* feat: replace legacy Kolibri image default with latest v19 image
* feat(supply-depot): add content migration instructions for Edu Platform Gen 1 to 2
Let users override an app's "Open" link with a reverse-proxy or
local-DNS address (e.g. https://jellyfin.myhomelab.net). Falls back to
the default host+port when unset. Metadata-only — no container changes.
Adds a self-update path for the NOMAD admin/core image that runs without a
human in the loop, gated by layered safety checks. Recreation remains the
sidecar's job; this only decides *whether* to update now and requests it.
An hourly job (AutoUpdateJob) evaluates a side-effect-free decision pipeline
(AutoUpdateService.evaluate):
- opt-in: disabled by default
- in a user-configured local-time window (handles midnight wrap)
- an eligible release: same major (major bumps stay manual), strictly newer,
past a configurable cool-off, GA only (no drafts/prereleases), strict semver
- pre-flight: sidecar present, no in-flight system update / downloads / app
installs, and sufficient host disk (estimated from the registry manifest)
When all pass, it drives SystemUpdateService.requestUpdate() with a vetted tag.
Failure backoff auto-disables after 3 genuine update-request failures; transient
release-lookup errors are skips (offline-first appliances are routinely without
connectivity). Re-enabling clears the backoff state.
Settings UI exposes the toggle, window, and cool-off, with live status (eligible
target, in/out of window, last result/error). A `node ace auto-update:dry-run`
command exercises the full pipeline — and a deterministic --scenarios suite the
pure decision logic — without ever triggering an update.
New KVStore keys under autoUpdate.* hold config + last-run state.
Jake noted that `inspect.State.StartedAt` could be missing/malformed,
which would land NaN inside `container.logs({ since, until })`. Add
defensive validation that the parsed timestamp is finite and positive
before using it, with a fallback to the previous tail:500 strategy
(plus a warn log) when it isn't. Happy path is unchanged.
Two related fixes to make the System Information page reliably show real
GPU info instead of misleading lspci BAR0 readings or N/A.
1. Generalize bogus-VRAM detection to AMD.
Same root cause as #835 (NVIDIA showing 32 MB), this time for AMD: lspci
parses the first PCI memory Region (BAR0, typically 1-16 MiB on Navi
cards) as `vram`. On NOMAD8 (Threadripper 3960X + Radeon RX 6800), the
System Information page showed "1 MB" instead of "16 GB". PR #850 fixed
this for NVIDIA by clearing the bogus value and re-running the Ollama
log probe; the check was vendor-gated to NVIDIA only.
`isBogusNvidiaVram` becomes `isBogusDgpuVram` with a `isDiscreteGpuVendor`
helper matching /nvidia|advanced micro devices|amd|ati/i. Same 256-MiB
threshold — no real discrete GPU has less than that, while Intel iGPUs
(which legitimately report small shared-memory VRAM via lspci) are left
untouched. The probe gate condition is similarly renamed.
2. Read Ollama logs from the startup window, not tail:N.
`getOllamaInferenceComputeFromLogs()` was reading the last 500 log lines
and grepping for the "inference compute" line. That line is written once
during Ollama's GPU discovery phase within seconds of startup. Under
active embedding workloads we measured >1000 log lines/min, which pushes
the line past any reasonable tail within minutes — at which point the
probe returns null and the UI flips to "GPU Not Accessible" even though
Ollama is happily using the GPU (size_vram > 0 in /api/ps).
Switch from `tail: 500` to `since: containerStartedAt, until:
containerStartedAt + 300s`. The 5-minute window is bounded regardless of
container uptime and always captures Ollama's GPU discovery output. The
inference-compute line is emitted in the first few seconds of startup, so
5 min is generous headroom.
