Every Docker pull went through `followProgress(pullStream, resolve)`, passing
the Promise's resolve as dockerode's onFinished(err, output) callback — so the
error argument was ignored and a failed pull (dropped/metered connection, bad
manifest, registry error, disk full mid-pull) resolved as if it had succeeded.
The code then tried to create/start a container from a missing or partial
image, surfacing a confusing downstream error rather than the real cause. (#790)
Add a DockerService.pullImage() helper that rejects when followProgress reports
an error, and route all five pull sites through it:
- service install
- AMD ROCm image pull
- service update
- force-reinstall / recreate (forcePull)
- sysbench benchmark image pull
Closes#790
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The service update path stopped and renamed the old container aside before
the new one was confirmed running, but only wired up rollback if
createContainer threw or the 5s health check failed. A throw from
newContainer.start() itself (bad device/GPU config, host port already bound,
image incompatibility) bubbled straight to the outer catch, which returned a
generic 400 and never restored the old container, leaving the service down.
Retrying then wedged: the failed new container still held the service name, so
the next attempt's rename to `<name>_old` collided with the leftover from the
first attempt and threw the same error every time.
- Wrap newContainer.start() so a start failure removes the half-created
container and rolls back to the previous one.
- Clear any stale `<name>_old` before renaming so retries can't collide.
- Dedupe the three rollback sites into a single rollbackToOld() helper (also
removes a non-null assertion in the create-failure path that could itself
throw when no `_old` existed).
Refs #949
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
When a service fails to install because something on the host already binds its
port, the user saw the raw dockerode error ("Bind for 0.0.0.0:11434 failed:
port is already allocated"), which is meaningless to a non-technical user. The
most common case is a native Ollama install holding 11434.
Add _humanizeDockerError() to map host port-conflict errors to an actionable
message that names the port and, for Ollama/11434, points at the likely cause
(a host Ollama service) with the commands to stop it. Unrecognized errors pass
through unchanged. Wired into the install failure broadcast and the thrown
error.
Closes#934
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
_applyHostStorageRoot only rewrote binds carrying the current
NOMAD_STORAGE_PATH and short-circuited when env == resolved root. That
left user-modified/custom apps — whose binds freeze the default prefix at
edit time — mounting an empty dir on the documented "set NOMAD_STORAGE_PATH
+ relocate the volume" path (#938).
- Match either the env value or the hardcoded default prefix; drop the
root == seededRoot no-op, excluding root from the candidates instead so
rewrites stay idempotent.
- Add ADMIN_STORAGE_DEST / DEFAULT_HOST_STORAGE_ROOT constants; replace the
cwd-derived storage dest so admin-mount lookup can't silently break.
- Comment why a transient inspect failure is deliberately not cached.
Child services (Kiwix, Ollama, Qdrant, Flatnotes, Kolibri) are created via the
Docker socket, so their bind mounts use a HOST path. That path was baked into
the services table at seed time from NOMAD_STORAGE_PATH (default
/opt/project-nomad/storage) — and NOMAD_STORAGE_PATH wasn't even present in
management_compose.yaml, so it always fell back to the default.
Result: relocating the admin storage volume in compose (e.g.
/mnt/big/storage:/app/storage) moved the admin's own data but left child apps
mounting the old, now-empty /opt/project-nomad/storage. Kiwix would come up
with no content. (#938)
- Add _resolveHostStorageRoot(): inspect the admin's own container, find the
bind backing /app/storage, and use its host Source as the single source of
truth (cached). Falls back to NOMAD_STORAGE_PATH/default if it can't be
inspected.
- Add _applyHostStorageRoot(): rewrite the host-side prefix of each storage bind
to that root. No-op when it already matches the seeded prefix, so default
installs are unaffected.
- Apply it in _createContainer (covers installs + dependency recursion) and in
the Kiwix library-mode recreate path.
