hot match wins would need lazy cross-file changes and is out of scope here. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(nodes): fire replica-removed hook on bulk node_models deletes; trim LoadedReplicaStats columns Bulk node-scoped node_models deletes (Register re-register cleanup, MarkOffline, MarkDraining, Deregister) removed rows directly without firing the replica-removed hook, so the prefix-cache index kept pointing at nodes whose models were gone. Capture the DISTINCT model names before each bulk delete and fire fireReplicaRemoved once per model after a successful delete, restoring the single-chokepoint invariant for all removal paths. The pre-query is skipped when no hook is set so the no-hook path stays cheap. Also narrow LoadedReplicaStats to SELECT only node_id and in_flight (the only fields the router consumer reads), dropping the JOIN-side available_vram fetch and unused columns while keeping the []ReplicaCandidate return type unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(reconciler): consume autoscale signals only on a real scale-up scaleUp was fire-and-forget (void) yet its callers unconditionally consumed the pressure signal (Pressure.Reset) and the MinReplicas hysteresis (ClearUnsatisfiable) right after calling it. If scaleUp added nothing (ScheduleAndLoadModel errored, or no node could be loaded) the saturated warm replica got no new replica AND its accumulated forced-disturb history was wiped, forcing the signal to re-accumulate over a full PressureWindow before the next attempt. Make scaleUp return whether at least one replica was actually scheduled, and gate the side effects on it: - pressure block (2b): set scaledUp and call Pressure.Reset only on success; on failure preserve the signal so the next tick retries off the same accumulated pressure. - busy-burst block (2): set scaledUp from the return value so a failed attempt does not suppress the pressure path or scale-down. - MinReplicas block: call ClearUnsatisfiable only on success so a failed attempt does not reset the unsatisfiable counter. All existing invariants (MaxReplicas, capacity gating, UnsatisfiableUntil cooldown, at-most-one-scale-up-per-tick) are preserved. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(nodes): drop router's redundant prefix-cache Invalidate calls The NodeRegistry removal chokepoint (RemoveNodeModel / RemoveAllNodeModelReplicas) now fires SetReplicaRemovedHook, which invalidates the prefix-cache index. The router was also calling prefixProvider.Invalidate explicitly right after each registry removal on the two stale-replica health-probe fall-throughs in Route and in UnloadModel, so every router-side eviction invalidated twice (double tree-prune + double NATS broadcast). Remove the three redundant explicit Invalidate calls and their empty nil-guards. Each removed call sat immediately after a registry removal that fires the hook, so invalidation is preserved via the chokepoint. Decide/Observe usage is untouched. Re-point the unit test (fake registry fires no hook) to assert the removal chokepoint is exercised on unload instead of the router's direct invalidation. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(prefixcache): broadcast invalidations unconditionally for cross-frontend coherence Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(prefixcache): reject TTL<=0 in Config.Validate (eviction ticker would panic) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(nodes): make capture+delete atomic in bulk node_models removal paths MarkOffline, MarkDraining, and the Register re-register cleanup ran the nodeModelNames SELECT and the bulk node_models DELETE as two separate statements on r.db with no transaction. A SetNodeModel landing between the two was deleted but its replica-removed hook never fired, leaving the prefix-cache index pointing at a removed replica until TTL or candidacy self-heal. Wrap the capture and the delete in a single db.Transaction in each path (mirroring how Deregister already does it). The captured model names are collected into a slice declared outside the closure; the replica-removed hook fires for each only after the transaction commits, so a rollback never invalidates the index for a removal that did not persist. The set of fired hooks now equals exactly the set of node_models rows actually deleted, with no interleaving gap. The status flip in MarkOffline/MarkDraining (setStatus) is a separate, pre-existing operation and routing already filters non-healthy nodes, so it stays outside the transaction; return contracts are unchanged. Deregister was already correct and is untouched. The cheap-path skip (no hook -> skip the SELECT) is preserved. Adds a spec asserting MarkOffline fires hooks for exactly the rows it deletes and leaves no node_models row behind (consistent snapshot). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(nodes): debug logging for prefix-cache routing decisions and observations Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(radixtree): match shared prefixes by valuing every node on insert Insert recorded the value (node id) only on the final node of the key chain, leaving every intermediate prefix node valueless. LongestMatch returns the deepest node that hasValue, so two chains that share a leading block but diverge in the tail never matched: only exact-repeat queries hit. That broke the prefix-cache routing core use cases (shared system prompt, multi-turn extension, volatile tail), all of which rely on prefix matching rather than exact-repeat. Set value/hasValue/lastSeen at every node along the chain so each prefix-block node remembers the node id that served that prefix (SGLang/vLLM-style). The deepest match wins, and the last writer owns a shared prefix node (a recency heuristic: the most recent chain through a block is the one most likely still warm). size now counts valued nodes, which is the intended meaning. Updated radixtree tests to the new semantics: deepest-prefix test uses non-overlapping chains, a new test asserts last-writer-owns-shared-node, Evict/Remove/MaxEntries expectations recomputed for per-prefix-node counting, and a shared-prefix LongestMatch red test added. Added a prefixcache Decide test proving a prefix-only query routes to the warm node. No prefixcache .go logic changed. