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11
Commits
| Author | SHA1 | Message | Date | |
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 mudler-agent
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cd604b5c5b |
fix(distributed): bound, stop and cancel model loads with leases and worker operations (#12524)
Fence model load jobs by generation, lease them on the database clock, bound the work on the worker with operations and a process-group watchdog, and add one stop path with a load-cancel API. See the pull request for the design, the rolling upgrade notes and the test evidence. Assisted-by: Claude Code:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler-agentandEttore Di Giacinto
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590512d9eb |
feat(distributed): report worker version and show models in node inspector (#12328)
* ci: bump Hugo from 0.146.3 to 0.166.0 The hugo-theme-relearn submodule was bumped to 9.1.x in #12096, which requires Hugo >= 0.165.0. The pinned 0.146.3 broke the docs site build with a template error in alias.html that could not evaluate the Locale field on langs.Language. Bump HUGO_VERSION to 0.166.0 (latest stable) to satisfy the theme minimum and resolve the alias.html template error. Assisted-by: nib:claude-sonnet-4.5 [bash] [read] [edit] * feat(distributed): report worker version and show models in node inspector Workers now send their LocalAI build version and git commit at registration. The controller stores them on BackendNode and exposes them through the existing node list/detail API responses. The node inspector side pane now fetches and renders the list of loaded models (name, state, in-flight) instead of showing only a count, matching what the node detail page already displays. --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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localai-org-maint-botandEttore Di Giacinto
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997d403de4 |
feat(nodes): add fleet operations dashboard (#12046)
* feat(nodes): report CPU telemetry Assisted-by: Codex:gpt-6 * feat(nodes): add fleet view utilities Assisted-by: Codex:gpt-6 * feat(nodes): add fleet operations dashboard Replace the panel roster with aggregate capacity gauges, fleet filtering and selection, bounded bulk actions, and an on-demand node inspector. Extend node details and distributed-mode documentation with CPU and models-disk telemetry. Assisted-by: Codex:gpt-6 * fix(nodes): harden fleet lifecycle actions Assisted-by: Codex:gpt-6 * fix(nodes): restore compact fleet composition Keep fleet health, capacity, and attention in one compact overview at ordinary desktop widths. The inspector now overlays the roster until the workbench can preserve a useful table beside it. Assisted-by: Codex:gpt-6 * feat(nodes): add accessible running models workbench Assisted-by: Codex:gpt-6 * fix(nodes): correct model view ARIA links Keep each tab panel available for its controlling tab while native hidden state removes inactive content from accessibility navigation. Model controls now expose only supported state and valid inspector relationships. Assisted-by: Codex:gpt-6 * fix(nodes): align lifecycle and capacity states Pending nodes now expose approval wherever node actions appear, while other lifecycle controls follow the server transition rules. Capacity totals exclude incomplete readings so missing availability remains unknown. Assisted-by: Codex:gpt-6 * fix(nodes): restore approved dashboard composition Assisted-by: Codex:gpt-6 * fix(nodes): integrate operate navigation Assisted-by: Codex:gpt-6 * fix(nodes): restore low density fleet view Assisted-by: Codex:gpt-6 * fix(nodes): preserve complete operate menu Assisted-by: Codex:gpt-6 * fix(nodes): preserve inspector workspace height Assisted-by: Codex:gpt-6 * fix(nodes): restore standard operate navigation Assisted-by: Codex:gpt-6 * feat(ui): add collapsible console rail Assisted-by: Codex:gpt-6 * feat(nodes): stop models from fleet view Assisted-by: Codex:gpt-6 * fix(nodes): make inspector a full height drawer Assisted-by: Codex:gpt-6 * fix(model): stop mixed local and remote placements Assisted-by: Codex:gpt-6 * fix(ui): announce action menu navigation Assisted-by: Codex:gpt-6 * fix(nodes): keep inspector within viewport Assisted-by: Codex:gpt-6 * fix(ui): preserve focus across model actions Assisted-by: Codex:gpt-6 --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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localai-org-maint-botandlocalai-org-maint-bot
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034df6ceb1 |
fix(worker): report RAM alongside GPU memory (#11167)
* fix(worker): report RAM alongside GPU memory Assisted-by: Codex:gpt-5 * feat(ui): show worker RAM on node views Assisted-by: Codex:gpt-5 --------- Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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mudler's LocalAI [bot]andEttore Di Giacinto