- management_compose.yaml: add an explicit NOMAD_STORAGE_PATH knob and rewrite
the comments so the admin volume, env var, and disk-collector volume that must
agree are spelled out.
Closes#938
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add NOMAD-specific getting-started docs for all 9 curated Supply Depot apps,
the catalog/install fixes each one surfaced, and a way to reach the docs from
each app card.
Docs:
- New in-app Markdoc page admin/docs/supply-depot-apps.md covering all 9 apps
(Stirling PDF, File Browser, Calibre-Web, IT Tools, Excalidraw, Homebox,
Vaultwarden, Jellyfin, Meshtastic Web): first run/login, where data lives,
and offline behaviour. Registered in docs_service DOC_ORDER.
- Manage > Docs dropdown item linking each app to its section
(/docs/supply-depot-apps#<anchor>): anchor map in constants/supply_depot_docs.ts,
heading anchors via Markdoc {% #id %}, and hash-scroll on the docs page.
Install / catalog fixes:
- Stirling PDF: open straight to the tools (SECURITY_ENABLELOGIN=false; the old
v1 DOCKER_ENABLE_SECURITY flag was dead).
- File Browser: seed a known admin/nomad login (bcrypt) instead of a random
log-only password; scope visibility to content folders via mount selection and
move the DB out of the browsable root.
- Calibre-Web: bundle an empty Calibre library and seed it on install so setup
doesn't dead-end at db config (_runPreinstallActions__CalibreWeb).
- Homebox: swap the archived hay-kot image for the maintained sysadminsmedia fork.
- Vaultwarden: generate a self-signed cert on install and serve HTTPS by default
(_runPreinstallActions__Vaultwarden + ROCKET_TLS + ui_location https:8480), so
the web vault has the secure context it requires.
- Jellyfin: pre-create storage/media/{Movies,TV Shows,Music,Photos} so each
library points at its own subfolder, avoiding the overlapping-path issue that
silently hides content (_runPreinstallActions__Jellyfin).
- Seeder run() now also syncs ui_location for non-modified curated services, so a
catalog link/scheme/port change reaches existing installs on update.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Closes#855.
PR #804's AMD branch in `updateContainer()` overrode `newImage` to
`ollama/ollama:rocm` and then persisted that literal string to
`service.container_image` (line 1273). Two downstream consequences for
every AMD user who clicked Update on AI Assistant:
1. Apps page (`apps.tsx`) extracts the displayed version from
`container_image` and rendered the literal string "rocm".
2. `ContainerRegistryService.getAvailableUpdates()` parsed `currentTag =
"rocm"`, which isn't semver, so `parseMajorVersion` returned NaN, the
filter didn't reject newer tags by major-version, and `isNewerVersion`
treated any future tag as newer. Result: the same update reappeared
on every check, forever.
Fix: separate "what we run" from "what we persist". `runtimeImage`
holds the tag passed to `docker.pull()` and `createContainer()` (still
`:rocm` for AMD), while `newImage` keeps the semver tag and is the
value written to the DB. Surgical: 3 references renamed plus 1
declaration added.
The install path (`_createContainer`) already had the right shape
(runtime-only override, no DB write of the override), so this PR only
touches `updateContainer`.
Test plan:
- `npm run typecheck` passes locally.
- Manual repro on NOMAD2 (AMD HX 370 / 890M, rc.2): before fix, DB
shows `container_image = ollama/ollama:rocm` after triggering an
Ollama update via Settings > Apps; Apps page shows version "rocm";
`/api/system/services/check-updates` immediately re-reports the same
update available. After fix, DB shows `container_image =
ollama/ollama:<targetVersion>`; Apps page shows the semver; check-
updates does not re-report the same update.
- nomad_ollama container itself still runs the `:rocm` image
(verified via `docker inspect`).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Closes#810.