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(distributed): lock in prefix-cache routing behavior end to end Add a DB-backed e2e spec that drives SmartRouter against a real NodeRegistry (Postgres testcontainer) and the real prefixcache.Index radix-tree provider, using a fake gRPC backend factory so no real inference runs. Covers the five behaviors validated by hand: 1. Cold miss + observe: an unseen prefix chain cold-places and is recorded. 2. Hot-match affinity: the same chain returns to its warm node X. 3. Shared-prefix match: a divergent chain sharing X's leading prefix still routes to X (the radix-tree regression we fixed). 4. Negative control: an unrelated chain is a cold miss, not a false hot match on X. 5. Failover + invalidation: removing X's replica fires the registry chokepoint hook to invalidate the prefix entry, and the chain fails over to surviving node Y and re-homes there. Replaces the need for manual docker-compose re-runs. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(prefixcache): make prefix-cache affinity replica-granular Track prefix-cache affinity per loaded replica (a backend process with its own KV cache) instead of per node, so multiple replicas of the same model on one node each keep distinct affinity and a hot prefix routes back to the exact replica that served it. - radixtree: add RemoveFunc(pred) and reimplement Remove on top of it. - prefixcache: introduce ReplicaKey{NodeID, Replica}; Index/Candidate/ PrefixDecision/Select/Provider now key on ReplicaKey. Add InvalidateNode to drop every replica of a node; Invalidate drops one replica. Select returns (ReplicaKey, bool) and gains a deterministic least-in-flight eligible fallback (tiebreak NodeID then Replica). - messaging: carry Replica on PrefixCacheObserveEvent and PrefixCacheInvalidateEvent (Replica < 0 means all replicas of the node). - Sync delegates + broadcasts with replica; InvalidateNode broadcasts Replica=-1; ApplyInvalidate routes negative replica to InvalidateNode. This is part 1 of 2; the registry/router/wiring consumers are updated separately. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): make prefix-cache routing replica-granular Wire the SmartRouter, NodeRegistry, and distributed startup to the replica-keyed prefixcache API. Affinity is now tracked per replica (each replica is a separate process with its own KV cache), so a prefix served by (node,0) no longer leaks onto the same-node sibling (node,1). - RoutePreference gains PreferredReplica; FindAndLockNodeWithModel locks the EXACT (node_id, replica_index) row, falling through to the default ORDER BY when that replica is not loaded. - SetReplicaRemovedHook now carries replicaIndex; RemoveNodeModel fires the specific replica, RemoveAllNodeModelReplicas and the four bulk node-scoped deletes fire replica<0 (all replicas of the node). - buildPreference builds one Candidate per loaded replica and locks the exact replica the policy chose; observePrefix records the served ReplicaKey at every call site. - distributed.go routes the hook to InvalidateNode (replica<0) or Invalidate(key). - Tests updated to the replica-keyed API plus new coverage: a hot prefix on (node,0) prefers replica 0 over the same-node sibling (router unit + e2e), and FindAndLock locks the exact preferred replica. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): derive prefix chain from messages for tokenizer-template models Prefix-cache-aware routing built its prompt-prefix chain from the rendered prompt string `s` in ModelInference. For models with TemplateConfig.UseTokenizerTemplate the frontend never renders a prompt - the backend tokenizes the structured messages itself - so `s` is empty, the chain is empty, and routing silently falls back to round-robin. That covers the bulk of modern chat models (qwen3, llama3, ...), so the feature effectively never engaged for them. Fall back to messagesPrefixSource(messages): a deterministic, prefix-stable head-first serialization of the conversation (role + content per turn). Two requests sharing a leading system prompt and early turns share a leading byte prefix, which ExtractChain maps to a shared chain prefix - landing both on the same cache-warm replica. The rendered `s` is still preferred when present (higher fidelity for non-template models). Found via the multi-replica-per-node e2e: zero "prefix-cache routing decision" logs despite per-request Route calls, traced to the empty-chain guard. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): document prefix-cache routing roadmap Add a routing-and-caching roadmap section to the distributed-mode guide, linking the epic (#10063) and the follow-up issues (#10064-#10070) surfaced from a survey of SGLang, vLLM production-stack, Ray Serve, llm-d, AIBrix, and NVIDIA Dynamo. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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12d1f3a697 |
security(http): refuse redirects on outbound clients via hardened pkg/httpclient (#10087)
LocalAI's outbound HTTP clients used Go's default redirect policy, which
follows up to 10 redirects. On a cross-host redirect Go forwards custom
request headers — including credential headers such as Anthropic's
x-api-key — to the redirect target (Go strips Authorization, Cookie and
WWW-Authenticate cross-host, but NOT arbitrary custom headers). An
attacker able to elicit a redirect from an upstream (a hijacked or
spoofed upstream, DNS trickery, or a malicious upstream_url) then
harvests the operator's provider API key.
