The queue shipped with no tests despite having produced four bugs during
development, which is the wrong order. 21 tests now cover the parts whose failure
modes are not obvious from reading the code: ordering by priority then FIFO, that
depth is genuinely unbounded and survives a restart, that running work is never
cancelled, that a job abandoned mid-run is requeued rather than left RUNNING
forever, and that an LLM job's VRAM requirement comes from its own model rather
than a tenant-wide figure -- the mistake that dispatched a 14.9 GB model into 8 GB
of free memory and killed llama-server three times.
The dashboard had no view of the queue at all, so a stuck queue was
indistinguishable from an empty one. The Job Queue panel shows what is running and
what it released to get there, pending jobs in execution order with their wait time
and a cancel control, and -- when the scheduler is blocked -- how long it has been
waiting and why.
MCP gains queue_job, get_job_queue and cancel_job, so an agent can line work up
rather than firing a request and hoping the GPU is free.
Tests: 271.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The engine subtitles were hardcoded: "FlashAttention + Q4 KV Cache" and "DynamicVRAM
+ Pinned Async Offload". The first turned out to be accurate -- OLLAMA_FLASH_ATTENTION
and OLLAMA_KV_CACHE_TYPE really are set -- which is worse than being wrong, because it
would have gone on looking accurate after the settings changed.
engines.py reads both engines' real configuration: the ollama service environment via
systemd, and ComfyUI's own /system_stats for version, allocator, VRAM mode and argv.
Exposed at GET /api/engines, as an MCP tool, and in the dashboard subtitles with the
full settings list as a tooltip.
The settings worth surfacing are the ones that dictate how this service must behave
and that previously had to be discovered by reading journald: OLLAMA_NUM_PARALLEL=1
is why an unload queues behind a running generation and is reported as deferred
rather than failed, and OLLAMA_MAX_LOADED_MODELS=1 is why every swap evicts the
previous model. Each is reported with that explanation attached.
Writing the tests found a bug in the new code: (system.get("python_version") or
"").split()[0] raises IndexError when ComfyUI omits the field, and the surrounding
except would have swallowed it and reported ComfyUI as entirely offline.
Tests: 192 (was 182).
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Self-check. Fan control failed for an entire session -- recoverably, and completely
invisibly. It appeared once, inside one field of one log line, and nothing ever
asked whether fan control worked. health.py now checks everything this service
depends on (NVML, passwordless sudo for nvidia-smi, fan control via the headless X
server, overclock drift, the telemetry store, residency measurement capability,
model directories, the ComfyUI websocket, and both upstream HTTP services) and
reports for each one what is broken, what that breaks, and how to fix it. Exposed at
GET /api/health, as an MCP tool, and as a dashboard panel that collapses to a badge
when healthy and expands to impact-and-fix when not. Current state: 9 ok, 1 degraded
(the known cachestat permission limit on Ollama's blobs).
A self-check that returns ok while a dependency is broken is worse than none, so the
tests drive each check to its failure state -- including the exact "Error resolving
target specification 'gpu:0'" string from the original incident -- and assert that a
check which raises surfaces as failed rather than taking down the endpoint.
SSE payload. The installed-model catalog was 10.6 KB of a 13.1 KB frame, 81% of the
stream, re-sent to every subscriber every second despite changing only when a model
is pulled or removed: 135 MB/hour across three tabs. It is now sent on a
subscriber's first frame and whenever the set changes; the client keeps the last
known list. Steady-state frames dropped from 14041 to 3664 bytes, a 74% reduction,
and /api/stats still returns the complete snapshot for API consumers.
Tests: 182 (was 169).
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Calibration. The same 12.87GB model loaded through Ollama on this box:
3.1% resident (FADV_DONTNEED) -> 34.3s -> 0.38 GB/s
100% resident (force-warmed) -> 4.9s -> 2.63 GB/s
The thresholds had been guessed from PCIe bus bandwidth: cache hit at >=5 GB/s. A fully
warm load only reaches 2.63 GB/s, because load_duration covers host-to-device transfer
and model init as well as the file read -- the page cache itself reads at 6.4 GB/s. The
5 GB/s bar was therefore unreachable, and every warm load was being reported as a
partial hit. Now 2.0 / 0.8 GB/s, either side of the measured 6.9x separation.
Warm-skip was also unsafe. A 12.87GB blob was skipped as already resident on the
strength of twelve 2MB probe windows, then loaded at 2.44 GB/s. Skipping now requires
warm_confident: an exact cachestat reading, or a probe finding every one of 32 denser
samples resident. warm_file_to_ram/warm_ollama_blob take force=True, exposed on the
warm-model endpoint, whose Pydantic model was missing the field entirely.
MCP parity: the server had drifted well behind the REST API. Adds tools for measured
residency, warm planning, VRAM requests, per-profile analytics, thermal governor
control, overclock status/apply/restore, and autotune sweeps plus status -- 23 tools
and 6 resources, up from 12 and 3. The telemetry store now starts in __main__ rather
than at import scope, since server.py imports this module for the benchmark tool.
README: replaced the remaining theoretical claims (31.5 GB/s bus rate, sub-1.5s loads,
15ms yields) with the measured numbers.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