Validated on NOMAD8 (RX 6800, container uptime ~10 min with sustained
ingestion that generated 6,345 log lines):
Before:
controllers[0]: { model: "Navi 21 ...", vram: 1 }
After (bogus AMD VRAM cleared, log probe stale due to tail:500 churn):
controllers[0]: { model: "Navi 21 ...", vram: null }
gpuHealth: { status: "passthrough_failed" }
-> UI shows "N/A" and the banner from PR #208
After (bogus cleared + log probe reads startup window):
controllers[0]: { model: "AMD Radeon RX 6800", vram: 16384 }
gpuHealth: { status: "ok", hasRocmRuntime: true, ollamaGpuAccessible: true }
-> UI shows "16 GB", no banner
Both branches of the fix exercise correctly: NVIDIA path unchanged
(same code, just renamed identifiers), AMD path now triggers the probe
and the probe reliably finds the GPU info regardless of container age.
PR #804 added pciutils to the admin image so AMD detection could fall
back to lspci. Side effect for NVIDIA: si.graphics() now finds the card
via lspci but reads the BAR0 region size (16-32 MiB on most NVIDIA
cards) as VRAM, since nvidia-smi isn't installed in the admin image to
enrich the result. A GTX 1050 Ti showed 32 MB instead of 4096.
The nvidia-smi-via-Ollama and Ollama log probes already give the right
number, but they only ran when graphics.controllers came back empty.
Extend the trigger so they also run when the only entries are NVIDIA
controllers reporting under 256 MiB (no real dGPU has that little). If
the probes can't reach a value either (Ollama not installed, passthrough
broken), VRAM now falls back to N/A instead of the bogus 32.
Verified locally on an RTX 4070 Ti by simulating the container condition
(lspci available, nvidia-smi unreachable). Before the fix: vram 32, model
"AD104 [GeForce RTX 4070 Ti]". After: vram 12288, model "NVIDIA GeForce
RTX 4070 Ti", from the Ollama inference-compute log line. Also confirmed
the same result inside the actual admin Docker image.
* feat(AI): re-enable AMD GPU acceleration for Ollama via ROCm + HSA override
Re-enables AMD GPU support that was disabled in 77f1868 pending validation
of the ROCm image and device discovery. Validation done 2026-04-28 on a
Minisforum UM890 Pro (Ryzen 9 PRO 8945HS + Radeon 780M iGPU) — Ollama
correctly offloaded all model layers to the iGPU when the container was
started with /dev/kfd + /dev/dri passthrough and HSA_OVERRIDE_GFX_VERSION=11.0.0.
On llama3.2:1b, GPU inference ran at 51.83 tok/s vs 33.16 tok/s on CPU
(same hardware, same prompt) — a 1.56x speedup confirmed by Ollama logs
showing "load_tensors: offloaded 17/17 layers to GPU".
Changes
-------
docker_service.ts
- Restore _discoverAMDDevices() (simplified — pass /dev/dri as a directory
entry, mirroring `docker run --device /dev/dri` behavior, instead of the
prior brittle hardcoded card0/renderD128 fallback that broke on systems
where the AMD GPU enumerates as card1+).
- Restore the AMD branch in _createContainer():
- Switches Ollama image to ollama/ollama:rocm
- Mounts /dev/kfd + /dev/dri via Devices
- Sets HSA_OVERRIDE_GFX_VERSION=11.0.0 (required for unsupported-but-RDNA3
iGPUs like gfx1103; harmless on supported discrete cards)
- KV opt-out via ai.amdGpuAcceleration (default on)
- Mirror the AMD branch in updateContainer():
- Lifted GPU detection above docker.pull() so AMD updates pull :rocm
rather than the standard :targetVersion tag (per-version ROCm tags
aren't always published)
- Replaces stale HSA_OVERRIDE in the inspect-captured env on update,
so containers built before this PR pick up the current value
system_service.ts
- New getOllamaInferenceComputeFromLogs() — parses Ollama startup log line
"msg=\"inference compute\" ... library=CUDA|ROCm ..." which Ollama emits
for both NVIDIA and AMD. Catches silent CPU fallback (e.g. NVML death
after update, or HSA_OVERRIDE failure) that the prior nvidia-smi exec
probe couldn't detect.