This was first reported against the cloud-proxy / MITM PII path
(GHSA-3mj3-57v2-4636); the same class affects every other outbound
client. Rather than patch each call site, add pkg/httpclient as the one
sanctioned constructor for outbound HTTP and route everything through it.
pkg/httpclient:
- New(...) refuses redirects, TLS 1.2 floor, no body
deadline (streaming/SSE safe)
- NewWithTimeout(d) simple request/response calls
- WithFollowRedirects opt-in following that still strips credential
headers on any cross-host hop; different
scheme/host/port == different origin, guarding
the curl CVE-2022-27774 port-confusion class
- WithTransport(rt) keep a custom transport (IP-pin, HTTP/2, a
credential-injecting RoundTripper)
- HardenedTransport() base transport with the TLS floor + bounded setup
- Harden(c) apply the policy to a library-supplied *http.Client
- NoRedirect the CheckRedirect policy; wraps ErrRedirectBlocked
Lint: a forbidigo rule flags http.DefaultClient and http.Get/Post/
PostForm/Head, pointing at pkg/httpclient (.golangci.yml,
.agents/coding-style.md). forbidigo cannot match the &http.Client{}
composite literal without also flagging legitimate *http.Client type
references, so that form is enforced by review.
Migrates every non-test outbound call site across core/, pkg/, cmd/, and
the Go backend (backend/go/cloud-proxy). Credential-bearing and
internal-RPC clients refuse redirects; download / CDN / registry clients
use WithFollowRedirects so they keep working while stripping secrets
cross-host. The only credential-bearing client that follows redirects is
the gated-download path (pkg/downloader/uri.go), which strips the token
on the cross-host hop to the CDN. Hardening this closes, in passing:
- MCP remote-server bearer token leaking via a redirect (the
RoundTripper re-injected Authorization on every hop)
- agent multimedia/webhook clients leaking user-supplied auth headers
- cors_proxy following redirects, bypassing its SSRF IP-pin
- downloader's authorized read path leaking the token cross-host
Fixes: GHSA-3mj3-57v2-4636 (cloud-proxy leaks operator provider API key
(x-api-key) to attacker host on cross-host redirect)
Reported-by: tonghuaroot
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
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LocalAI [bot]
|
a891eedd08 |
fix(distributed): persist per-model load info so reconciler survives frontend restart (#9981)
* feat(distributed): add per-model ModelLoadInfo persistence
Adds a dedicated ModelLoadInfo table keyed by model name, decoupled from
the per-replica NodeModel rows. The reconciler can now recover model load
metadata after every NodeModel row has been removed (worker death,
eviction, MarkOffline reaping, frontend restart with stale heartbeats),
which is the read side of Bug-1 from the distributed mode bug hunt.
Registry exposes:
- UpsertModelLoadInfo: ON CONFLICT (model_name) update; last-write-wins,
matching the existing per-replica blob semantics under concurrent
multi-frontend dispatch.
- GetModelLoadInfo: read from the new table first; fall back to the
legacy NodeModel-blob scan for rows written before any frontend in
the cluster ran an UpsertModelLoadInfo (rolling-upgrade transition).
SetNodeModelLoadInfo (per-replica blob) is preserved for backward
compatibility and per-replica diagnostics; the dispatch-path hook in the
next commit calls both.
The new table joins the existing nodes AutoMigrate set under the same
schema-migration advisory lock.
Refs: Bug-1, docs/superpowers/specs/2026-05-24-distributed-mode-bug-hunt-findings.md
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7[1m]
* fix(distributed): persist per-model load info on dispatch
scheduleAndLoad now writes the (backendType, ModelOptions blob) pair to
the new ModelLoadInfo table in addition to the existing per-replica
NodeModel.model_opts_blob field. The per-replica blob still works for
the hot path; the per-model row outlives every NodeModel row going away,
which is what unblocks the reconciler on the read side.
Both writes are best-effort with warn-level logging on failure: a write
miss here just means the reconciler may need a fresh inference request
to repopulate, which is the pre-fix behavior.
Concurrency: two frontends loading the same model at the same time both
fire UpsertModelLoadInfo; ON CONFLICT (model_name) makes the row
converge to whichever commits last. Matches the existing per-replica
blob semantics.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7[1m]
* test(distributed): cover load info persistence and Bug-1 recovery
Adds Ginkgo specs that prove the persistence layer behaves correctly and
that the reconciler actually recovers from the frontend-restart scenario
that was failing in production:
registry_test.go:
- per-model row survives RemoveAllNodeModelReplicas (the bug repro)
- ON CONFLICT (model_name) updates backend type + blob, last-write-wins
- legacy NodeModel-blob fallback still works (rolling-upgrade transition)
- GetModelLoadInfo returns ErrRecordNotFound when both sources are empty
- UpsertModelLoadInfo rejects empty model names
reconciler_test.go:
- Bug-1 end-to-end: with min_replicas=2, no NodeModel rows, but a
ModelLoadInfo row present, one reconcile tick fires two scheduler
calls. Pre-fix this returned "no load info" and the scheduler never
got called until a fresh inference request arrived.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7[1m]
* docs(distributed): note restart-safe reconciler behavior
Adds a bullet to the Replica Reconciler section explaining that per-model
load metadata is persisted across frontend restarts via the new
model_load_infos PostgreSQL table, so a rolling upgrade no longer needs a
fresh inference request per model before the reconciler can replace dead
replicas.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7[1m]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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LocalAI [bot]
|
06e777b75e |
feat(distributed): gated X-LocalAI-Node response header (middleware + wrapper) (#9976)