## Bug A: HSA_OVERRIDE_GFX_VERSION=11.0.0 was unconditional
PR #804 set HSA_OVERRIDE_GFX_VERSION=11.0.0 for any AMD GPU. The inline comment
claimed this was harmless on supported discrete cards (gfx1030 RX 6800, etc.) — empirically false. With the override, Ollama crashes during GPU discovery on
gfx1030 and falls back to CPU silently. Affects every NOMAD user with an
RX 6800 or other RDNA 2 discrete card.
The correct value depends on the gfx version:
- gfx1030, gfx1100, gfx1101, gfx1102: officially supported by ROCm — no override
- gfx1031..gfx1036 (RDNA 2 variants + iGPUs like Rembrandt 680M): 10.3.0
- gfx1103, gfx1150, gfx1151 (Phoenix 780M, Strix 890M, Strix Halo): 11.0.0
### Resolution chain in `_resolveAmdHsaOverride()`
1. KV `ai.amdHsaOverride` — manual override; accepts 'none' to disable, or a
semver-style value to force.
2. Marker file `/app/storage/.nomad-amd-gfx` — written by install_nomad.sh
based on lspci codename. Mapped to override via `_mapGfxToHsaOverride()`.
3. Default: `11.0.0` — preserves prior behavior so existing iGPU users
(780M / 890M, the dominant AMD population today) don't regress on upgrade.
Discrete RDNA 2 users on existing installs can opt out via
`ai.amdHsaOverride='none'` and force-reinstall AI Assistant, OR re-run
install_nomad.sh to refresh the marker file.
The helper is used in both `createContainer` (initial install) and
`updateContainer` (image update) paths, replacing the unconditional push.
## Bug B: BenchmarkService had no AMD discrete detection path
`BenchmarkService.getHardwareInfo()` had three GPU detection fallbacks:
1. `si.graphics()` — empty inside Docker for AMD
2. nvidia-smi — NVIDIA only
3. AMD APU regex from CPU model — integrated only
Result: AMD discrete cards (RX 6800, RX 7900 XTX, etc.) showed up as
"GPU: Not detected" on the leaderboard despite ROCm working. Corrupts
leaderboard data quality for that population.
Fix: after the existing fallbacks, call `SystemService.getSystemInfo()` and
read `graphics.controllers[0].model`. That path already handles AMD via the
marker file + Ollama log probe added in PR #804, so we're reusing existing
plumbing rather than duplicating detection logic.
## install_nomad.sh changes
The existing AMD detection block already runs lspci. Added a codename parse
step that maps Navi 21/22/23/24, Rembrandt, Phoenix1/Phoenix2, Strix/Strix
Point/Strix Halo, and Navi 31/32/33 to gfx versions, then writes
`/opt/project-nomad/storage/.nomad-amd-gfx`. Unknown codenames write nothing
(admin handles missing-marker case via the backward-compat default).
## Validation
Both bugs were originally surfaced and validated empirically on RX 6800 /
gfx1030 / Ubuntu 24.04 + kernel 6.17 + ollama/ollama:rocm during the #810
filing. Validation grid from that report:
| Run | NOMAD Score | tok/s | GPU detected |
|-----------------------------------------------|-------------|-------|-------------------------|
| Pre-fix (Bug A active) | n/a | 0 | yes, but library=cpu |
| HSA_OVERRIDE removed, Bug B unfixed | 73.8 | 221.6 | "Not detected" |
| Both fixes hot-patched (this PR's behavior) | 73.7 | 216.0 | AMD Radeon RX 6800 |
Local checks: `npm run typecheck` clean, `npm run build` clean.
* 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>
New defaults:
OLLAMA_NO_CLOUD=1 - "Ollama can run in local only mode by disabling
Ollama’s cloud features. By turning off Ollama’s cloud features, you
will lose the ability to use Ollama’s cloud models and web search."
https://ollama.com/blog/web-searchhttps://docs.ollama.com/faq#how-do-i-disable-ollama%E2%80%99s-cloud-features
example output:
```
ollama run minimax-m2.7:cloud
Error: ollama cloud is disabled: remote model details are unavailable
```
This setting can be safely disabled as you have to click on a link to
login to ollama cloud and theres no real way to do that in nomad outside
of looking at the nomad_ollama logs.