- gpuHealth refactored to use log parsing as the primary probe for both
vendors, with nvidia-smi exec retained as the NVIDIA-only secondary
path for hardware enrichment when log parsing has no startup line yet.
- AMD path uses gpu.type KV value (persisted by DockerService._detectGPUType)
+ ai.amdGpuAcceleration opt-out to determine hasRocmRuntime.
types/system.ts
- GpuHealthStatus extended additively: hasRocmRuntime + optional gpuVendor.
types/kv_store.ts
- New ai.amdGpuAcceleration boolean (default-on).
settings/models.tsx, settings/system.tsx
- passthrough_failed banner copy now reads vendor from gpuHealth.gpuVendor
("an AMD GPU" vs "an NVIDIA GPU"). Same Fix button hits the same
force-reinstall endpoint, which now configures AMD correctly.
install_nomad.sh
- AMD detection in verify_gpu_setup() upgraded from a strict-positive
"ROCm not currently available" message to "ROCm acceleration will be
configured automatically." Also tightens the lspci match to display
controller classes (avoids false positives from AMD CPU host bridges,
matching the same fix already in DockerService._detectGPUType).
Auto-remediation
----------------
Issue #755 proposes auto-remediation when gpuHealth.status flips to
passthrough_failed (today the user has to click "Fix: Reinstall AI
Assistant"). When that PR lands, AMD coverage falls out for free since
this PR uses the same passthrough_failed status code via the shared
gpuHealth machinery — #755's guard will need to flip from
hasNvidiaRuntime === true to (hasNvidiaRuntime || hasRocmRuntime).
Closes#124 (AMD GPU support).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(AI): detect AMD GPU presence inside admin container via marker file
The admin container doesn't have lspci installed, and AMD GPUs don't register
a Docker runtime the way NVIDIA does — so DockerService._detectGPUType() and
SystemService.gpuHealth had no way to know an AMD GPU was present.
The previous implementation fell through to lspci, which silently failed inside
the admin container, leaving gpu.type unset and gpuHealth stuck at 'no_gpu'
even on systems with an AMD GPU. (NVIDIA worked because Docker registers the
nvidia runtime, which is reachable via dockerInfo.Runtimes from any container.)
Discovered while testing the AMD acceleration patch on a Minisforum UM890 Pro:
the AMD branch in _createContainer() never fired because _detectGPUType()
returned 'none' even on a host with a working /dev/kfd.
Fix
---
install_nomad.sh writes the host-detected GPU type ('nvidia' | 'amd') to a
marker file in the storage volume the admin container already bind-mounts:
/opt/project-nomad/storage/.nomad-gpu-type → /app/storage/.nomad-gpu-type
DockerService._detectGPUType() reads the marker as a secondary probe (after
the Docker runtime check) — covers AMD detection from inside the container
without requiring lspci or a /dev bind mount.
SystemService falls back to the marker file when KV gpu.type is empty so the
System page reflects AMD presence even before the user installs AI Assistant
for the first time. (Without this, the page would say 'no_gpu' until Ollama
was installed, even on hosts with an AMD GPU detected at install time.)
Verified on NOMAD6 (UM890 Pro, Ubuntu 24.04, 780M iGPU): with the marker file
in place and admin restarted, the patch's AMD branch fires correctly on Force
Reinstall AI Assistant. Resulting nomad_ollama runs ollama/ollama:rocm with
/dev/kfd + /dev/dri passthrough and HSA_OVERRIDE_GFX_VERSION=11.0.0; Ollama
logs show 'library=ROCm compute=gfx1100 ... type=iGPU'. NOMAD's in-product
benchmark on the same hardware climbed from 33.8 tok/s (CPU) to 57.3 tok/s
(GPU) — a 1.69x speedup, with TTFT dropping from 148ms to 66ms.