* feat(distributed): add per-request node ID context holder Introduce pkg/distributedhdr, a leaf package carrying a per-request *atomic.Value holder for the picked worker node ID from the SmartRouter (core/services/nodes) up to the HTTP response writer wrapper (core/http/middleware). Avoids the import cycle that a shared key in either consumer would create. Exposes NewHolder, WithHolder, Holder, Stamp, Load, Inherit. The holder is atomic.Value so cross-goroutine publish from the router to the response writer wrapper is race-clean. Assisted-by: Claude:claude-opus-4-7[1m] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): add ExposeNodeHeader middleware + response writer wrapper New ApplicationConfig.ExposeNodeHeader bool + --expose-node-header CLI flag / LOCALAI_EXPOSE_NODE_HEADER env var (default off; the node ID reveals internal topology and is opt-in). The middleware creates a per-request *atomic.Value holder, attaches it to c.Request().Context() via distributedhdr.WithHolder, and wraps c.Response().Writer with a custom http.ResponseWriter that sets the X-LocalAI-Node header on first Write / WriteHeader / Flush by reading the holder. Implements http.Flusher, http.Hijacker, Unwrap so it composes cleanly with Echo and http.NewResponseController. request.go propagates the holder onto derived contexts via distributedhdr.Inherit so the holder survives the correlation-ID context replacement. Unit + race-clean concurrency + integration specs. Assisted-by: Claude:claude-opus-4-7[1m] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): stamp node ID in router and wire middleware to inference routes ModelRouterAdapter.Route stamps the picked node ID into the per-request holder via distributedhdr.Stamp(ctx, result.Node.ID) right after replica selection. Wire ExposeNodeHeader middleware to: - OpenAI chat/completion/embeddings + audio transcriptions/speech + image generations/inpainting - Anthropic /v1/messages - Ollama /api/chat, /api/generate, /api/embed, /api/embeddings - Jina /v1/rerank - LocalAI /v1/vad The middleware's wrapper reads the holder on first byte and sets the X-LocalAI-Node response header before delegating to the underlying writer. Per-request scope means no race under concurrent multi-replica routing. Assisted-by: Claude:claude-opus-4-7[1m] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): thread request context through backend Load + cover ctx propagation Five non-OpenAI backend helpers were silently using app.Context instead of the request context for the gRPC backend call: transcription, TTS, image generation, rerank, VAD. Effect: distributedhdr.Stamp in the router callback was a silent no-op for these paths, AND client cancellation didn't propagate to in-flight inference. Thread c.Request().Context() (or the equivalent input.Context after the request middleware has installed the correlation-ID derived context) through each helper and into ModelOptions via model.WithContext(ctx). ImageGeneration's signature gains a leading ctx parameter; in-tree callers (openai image, openai inpainting, openai inpainting_test) are updated to match. ModelEmbedding gains a leading ctx parameter for the same reason; the openai and ollama embedding handlers pass the request context through. chat_stream_workers.go defers the initial role=assistant chunk emission until the first token callback so the wrapper's lazy X-LocalAI-Node lookup against the loader runs AFTER ml.Load has stamped the per-modelID node ID; semantically identical for clients (role still arrives before any text). Regression test core/backend/ctx_propagation_test.go pins ctx propagation for all five helpers. Docs updated to enumerate the full endpoint coverage of the --expose-node-header flag. Assisted-by: Claude:claude-opus-4-7[1m] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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|
Richard Palethorpe
|
6a80e23733 |
feat(middleware): Model routing, PII filtering, Cloud model proxies (#9802)
Add a routing middleware stack and a cloud-proxy backend. * cloud-proxy: a Go gRPC backend that forwards OpenAI- and Anthropic-shaped chat requests to upstream providers, with an optional translate mode (OpenAI request -> Anthropic /v1/messages -> OpenAI response) and full tool-calling support. * routing: admission control, content-aware model routing (embedding cache + classifier + rerank + Arch-Router score), PII detection/redaction (regex + NER) with streaming filter and OpenAI/Anthropic adapters, and a per-user/per-key billing recorder backed by GORM or in-memory storage. * middleware: UsageMiddleware records usage via the billing recorder, plus admission, route-model, usage-stamp and trace middlewares. * observability: BackendTrace ring buffer stores full request bodies (capped), MITM proxy emits structured trace events, and router classifier decisions surface at /api/router/decide. * gallery: Arch-Router-1.5B (Q4_K_M and Q8_0). * UI: cloud-proxy model-editor fields, classifier system-prompt and score-normalization config, and a Traces page rendering request bodies. Assisted-by: claude-code:claude-opus-4-7 [Read] [Edit] [Bash] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]
|
8bbe89a537 |
fix(distributed): route per request across loaded replicas + cache probeHealth (#9968)
* refactor(distributed): extract PickBestReplica from FindAndLockNodeWithModel Lifts the replica-selection policy (in_flight ASC, last_used ASC, available_vram DESC) out of the SQL ORDER BY into a pure Go function in the new replicapicker.go. The SQL clause keeps its FOR UPDATE atomicity and remains the production path used by SmartRouter; PickBestReplica is the canonical implementation that the future per-frontend rotating replica cache (TODO referenced from pkg/model) will call against an in-memory snapshot without paying a DB round-trip per inference. A new registry_test mirror spec seeds a multi-tier scenario and asserts both layers pick the same replica, so any future tweak to either side fails the test until the other side is updated. No behavior change. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-7 [Claude Code] * fix(distributed): route per inference request and cache probeHealth Two related fixes that together restore load balancing across loaded replicas of the same model. 1. ModelLoader.Load and LoadModel bypass the local *Model cache when modelRouter is set. The cached *Model wraps an InFlightTrackingClient bound to a single (nodeID, replicaIndex) — reusing it pinned every subsequent request to whichever node won the very first pick, so FindAndLockNodeWithModel's round-robin never got a chance to run even after the reconciler scaled the model out to a second node. In distributed mode SmartRouter.Route now runs per request, and PickBestReplica picks the least-loaded replica each time. SmartRouter has its own coalescing (advisory DB lock for first-time loads + singleflight on backend.install RPC) so concurrent first requests for a not-yet-loaded model still produce a single worker side install. 