* 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>
* fix(gpu): detect GPUs via sysfs when no pci.ids database is present
ghw.GPU() calls pci.New() before it reads /sys/class/drm and fails
outright when it cannot find a pci.ids database file. jaypipes/pcidb
embeds no database and has network fetch disabled by default, so on an
image that ships no pci.ids, GPU enumeration returns an error and every
detection path downstream goes dark.
The Dockerfile installs pciutils only in the vulkan and cublas branches,
so the Intel image had no pci.ids. A correctly passed-through Arc A310
was reported as "No GPU detected" with zero VRAM even though clinfo and
sycl-ls both enumerated it inside the same container. NVIDIA and AMD
images were shielded by their nvidia-smi / rocm-smi binary fallbacks;
Intel has no equivalent, leaving it fully exposed.
Read PCI vendor IDs directly from /sys/class/drm/card*/device/vendor,
which needs no database, and consult that from DetectGPUVendor. The
same scan replaces the ghw-only guard in getIntelGPUMemory, which is
what had been blocking the working clinfo path and keeping VRAM at
zero. Install hwdata in the base image stage as well, so ghw stops
failing for every image variant rather than only Intel.
Also apply the documented NVIDIA > AMD > Intel priority to the ghw
path, which previously returned whichever card DRM enumerated first
and so reported "intel" on a machine with an Intel iGPU at card0 and
an NVIDIA dGPU at card1.
HasGPU() carried the same blindness plus one of its own: it matched
the requested vendor against ghw's card description with a
case-sensitive Contains, so "nvidia" never matched the pci.ids
spelling "NVIDIA Corporation". It only worked because that same
description embeds the lowercase kernel driver name ("nvidia",
"amdgpu"), and it returned false outright whenever ghw errored. Route
it through the shared vendor lookup so it matches case-insensitively
and falls back to sysfs. It feeds the GPU option and NGPULayers
defaults in core/config/gguf.go.
Fixes#10941
Assisted-by: Claude:claude-opus-4-8 golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactor(gpu): key vendor detection off the numeric PCI ID in both paths
The ghw and sysfs legs were identifying vendors by different means: ghw
by substring-matching the pci.ids vendor name, sysfs by the numeric PCI
vendor ID. ghw already exposes that same numeric ID via
DeviceInfo.Vendor.ID, read from the kernel's modalias rather than from
the database, so the name matching was both a duplicate mechanism and
the weaker of the two.
It is weaker because a card absent from an outdated pci.ids gets
Name: "unknown" while its ID is still correct. Detection then failed
even though ghw had enumerated the card successfully. Verified in a
container with a vendor-less pci.ids and an Arc's modalias: before,
DetectGPUVendor returned ""; after, "intel".
Both legs now resolve through the same pciVendorIDs table and share the
hex parsing, with the vendor name kept only as a fallback for devices
exposing no parseable ID.
ghwHasVendor is deliberately not a priority pick, unlike vendorFromGHW:
HasGPU("intel") must stay true on a hybrid-graphics host whose discrete
NVIDIA card outranks the integrated Intel one.
Assisted-by: Claude:claude-opus-4-8 golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(gpu): silence the gosec G304 on the sysfs attribute read
gosec flags os.ReadFile with a non-literal path. The path here is the
DRM root (a package constant in production, a temp dir under test)
joined with a ReadDir entry name and a fixed attribute filename, so no
external input reaches it.
gosec's suggested autofix, os.Root, cannot be used: /sys/class/drm/cardN
is a symlink into the PCI device tree, and os.Root refuses to traverse
it ("path escapes from parent"), which would disable the whole scan.
Assisted-by: Claude:claude-opus-4-8 gosec 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>
* 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>
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>
* 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>
fix(xsysinfo): make reported system RAM total cgroup/lxcfs-aware (#8059)
GetSystemRAMInfo derived Total from memory.TotalMemory(), which on Linux
uses syscall.Sysinfo().Totalram - the HOST kernel total. lxcfs/LXD does
NOT virtualize that value, while MemAvailable (used for Free/Available)
IS virtualized. Inside an LXD/container with a 128Gi host but a ~10Gi
container view this produced Total=128Gi, Available=10Gi => Used=118Gi,
reporting ~92% RAM usage on an idle container.