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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]andEttore Di Giacinto
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e55cc3e2a7 |
fix(worker): bound the gRPC port allocator and stop leaking dead backends' ports (#10968)
The worker's gRPC port allocator grew monotonically with no upper bound: nextPort started at the base port and incremented whenever freePorts was empty, and nothing checked 65535. Past that it handed out integers that cannot be bound, surfacing as an opaque "backend won't start". #10961 estimated this needed ~15,000 concurrent-peak allocations, i.e. effectively unreachable. It is not, because of a second defect: the "process died unexpectedly" branch in startBackend deleted the process map entry without releasing its port at all. That port was leaked, never quarantined and never reused. A crash-looping backend leaks one port per restart, so a backend dying every 30s walks 50051 to 65535 in about five days. The leak, not concurrent peak, is the realistic route to exhaustion. Fixing the leak alone would have been wrong. Releasing that port makes it re-bindable, and the death path is the one teardown path with no request/reply to carry StoppedProcessKeys back to the controller (#10952's eager row removal), so a stale NodeModel row could then resolve to a live listener belonging to a different backend. probeHealth verifies liveness, not identity, so the request is silently misrouted. The 15s port quarantine does not cover this: the only reaper is the per-model health check at ~45s, and it can be disabled outright. The residual was masked only because the port was never rebound. So both are fixed together: - The allocator takes an explicit [basePort, LOCALAI_GRPC_MAX_PORT] range and returns ErrNoFreePort naming the range, the live backend count, the quarantined count, and the knob to raise. Exhaustion is now diagnosable instead of surfacing as an unbindable port. - Released ports carry per-key affinity: a port is offered back to the process key that last held it before any other key. Process keys (modelID#replica) and NodeModel rows (nodeID, modelName, replicaIndex) are isomorphic, so a port that can only be re-bound by its previous owner can only ever be named by that owner's row, which that key's re-registration overwrites. Misrouting to a different model becomes impossible by construction rather than by racing the quarantine timer. Affinity is a preference, not a reservation: under range pressure an owned port is stolen with a warning, because a guaranteed outage is worse than a rare misroute window on a port long out of quarantine. Claiming a port evicts its previous owner's entry, keeping ownership injective over ports so the affinity map can never exceed the range width regardless of how many distinct model keys the worker sees. Ownership also expires. It is only load-bearing while a controller row could still name the port, which the per-model reaper bounds at roughly 45s, so it lapses after five minutes and the port becomes ordinary free space again. Holding it indefinitely would have made every distinct model the worker ever served consume a port permanently: every release path is keyed, so nothing would ever be unowned, the allocator would climb to the end of its range on distinct-key count rather than concurrency, stealing would become routine, and the steal warning would tell operators to widen a range that was not the constraint. With expiry, reaching the steal branch means the worker is genuinely out of concurrent capacity, so that advice is correct when it appears. Closes #10961 Closes #10952 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]andEttore Di Giacinto
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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
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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]andEttore Di Giacinto
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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]andEttore Di Giacinto
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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]andEttore Di Giacinto
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e5d7b84216 |
fix(distributed): split NATS backend.upgrade off install + dedup loads (#9717)
* feat(messaging): add backend.upgrade NATS subject + payload types
Splits the slow force-reinstall path off backend.install so it can run on
its own subscription goroutine, eliminating head-of-line blocking between
routine model loads and full gallery upgrades.
Wire-level Force flag on BackendInstallRequest is kept for one release as
the rolling-update fallback target; doc note marks it deprecated.