2. SmartRouter.probeHealth memoizes successful gRPC HealthCheck results in a new probeCache (probe_cache.go) with a 30s TTL. With per-request routing every inference call hits probeHealth, and llama.cpp-style backends serialize HealthCheck behind active Predict — so a burst of incoming requests stalled on the probe to a node already mid-stream, tripping the 2s timeout and falling through to the install path. singleflight collapses N concurrent first-time probes for the same (node, addr) into one round-trip, failed probes invalidate the entry so the staleness-recovery path still triggers, and the TTL matches pkg/model/model.go's healthCheckTTL so the single-process and distributed paths share a staleness budget. The background HealthMonitor still reaps actually-dead backends within ~45s. The bypass introduces one short FindAndLockNodeWithModel transaction per inference. A TODO in pkg/model/loader.go documents the future per modelID rotating-replica cache that would reuse PickBestReplica against an in-memory snapshot and skip the DB round-trip for hot paths. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-7 [Claude Code] --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
|
a0f3e26245 |
fix(distributed): make admin backend installs resilient and observable (#9958)
* feat(distributed): add configurable NATS backend install/upgrade timeouts Adds BackendInstallTimeout and BackendUpgradeTimeout to DistributedConfig with 15m defaults, following the existing MCPToolTimeout / WorkerWaitTimeout pattern. These will replace the hardcoded literals in RemoteUnloaderAdapter so admin-driven backend installs across the cluster survive long OCI image pulls that previously timed out at 3m. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(distributed): gofmt alignment after timeout fields Re-aligns the Validate() negative-duration map and the Default* const block so the new BackendInstall/UpgradeTimeout entries do not leave the surrounding columns mis-padded. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(cli): surface LOCALAI_NATS_BACKEND_INSTALL_TIMEOUT and _UPGRADE_TIMEOUT Parses the two new env vars on the run CLI and threads them through the existing AppOption builder so DistributedConfig picks them up. Invalid duration strings now fail loudly at startup rather than silently falling back to the default. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): inject NATS install/upgrade timeouts into RemoteUnloaderAdapter Removes the hardcoded 3m / 15m literals from RemoteUnloaderAdapter and threads in DistributedConfig.BackendInstallTimeoutOrDefault() and BackendUpgradeTimeoutOrDefault() at construction. Install now defaults to 15m (was 3m); cold OCI image pulls on Jetson Wi-Fi routinely blew past the old ceiling. Scripted messaging client captures the timeout so tests can assert the configured value actually reaches the NATS request. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): introduce galleryop.ErrWorkerStillInstalling sentinel When the NATS request-reply for backend.install (or .upgrade) times out the worker is almost always still pulling the OCI image. Wrap the timeout in a typed sentinel so the manager above can distinguish "worker hung" from "worker still working" and leave the pending_backend_ops row in place for the reconciler to confirm via backend.list. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): treat NATS install timeout as in-progress, not failure When a worker times out replying to backend.install but the install is still running on the worker, enqueueAndDrainBackendOp now reports a running_on_worker status and pushes NextRetryAt out by the install timeout so the reconciler does not immediately re-fire another install while the worker is still pulling the image. The pending_backend_ops row stays in place for the next reconciler pass to confirm via backend.list. InstallBackend wraps the result in galleryop.ErrWorkerStillInstalling so callers can branch (galleryop renders yellow in-progress instead of red error). UpgradeBackend uses the same wrap. Adds RemoteUnloaderAdapter.InstallTimeout() so the manager can push NextRetryAt by the configured timeout without reaching into a private field, and NodeRegistry.RecordPendingBackendOpInFlight as the soft cousin of RecordPendingBackendOpFailure. Also includes incidental gofmt-driven struct-field alignment in registry.go on lines unrelated to the change (touched files are re-formatted to canonical form per project policy). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): don't increment Attempts on in-flight install timeout An in-flight timeout (worker still pulling the OCI image) is not a failed attempt, it's a delayed one. Incrementing Attempts let genuinely-progressing slow installs (e.g. 30 GB CUDA images on Wi-Fi) trip the reconciler's maxPendingBackendOpAttempts cap and dead-letter the queue row while the worker was still legitimately working. RecordPendingBackendOpInFlight now only updates LastError and NextRetryAt. Also documents "running_on_worker" in the NodeOpStatus.Status enum comment so Task 6 implementers see the full surface. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(galleryop): surface ErrWorkerStillInstalling as non-error OpStatus When the distributed backend manager returns an error that wraps ErrWorkerStillInstalling, backendHandler now completes the op with a "still installing in background" message rather than marking it as a red failure. Admin UI sees a yellow in-progress state; reconciler confirms completion on its next pass. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(distributed): end-to-end install-timeout-then-reconcile Wires Task 1-6 end-to-end so any seam mismatch surfaces in CI rather than during a real cluster install. NATS times out, the queue row stays alive with running_on_worker status, the worker eventually reports the backend installed via backend.list, the manager surfaces it via ListBackends. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): document LOCALAI_NATS_BACKEND_INSTALL_TIMEOUT / _UPGRADE_TIMEOUT Add the two new operator-tunable env vars to the Frontend Configuration table in the distributed-mode docs. Explains the 15m default, when to raise it (slow links pulling multi-GB OCI images), and the new "still installing in background" admin-UI state when the round-trip times out but the worker is still working. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): clear pending install rows when backend.list confirms DistributedBackendManager.ListBackends now proactively clears pending_backend_ops install rows whose (nodeID, backend) is reported installed by backend.list. Operator UI updates immediately instead of waiting up to installTimeout (default 15m) for the next reconciler tick after NextRetryAt. Only install rows are cleared; upgrade and delete intents are not satisfied by presence in backend.list and continue to drain through their normal reconciler paths. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(messaging): add BackendInstallProgressEvent wire type and subject New NATS subject nodes.<nodeID>.backend.install.<opID>.progress lets the worker publish transient progress events (file, current/total bytes, percentage, phase) while a long-running install pulls its OCI image. BackendInstallRequest gains an optional OpID field so the worker knows which subject to publish on. Transient pub/sub (not JetStream): the install reply remains ground truth for success/failure; dropped progress events are tolerable. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(messaging): drop em-dash from BackendInstallProgress test comment Per project convention (no em-dashes anywhere). Comment substance is unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): worker publishes debounced install progress over NATS When BackendInstallRequest.OpID is set, the worker's backend.install handler wires a debounced publisher (250ms window) into the gallery download callback. Each tick becomes a BackendInstallProgressEvent on nodes.<nodeID>.backend.install.<opID>.progress; the publisher always emits a final event on Flush so the UI sees the terminal percentage. Old masters that do not set OpID continue to run silent installs: no behavior change for them. Lock ordering: the publisher releases its mutex before calling messaging.Publish so a slow network never stalls the install loop. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): RemoteUnloaderAdapter subscribes to install progress InstallBackend gains opID + onProgress parameters. When both are set, the adapter subscribes to nodes.<nodeID>.backend.install.<opID>.progress BEFORE publishing the install request, decodes each message into the caller's onProgress callback in a goroutine (so a slow callback never stalls the NATS reader thread), and unsubscribes after RequestJSON returns. When onProgress is nil OR opID is empty (the reconciler retry path), subscription is skipped entirely - silent installs cost nothing extra. Subscribe failure is logged at Warn and the install proceeds without progress streaming; the NATS round-trip still owns terminal status. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): forward backend install progress into galleryop OpStatus DistributedBackendManager.InstallBackend now passes the gallery op ID and a progress bridge into the adapter call. Each BackendInstallProgressEvent from the worker becomes a galleryop.ProgressCallback tick - which the existing backendHandler already turns into OpStatus.UpdateStatus, so the admin UI/SSE polling sees per-byte progress for distributed installs without any UI-side change. UpgradeBackend is intentionally left silent for now: its wire request (BackendUpgradeRequest) does not carry OpID, and rolling-update fallback is the rarer path. Will be picked up in a follow-up if the worker upgrade path also gets a progress channel. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(distributed): InstallBackend tolerates silent (pre-Phase-2) workers A worker on pre-Phase-2 code never publishes progress events. The new master subscribes optimistically; this spec pins that a silent worker still produces a green install with no progressCb ticks. The install reply is the source of truth for terminal state; the progress stream is a best-effort UX enrichment. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): document install progress streaming Note the new nodes.<nodeID>.backend.install.<opID>.progress subject and the silent-worker compatibility behavior so operators know to expect real-time progress and what happens on a mixed-version cluster. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): note progress-event ordering trade-off in InstallBackend Document near the goroutine dispatch why ordering at the consumer is best-effort, why it rarely matters in practice (worker debounce >> goroutine jitter), and what a future hardening pass would look like (Seq field + stale-by-seq drop). Stops the next reader from accidentally "fixing" the goroutine pool away. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(galleryop): add NodeProgress + OpStatus.Nodes for per-node breakdown Adds the data model the UI needs to render an expandable per-node breakdown of a fanned-out backend install. NodeProgress carries node identity (ID + name), per-node status (queued / running_on_worker / success / error / downloading), the current file + bytes + percentage from the Phase 2 progress stream, and any per-node error. OpStatus.Nodes is the slice the /api/operations handler will surface in a follow-up. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(galleryop): UpdateNodeProgress merges per-node ticks by NodeID GalleryService.UpdateNodeProgress(opID, nodeID, np) merges a NodeProgress into OpStatus.Nodes (keyed by NodeID, no duplicates) and mirrors the latest tick into the aggregate Progress / FileName / DownloadedFileSize / TotalFileSize fields so the legacy single-bar OperationsBar view keeps working unchanged alongside the new per-node breakdown. Concurrent-safe via the existing g.Mutex. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): write per-node OpStatus entries during install fan-out DistributedBackendManager now accepts a nodeProgressSink and feeds it two streams: 1. enqueueAndDrainBackendOp emits a per-node terminal entry on each status it appends to BackendOpResult (queued, success, error, running_on_worker). The opID is threaded through the function so the sink gets the right gallery op identity. 