This one can be disabled in settings in case theres a model out there
that doesn't play nice. but that doesnt seem necessary so far.
OLLAMA_FLASH_ATTENTION=1 - "Flash Attention is a feature of most modern
models that can significantly reduce memory usage as the context size
grows. "
Tested with llama3.2:
```
docker logs nomad_ollama --tail 1000 2>&1 |grep --color -i flash_attn
llama_context: flash_attn = enabled
```
And with second_constantine/deepseek-coder-v2 with is based on
https://huggingface.co/lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF
which is a model that specifically calls out that you should disable
flash attention, but during testing it seems ollama can do this for you
automatically:
```
docker logs nomad_ollama --tail 1000 2>&1 |grep --color -i flash_attn
llama_context: flash_attn = disabled
```
GPU detection results were only applied at container creation time and
never persisted. If live detection failed transiently (Docker daemon
hiccup, runtime temporarily unavailable), Ollama would silently fall
back to CPU-only mode with no way to recover short of force-reinstall.
Now _detectGPUType() persists successful detections to the KV store
(gpu.type = 'nvidia' | 'amd') and uses the saved value as a fallback
when live detection returns nothing. This ensures GPU config survives
across container recreations regardless of transient detection failures.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The previous lspci-based GPU detection fails inside Docker containers
because lspci isn't available, causing Ollama to always run CPU-only
even when a GPU + NVIDIA Container Toolkit are present on the host.
Replace with Docker API runtime check (docker.info() -> Runtimes) as
primary detection method. This works from inside any container via the
mounted Docker socket and confirms both GPU presence and toolkit
installation. Keep lspci as fallback for host-based installs and AMD.
Also add Docker-based GPU detection to benchmark hardware info — exec
nvidia-smi inside the Ollama container to get the actual GPU model name
instead of showing "Not detected".
Tested on nomad3 (Intel Core Ultra 9 285HX + RTX 5060): AI performance
went from 12.7 tok/s (CPU) to 281.4 tok/s (GPU) — a 22x improvement.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add comprehensive benchmarking capability to measure server performance:
Backend:
- BenchmarkService with CPU, memory, disk, and AI benchmarks using sysbench
- Database migrations for benchmark_results and benchmark_settings tables
- REST API endpoints for running benchmarks and retrieving results
- CLI commands: benchmark:run, benchmark:results, benchmark:submit
- BullMQ job for async benchmark execution with SSE progress updates
- Synchronous mode option (?sync=true) for simpler local dev setup
Frontend:
- Benchmark settings page with circular gauges for scores
- NOMAD Score display with weighted composite calculation
- System Performance section (CPU, Memory, Disk Read/Write)
- AI Performance section (tokens/sec, time to first token)
- Hardware Information display
- Expandable Benchmark Details section
- Progress simulation during sync benchmark execution
Easy Setup Integration:
- Added System Benchmark to Additional Tools section
- Built-in capability pattern for non-Docker features
- Click-to-navigate behavior for built-in tools
Fixes:
- Docker log multiplexing issue (Tty: true) for proper output parsing
- Consolidated disk benchmarks into single container execution
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Detect Windows platform and use named pipe (//./pipe/docker_engine)
instead of Unix socket for Docker Desktop compatibility
- Add NOMAD_STORAGE_PATH environment variable for configurable
storage paths across different platforms
- Update seeder to use environment variable with Linux default
- Document new environment variable in .env.example
This enables local development on Windows machines with Docker Desktop
while maintaining Linux production compatibility.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Chris Sherwood
jakeaturner
chriscrosstalk
Luís Miguel
Jake Turner
Henry Estela
Benjamin Sanders
builder555