Assisted-by: Claude:claude-sonnet-4-6
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(distributed/worker): add per-backend mutex helper to backendSupervisor
Different backend names lock independently; same backend serializes. This
is the synchronization primitive used by the upcoming concurrent install
handler — without it, wrapping the NATS callback in a goroutine would
race the gallery directory when two requests target the same backend.
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(distributed/worker): run backend.install handler in a goroutine
NATS subscriptions deliver messages serially on a single per-subscription
goroutine. With a synchronous install handler, a multi-minute gallery
download would head-of-line-block every other install request to the
same worker — manifesting upstream as a 5-minute "nats: timeout" on
unrelated routine model loads.
The body now runs in its own goroutine, with a per-backend mutex
(lockBackend) protecting the gallery directory from concurrent operations
on the same backend. Different backend names install in parallel.
Backward-compat: req.Force=true is still honored here, so an older master
that hasn't been updated to send on backend.upgrade keeps working.
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(distributed/worker): subscribe to backend.upgrade as a separate path
Slow force-reinstall now lives on its own NATS subscription, so a
multi-minute gallery pull cannot head-of-line-block the routine
backend.install handler on the same worker. Same per-backend mutex
guards both — concurrent install + upgrade for the same backend
serialize at the gallery directory; different backends are independent.
upgradeBackend stops every live process for the backend, force-installs
from gallery, and re-registers. It does not start a new process — the
next backend.install will spawn one with the freshly-pulled binary.
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(distributed): add UpgradeBackend on NodeCommandSender; drop Force from InstallBackend
Master now sends to backend.upgrade for force-reinstall, with a
nats.ErrNoResponders fallback to the legacy backend.install Force=true
path so a rolling update with a new master + an old worker still
converges. The Force parameter leaves the public Go API surface
entirely — only the internal fallback sets it on the wire.
InstallBackend timeout drops 5min -> 3min (most replies are sub-second
since the worker short-circuits on already-running or already-installed).
UpgradeBackend timeout is 15min, sized for real-world Jetson-on-WiFi
gallery pulls.
Updates the admin install HTTP endpoint
(core/http/endpoints/localai/nodes.go) to the new signature too.
router_test.go's fakeUnloader does not yet implement the new interface
shape; Task 3.2 will catch it up before the next package-level test run.
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(distributed): update fakeUnloader for new NodeCommandSender shape
InstallBackend lost its force bool param (Force is not part of the public
Go API anymore — only the internal upgrade-fallback path sets it on the
wire). UpgradeBackend gained a method. Fake records both call slices and
provides an installHook concurrency seam for upcoming singleflight tests.
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(distributed): cover UpgradeBackend's new subject + rolling-update fallback
Task 3.1 changed the master to publish UpgradeBackend on the new
backend.upgrade subject; the existing UpgradeBackend tests scripted the
old install subject and so all 3 began failing as expected. Updates them
to script SubjectNodeBackendUpgrade with BackendUpgradeReply.
Adds two new specs for the rolling-update fallback:
- ErrNoResponders on backend.upgrade triggers a backend.install
Force=true retry on the same node.
- Non-NoResponders errors propagate to the caller unchanged.
scriptedMessagingClient gains scriptNoResponders (real nats sentinel) and
scriptReplyMatching (predicate-matched canned reply, used to assert that
the fallback path actually sets Force=true on the install retry).
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(distributed): coalesce concurrent identical backend.install via singleflight
Six simultaneous chat completions for the same not-yet-loaded model were
observed firing six independent NATS install requests, each serializing
through the worker's per-subscription goroutine and amplifying queue
depth. SmartRouter now wraps the NATS round-trip in a singleflight.Group
keyed by (nodeID, backend, modelID, replica): N concurrent identical
loads share one round-trip and one reply.
Distinct (modelID, replica) keys still fire independent calls, so
multi-replica scaling and multi-model fan-out are unaffected.
fakeUnloader gains a sync.Mutex around its recording slices to keep
concurrent test goroutines race-clean.
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(e2e/distributed): drop force arg from InstallBackend test calls
Two e2e test call sites still passed the trailing force bool that was
removed from RemoteUnloaderAdapter.InstallBackend in
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