2. The install apply closure fans each BackendInstallProgressEvent into the sink as a downloading entry, alongside the legacy progressCb path so the aggregate single-bar view stays correct. Production wiring passes the GalleryService (which implements UpdateNodeProgress via Task 2) as the sink. Single-node tests pass nil. DeleteBackend and UpgradeBackend pass an empty opID so the sink path no-ops for ops that aren't gallery-tracked the same way as Install. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(operations): expose per-node breakdown on /api/operations When an operation's OpStatus has Nodes entries (populated by the Phase 4 progress sink wiring), surface them as a "nodes" array on the /api/operations response, sorted by node_name for stable rendering. Backward compatible: legacy clients ignore the field; ops without any node entries (single-node mode, model installs) omit the array entirely thanks to the empty-slice guard. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): per-node breakdown in OperationsBar When an install op fans out to more than one worker, the operations bar now shows a "N nodes" chevron that expands into a per-node list. Each row carries the node's status (color-coded pill), the current file being downloaded, byte counts, percentage, and a thin per-node progress bar. Yellow "Worker busy" pill marks running_on_worker status with a tooltip explaining the NATS round-trip timed out but the worker is still installing in the background. Backward compatible: ops without a nodes field (legacy or single-node mode) render as before. State for expand/collapse is local to the component, keyed by jobID/id - reload starts collapsed. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): document per-node breakdown in the operations bar Adds a short subsection covering the expandable "N nodes" chevron in the OperationsBar admin UI, the meaning of each status pill, and how it relates to the /api/operations nodes array. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(galleryop): UpdateStatus preserves Nodes when caller sends none Real-world bug surfaced by the Phase 4 multi-worker smoke test: the nodes[] array in /api/operations flickered between a single node at a time on a 2-worker install. Root cause: the Phase 2 progress bridge also calls the legacy progressCb -> UpdateStatus(&OpStatus{...}) on every tick. UpdateStatus then overwrote the entire status pointer, wiping the Nodes slice that UpdateNodeProgress had just merged in. Fix: in UpdateStatus, if the incoming op has an empty Nodes slice, carry forward the previous status's Nodes before storing. Callers that explicitly populate Nodes still win (their slice replaces the prior one, no merge across the two code paths). Two regression specs added pinning both directions of the contract. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): strip implementation details from user-facing docs Trim the new install/upgrade timeout rows and the install-progress sections to focus on what the operator sees and tunes. Drops: - the NATS subject names and pub/sub mechanics - "round-trip" / reconciler / backend.list jargon - /api/operations polling cadence - "pre-2026-05-22" version references Reframes the breakdown text around the admin UI (Operations Bar, chevron, status pills, "Worker busy" tooltip). Implementation context lives in the agent notes and code comments. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(config): move DistributedConfig.Validate flag names to constants The negative-duration check map was a wall of literal kebab-case strings that had to stay in sync with the kong-derived CLI flag names manually. Move them to a Flag* const block alongside the existing Default* block so a rename of either the Go field or the CLI naming convention forces a compile error rather than silent drift. Sole consumer today is Validate; the constants are exported so future operator-facing surfaces (e.g. error messages on other validation paths) can reference them by name instead of repeating the literals. Tests pin both the literal values (so a future "let's just rename this" doesn't accidentally regress the CLI flag) and the negative- duration error message for the new BackendInstall / BackendUpgrade fields. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(distributed): extract NodeStatus and Phase enums to constants Sweep for the same literal-string-as-identifier pattern called out on the Validate flag names: the per-node install status enum ("queued" | "downloading" | "running_on_worker" | "success" | "error") appeared as raw literals across managers_distributed.go (10+ sites, including 3 separate `n.Status == "running_on_worker"` checks), operation.go, and the test suite. Same shape for the Phase enum ("resolving" | "downloading" | "extracting" | "starting") in the worker-side progress publisher. Promote both to exported const blocks: - galleryop.NodeStatus{Queued,Downloading,RunningOnWorker,Success,Error} shared between galleryop.NodeProgress.Status (the wire field) and nodes.NodeOpStatus.Status (the in-process per-node summary) - messaging.Phase{Resolving,Downloading,Extracting,Starting} shared between the worker publisher and any future consumer that needs to switch on phase Tests pin both the literal values (so a future "let's just rename" doesn't silently change the JSON wire) and use the constants in setup (so the producer side stays drift-protected). Wire-format assertions on the /api/operations JSON output keep their literals deliberately, so the constant value can never silently diverge from what the UI receives. Out of scope for this PR (separate cleanup): the finetune and quantization job-status enums have the same anti-pattern with 14+ literal sites each, but predate this PR's work. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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a39e025d64 |
fix(nodes): make per-node backend install async via gallery job queue (#9928)