Migration for existing AMD installs
-----------------------------------
Users on an existing NOMAD install with an AMD GPU have no marker file (the
install script wrote it on a fresh install). Two paths get them on the GPU:
1. Re-run install_nomad.sh — writes the marker, no other side effects
2. Manually: echo amd | sudo tee /opt/project-nomad/storage/.nomad-gpu-type
Either then triggers AMD detection on the next AI Assistant install/reinstall.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(AI): pull ollama/ollama:rocm separately when AMD branch overrides image
The pull-if-missing logic in _createContainer ran against service.container_image
(the DB-pinned tag, e.g. ollama/ollama:0.18.2). The AMD branch then overrode
finalImage to ollama/ollama:rocm — but if that image wasn't already local, the
container creation step failed with "no such image: ollama/ollama:rocm".
Caught while validating on NOMAD2 (Ryzen AI 9 HX 370 + Radeon 890M / RDNA 3.5):
the prior end-to-end test on NOMAD6 had silently passed because the rocm image
was already pulled there from an earlier sidecar test, masking the bug.
Fix: inside the AMD branch, after setting finalImage to ollama/ollama:rocm,
run a parallel _checkImageExists + docker.pull dance for the new tag.
Also confirmed via this validation: the same HSA_OVERRIDE_GFX_VERSION=11.0.0
override works on the 890M (gfx1150 / RDNA 3.5) — Ollama logs report
'library=ROCm compute=gfx1100 description="AMD Radeon 890M Graphics"' and
inference runs at 51.68 tok/s (matching the existing X1 Pro published tile
of 51.7 tok/s on the same hardware class). RDNA 3 (780M, gfx1103) and RDNA
3.5 (890M, gfx1150) both use the same override successfully.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* build(Dockerfile): include pciutils for lspci gpu detection fallback
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: Jake Turner <jturner@cosmistack.com>
Add a "Debug Info" link to the footer and settings sidebar that opens a
modal with non-sensitive system information (version, OS, hardware, GPU,
installed services, internet status, update availability). Users can copy
the formatted text and paste it into GitHub issues.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Ollama can silently run on CPU even when the host has an NVIDIA GPU,
resulting in ~3 tok/s instead of ~167 tok/s. This happens when Ollama
was installed before the GPU toolkit, or when the container was
recreated without proper DeviceRequests. Users had zero indication.
Adds a GPU health check to the system info API response that detects
when the host has an NVIDIA runtime but nvidia-smi fails inside the
Ollama container. Shows a warning banner on the System Information
and AI Settings pages with a one-click "Reinstall AI Assistant"
button that force-reinstalls Ollama with GPU passthrough.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Inside Docker, systeminformation reports the container's Alpine Linux
distro, container ID as hostname, and no GPU. This enriches the System
Information page with actual host details via the Docker API:
- Distribution and kernel version from docker.info()
- Real hostname from docker.info().Name
- GPU model and VRAM via nvidia-smi inside the Ollama container
- Graphics card in System Details (Model, Vendor, VRAM)
- Friendly uptime display (days/hours/minutes instead of minutes only)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Updates the Dashboard to use the same user-friendly names as the Easy Setup
Wizard, giving credit to the open source projects powering each capability:
- Kiwix → Information Library (Powered by Kiwix)
- Kolibri → Education Platform (Powered by Kolibri)
- Open WebUI → AI Assistant (Powered by Open WebUI + Ollama)
- FlatNotes → Notes (Powered by FlatNotes)
- CyberChef → Data Tools (Powered by CyberChef)
Also reorders Dashboard cards to prioritize Core Capabilities first, with
Maps promoted to Core Capability status, followed by Additional Tools,
then system items (Easy Setup, Install Apps, Docs, Settings).
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
jakeaturner
Jake Turner
akashsalan
Chris Sherwood
Ben Gauger
chriscrosstalk
Henry Estela