Derive Total instead from the minimum of all non-zero, non-unlimited
candidates: cgroup v2 memory.max, cgroup v1 memory.limit_in_bytes (the
kernel unlimited sentinel is ignored), /proc/meminfo MemTotal (which
lxcfs virtualizes), and the syscall.Sysinfo total as the bare-metal
fallback. On bare metal every candidate is unlimited or equals the host
total, so behavior is unchanged.
The selection/parsing lives in a pure function chooseTotalMemory(...)
taking file CONTENTS, unit-tested without a real LXD host; OS file
reads stay in a thin wrapper.
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>
* fix(gpu-detect): clinfo --json fallback for Intel discrete VRAM
ghw returns 0 VRAM for any i915-driven Intel GPU because the kernel
driver doesn't expose VRAM through the sysfs paths ghw checks (no
mem_info_vram_total — that's an amdgpu interface). xpu-smi, the
canonical Intel tool, isn't in the oneAPI base image (it lives in a
separate xpumanager package). The capability gate added in 19c92c70
("default to CPU if there is less than 4GB of GPU available") then
demotes the host to CPU even on a 16 GB Arc A770.
clinfo ships with the OpenCL ICD loader and is present in the oneAPI
base image, so plug it in as the last-resort Intel VRAM source:
xpu-smi -> intel_gpu_top -> clinfo --json
The parser drops UMA devices via HOST_UNIFIED_MEMORY=true so an iGPU
sibling can't double-count system RAM, and dedups by PCI BDF when
multiple ICDs enumerate the same physical device (POCL caps reported
GLOBAL_MEM_SIZE at 4 GiB; the largest non-capped value wins).
Subprocess is wrapped in a 2s timeout and memoised with sync.OnceValue
— GPU hardware is static for the process lifetime. The Intel branch
also short-circuits when ghw saw no Intel vendor, so NVIDIA-only hosts
don't pay the spawn cost.
Verified end-to-end on Intel Arc A770: ghw -> 0, clinfo path reports
16,225,243,136 bytes (15.11 GiB), capability gate now passes naturally
without LOCALAI_FORCE_META_BACKEND_CAPABILITY=intel.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(gpu-detect): live VRAM usage from DRM fdinfo
The clinfo fallback reports total VRAM correctly but leaves UsedVRAM
at 0 because OpenCL has no portable live-memory property — the UI
ends up showing 0% utilisation even when llama-cpp is actually
holding gigabytes in device memory.
Fill that gap with the standardised Linux DRM fdinfo interface
(Documentation/gpu/drm-usage-stats.rst, kernel ≥5.19). Walking
/proc/<pid>/fdinfo for any fd that points at /dev/dri/render* yields
drm-total-<region> / drm-resident-<region> keys; aggregate per
render-node, resolve the render node to a PCI BDF via
/sys/class/drm/<name>/device, and merge the result into the matching
GPUMemoryInfo by BDF.
Region naming is driver-defined — i915 uses "local0" for device-local
VRAM, amdgpu and xe use "vram0" — so a prefix-match on local/vram
covers all three DRM drivers that LocalAI cares about. system/gtt/
stolen regions are deliberately excluded since they're host RAM
mirrors and would double-count against system RAM.
GPUMemoryInfo gains an optional BDF field (`bdf,omitempty` in JSON)
so future vendor-specific detectors can plug into the same matcher.