* feat(galleryop): add TargetNodeID to ManagementOp for single-node installs Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(galleryop): add NodeScopedKey helpers for per-node opcache rows Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(galleryop): use strings.Cut for NodeScopedKey parsing, reject empty nodeID Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(nodes): scope DistributedBackendManager.InstallBackend to single node via TargetNodeID Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(http): make /api/nodes/:id/backends/install async via gallery service job queue The handler previously called unloader.InstallBackend synchronously and blocked the browser for up to 3 minutes waiting on the NATS reply. It now enqueues a TargetNodeID-scoped ManagementOp on BackendGalleryChannel and returns HTTP 202 + jobID immediately, matching /api/backends/install/:id. The opcache key is built via NodeScopedKey(nodeID, backend) so concurrent installs of the same backend across different nodes do not stomp each other. galleryService/opcache/appConfig are threaded through RegisterNodeAdminRoutes for this. Assisted-by: Claude:opus-4-7 [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(http): log malformed backend_galleries override and stop test drain goroutine Assisted-by: Claude:opus-4-7 [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(api): expose nodeID for node-scoped backend ops in /api/operations Node-scoped backend installs land in opcache under "node:<nodeID>:<backend>" keys. Without splitting that prefix back out, the operations panel renders the full key as the display name and has no structured way to label which worker an install is targeting. Detect the prefix, surface nodeID as its own response field, and reduce the display name back to the bare backend slug. Bare (non-scoped) ops are left untouched so legacy installs do not gain a misleading empty nodeID. Assisted-by: Claude:opus-4-7 [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(react-ui): poll job status for node-targeted backend installs Assisted-by: Claude:opus-4-7 [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(react-ui): make NodeInstallPicker state updates pure and surface cancellations as errors Assisted-by: Claude:opus-4-7 [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(react-ui): clarify async semantics in handleInstallOnTarget Assisted-by: Claude:opus-4-7 [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(http): use statusUrl casing for node install response to match codebase precedent Assisted-by: Claude:opus-4-7 [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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b4fdb41dcc |
fix(distributed): cascade-clean stale node_models rows + filter routing by healthy status (#9754)
* fix(distributed): cascade-clean stale node_models on drain and filter routing by healthy status Stale node_models rows (state="loaded") were surviving past the healthy state of their owning node, causing /embeddings (and other inference paths) to dispatch to a backend whose process was gone or drained. The downstream symptom in a live cluster was pgvector rejecting inserts with "vector cannot have more than 16000 dimensions (SQLSTATE 54000)" because the misbehaving backend silently returned a malformed (oversized) tensor; the Models page showed the model as "running" without an associated node, like a stale entry, even though the node was no longer visible in the Nodes view. Two changes here, plus a third in a follow-up commit: - MarkDraining now cascade-deletes node_models rows for the affected node, mirroring MarkOffline. Drains are explicit operator actions — the box has been intentionally taken out of rotation — so clearing the rows stops the Models UI from misreporting and prevents the routing layer from picking those rows if scheduling logic is ever relaxed. In-flight requests already hold their gRPC client through Route() and finish normally; the only observable effect is a non-fatal IncrementInFlight warning, acceptable for a drain. MarkUnhealthy is deliberately left status-only: it fires from managers_distributed / reconciler on a single nats.ErrNoResponders with no retry, so a transient NATS hiccup must not nuke every loaded model and force a full reload on recovery. - FindAndLockNodeWithModel's inner JOIN now filters on backend_nodes.status = healthy in addition to node_models.state = loaded. The previous version relied on the second node-fetch step to reject non-healthy nodes, but a concurrent reader could still pick the same stale row in the same window. Belt-and-braces. - DistributedConfig.PerModelHealthCheck renamed to DisablePerModelHealthCheck and inverted at the call site so per-model gRPC probing is on by default. The probe (now made consecutive-miss aware in a follow-up commit) independently health- checks each model's gRPC address and removes stale node_models rows when the backend has crashed even though the worker's node-level heartbeat is still arriving. Migration: the field had no CLI flag, env var binding, or YAML key in tree (only the bare struct field), so there is no user-facing migration. Anything constructing DistributedConfig in code needs to drop the assignment (default now does the right thing) or invert it. Assisted-by: Claude:claude-opus-4-7 go-vet go-test golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): require consecutive misses before per-model probe removes a row The per-model gRPC probe used to remove a node_models row on a single failed health check. With the per-model probe now on by default, that made any 5-second gRPC blip (network jitter, a long-running request hogging the worker's gRPC server thread, brief GC pause) trigger a full reload of the affected model — too eager for production. Require perModelMissThreshold (3) consecutive failed probes before removal. At the default 15s tick a model must be unreachable for ~45s before reap; a single successful probe in between resets the streak. Per-(node, model, replica) state tracked under a mutex on the monitor. If the removal call itself fails, the miss counter is left in place so the next tick retries rather than starting the streak over. Tests: - removes stale model via per-model health check after consecutive failures (replaces the single-shot expectation) - preserves model row when an intermittent failure is followed by a success (covers the reset-on-success path and verifies the counter reset by failing twice more without crossing threshold) - newTestHealthMonitor initializes the misses map so direct-construct test helpers don't nil-map-panic in the probe path Assisted-by: Claude:claude-opus-4-7 go-vet go-test golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |