* fix(downloads): increase retry attempts and backoff for large file downloads
* fix download retry config and abort handling
* use abort reason to detect user-initiated cancels
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
```
* feat(downloads): rich progress, friendly names, cancel, and live status
Redesign the Active Downloads UI with four improvements:
- Rich progress: BullMQ jobs now report downloadedBytes/totalBytes instead
of just a percentage, showing "2.3 GB / 5.1 GB" instead of "78% / 100%"
- Friendly names: dispatch title metadata from curated categories, Content
Explorer library, Wikipedia selector, and map collections
- Cancel button: Redis-based cross-process abort signal lets users cancel
active downloads with file cleanup. Confirmation step prevents accidents.
- Live status indicator: green pulsing dot with transfer speed for active
downloads, orange stall warning after 60s of no data, gray dot for queued
Backward compatible with in-flight jobs that have integer-only progress.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(downloads): fix cancel, dismiss, speed, and retry bugs
- Speed indicator: only set prevBytesRef on first observation to prevent
intermediate re-renders from inflating the calculated speed
- Cancel: throw UnrecoverableError on abort to prevent BullMQ retries
- Dismiss: remove stale BullMQ lock before job.remove() so cancelled
jobs can actually be dismissed
- Retry: add getActiveByUrl() helper that checks job state before
blocking re-download, auto-cleans terminal jobs
- Wikipedia: reset selection status to failed on cancel so the
"downloading" state doesn't persist
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(downloads): improve cancellation logic and surface true BullMQ job states
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Jake Turner <jturner@cosmistack.com>
The log2 normalization formula `50 * (1 + log2(ratio))` produces negative
values (clamped to 0) whenever the measured value is less than half the
reference. For example, a CPU scoring 1993 events/sec against a 5000
reference gives ratio=0.4, log2(0.4)=-1.32, score=-16 -> 0%.
Fix by dividing log2 by 3 to widen the usable range. This preserves the
50% score at the reference value while allowing below-average hardware
to receive proportional non-zero scores (e.g., 28% for the CPU above).
Also adds debug logging for CPU sysbench output parsing to aid future
diagnosis of parsing issues.
Fixes#415
Model downloads that fail (e.g., when Ollama is too old for a model)
were silently retrying 40 times with no UI feedback. Now errors are
broadcast via SSE and shown in the Active Model Downloads section.
Version mismatch errors use UnrecoverableError to fail immediately
instead of retrying. Stale failed jobs are cleared on retry so users
aren't permanently blocked.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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>
Failed download jobs persist in BullMQ forever with no way to clear
them, leaving stale error notifications in Content Explorer and Easy
Setup. Adds a dismiss button (X) on failed download cards that removes
the job from the queue via a new DELETE endpoint.
- Backend: DELETE /api/downloads/jobs/:jobId endpoint
- Frontend: X button on failed download cards with immediate refresh
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Three bugs caused downloads to hang, disappear, or leave stuck spinners:
1. Wikipedia downloads that failed never updated the DB status from 'downloading',
leaving the spinner stuck forever. Now the worker's failed handler marks them as failed.
2. No stall detection on streaming downloads - if data stopped flowing mid-download,
the job hung indefinitely. Added a 5-minute stall timer that triggers retry.
3. Failed jobs were invisible to users since only waiting/active/delayed states were
queried. Now failed jobs appear with error indicators in the download list.
Closes#364, closes#216
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.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>
Rotate the HMAC secret used for signing benchmark submissions to the
community leaderboard. The previous secret was compromised (hardcoded
in open-source code and used to submit a fake leaderboard entry).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Fixes 4 high-severity findings from a comprehensive security audit:
1. Path traversal on ZIM file delete — resolve()+startsWith() containment
2. Path traversal on Map file delete — same pattern
3. Path traversal on docs read — same pattern (already used in rag_service)
4. SSRF on download endpoints — block private/internal IPs, require TLD
Also adds assertNotPrivateUrl() to content update endpoints.
Full audit report attached as admin/docs/security-audit-v1.md.
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>
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>
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>