Empty BDF skips the merge — non-PCI devices and detection paths that
don't surface PCI location keep their existing behaviour.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix: parse vulkan VRAM from text
Assisted-by: opencode:gpt-5.5
Signed-off-by: Andreas Egli <github@kharan.ch>
* fix: replace string.split with streaming iteration
Assisted-by: Opencode:Gemma4
Signed-off-by: Andreas Egli <github@kharan.ch>
---------
Signed-off-by: Andreas Egli <github@kharan.ch>
Workers on NVIDIA unified-memory hardware (DGX Spark / GB10, Jetson AGX Thor,
Jetson Orin/Xavier/Nano) were reporting `available_vram=0` back to the frontend,
so the Nodes UI showed the node as fully used even when most of the unified
memory was actually free.
Three causes addressed:
* `isTegraDevice` only matched `/sys/devices/soc0/family == "Tegra"`. DGX Spark
(SBSA) reports JEDEC codes there instead — `jep106:0426` for the NVIDIA
manufacturer — so the Tegra/unified-memory fallback never ran. Renamed to
`isNVIDIAIntegratedGPU` and extended to also match `jep106:0426[:*]` via
`/sys/devices/soc0/soc_id`.
* The unified-iGPU code defaulted the device name to `"NVIDIA Jetson"` when
`/proc/device-tree/model` was missing. That's what happens for Thor inside a
docker container, and always on DGX Spark. New `nvidiaIntegratedGPUName`
resolves via dt-model → `/sys/devices/soc0/machine` → `soc_id` lookup
(`jep106:0426:8901` → `"NVIDIA GB10"`) so the Nodes UI labels the box
correctly.
* Worker heartbeat sent `available_vram=0` (or total-as-available) when VRAM
usage was momentarily unknown — e.g. when `nvidia-smi` intermittently failed
with `waitid: no child processes` under containers without `--init`. Each
such heartbeat overwrote the DB and made the UI flip to "fully used".
`heartbeatBody` now omits `available_vram` in that case so the DB keeps its
last good value.
Also updates the commented GPU blocks in both compose files with
`NVIDIA_DRIVER_CAPABILITIES=compute,utility`, `capabilities: [gpu, utility]`,
and `init: true`, and documents the requirement in the distributed-mode and
nvidia-l4t pages. Without `utility`, NVML/`nvidia-smi` are absent inside the
container, which is what put the DGX Spark worker into the buggy fallback in
the first place.
Detection verified on live hardware (dgx.casa / GB10 and 192.168.68.23 / Thor)
by running a cross-compiled probe of the new helpers on both host and inside
the worker container.
Assisted-by: Claude:opus-4.7 [Claude Code]
* feat: add distributed mode (experimental)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix data races, mutexes, transactions
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactorings
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix events and tool stream in agent chat
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* use ginkgo
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(cron): compute correctly time boundaries avoiding re-triggering
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* enhancements, refactorings
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* do not flood of healthy checks
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* do not list obvious backends as text backends
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* tests fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop redundant healthcheck
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* enhancements, refactorings
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: extract reasoning to its own package
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* make sure we detect thinking tokens from template
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Allow to override via config, add tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
An example output of `rocm-smi --showproductname --showmeminfo vram --showuniqueid --csv`:
```
device,Unique ID,VRAM Total Memory (B),VRAM Total Used Memory (B),Card Series,Card Model,Card Vendor,Card SKU,Subsystem ID,Device Rev,Node ID,GUID,GFX Version
card0,0x9246____________,17163091968,692142080,Navi 21 [Radeon RX 6800/6800 XT / 6900 XT],0x73bf,Advanced Micro Devices Inc. [AMD/ATI],001,0x2406,0xc1,1,45534,gfx1030
card1,N/A,67108864,26079232,Raphael,0x164e,Advanced Micro Devices Inc. [AMD/ATI],RAPHAEL,0x364e,0xc6,2,52156,gfx1036
```
Total memory is actually showed before the total used memory as can be seen in https://github.com/LostRuins/koboldcpp/issues/1104#issuecomment-2321143507.
This PR fixes https://github.com/mudler/LocalAI/issues/7724
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: default to 10seconds of watchdog if runtime setting is malformed
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: use gosigar for RAM estimation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
