A wedged LM Studio daemon cost CI job 105558224377 three hours: three kills at
the global 60-minute `CONTAINER_EXEC_TIMEOUT`, for a 531 MB model that a healthy
node pulls in 27 seconds. Nothing in the path was sized against the thing being
fetched, and nothing checked whether it could land at all.
Size. Each model now declares `bytes`, taken from the value the deploy already
printed as "OK (531068224 bytes)", and the pre-pull derives its own timeout per
model: max(preload_floor_seconds, bytes / preload_min_bytes_per_second). The
floor of 600s protects a small model from a brief stall; the rate of 1 MB/s sits
twenty times below the 21.98 MB/s a healthy pull reached and seven thousand
times above the 142 B/s the wedged one managed, so it separates the two without
guessing. Evaluated against ansible-core's filters: 531 MB and 10 MB both land
on the floor, 4 GB gives 4000s.
Disk. The seed measures free space with `df -P -B1` before fetching and refuses
with a message naming both figures in GiB, the factor and the file to edit. The
factor is three because the node cache, the staging copy inside the container
and the models volume all exist at once during the import; that is measured, not
padding.
Fallback. A model may list `mirrors`, extra bases tried after the source. The
mirror task is a no-op once the source delivered, because get_url skips a dest
whose checksum already matches: measured at 145.24s for the first pass against
0.64s for the second. The list ships empty, because the obvious candidate is
not one: hf-mirror.com answers 206 but resolves to us.aws.cdn.hf.co, so it is a
front end for HuggingFace and adds no redundancy against a HuggingFace outage.
The mechanism is here; an independent copy is the operator's to name.
The external check grew with the declaration: `bytes` is compared against the
size the API publishes, and every declared mirror must still serve the pinned
file, or it is redundancy on paper only.
01_manager_ops.yml's rescue rationale claimed that restarting the container
clears a wedged transfer. Job 105558224377 shows the restart returning rc 0
without clearing it, so the claim now says what was measured instead.
Residual: the mirror path has never run in a deploy, since no mirror is
declared. Its loop expression is verified against ansible-core's subelements
over four shapes, including the zero-iteration case when nothing is mirrored.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The ranking the last commits built toward is now enforced: a semver tag,
else a digest, and nothing else without a written reason. Two states fail.
A pin that is neither names something the registry can move underneath us,
and nothing here can tell whether it still points where it did when it was
written. A digest on an upstream that does publish semver takes the
fallback for no gain and hides the entry from the update job.
Thirteen entries stood in the way when the check was first run. Four were
resolved by the channel grammar, which made the Jitsi components orderable
without touching their pins. Three needed nothing once a moving tag could
declare itself with 'docker-version'. Two are fixed here and four are
declared.
LiteLLM leaves 'main-v1.77.3.dynamic_rates'. That tag exists exactly once
among 2609, so it is a dead end no updater can follow, and it misreports
itself: the image inside answers 1.77.2. The pin moves to
'main-v1.77.7-stable', five patches along the same minor and onto a channel
with 134 builds. Started against this repository's config shape, 1.77.7
registers both model entry forms and accepts drop_params and the
os.environ master key.
Pixelfed leaves 'latest' for '20260917'. The upstream stamps its dates as
one eight-digit number, which is depth 1 and orderable with no tooling
change at all, and the two tags resolve to the same digest, so nothing
about the deployed image moves.
The four declarations carry the count that justifies them rather than a
claim: LM Studio publishes two tags and neither is a version, the MIG
registry carries one, and the two Nextcloud Talk images publish 174 and 104
AIO build stamps between them.
Both branches of the gate were negative-controlled. The digest branch
skips in this repository because no digest pin exists at all, so it was
exercised by introducing one and watching it fail.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
svc-ai-lmstudio had no model-load step at all: tasks/00_core.yml set the
once-flag and the container network, nothing else. The gateway published
lmstudio/default anyway, so the route existed and nothing could answer it.
wp-compose-tor.log:48994 shows the route written into the config, :51770 shows
the warm step reaching qwen2.5:0.5b on Ollama instead, and the green ai-engine
spec ran against that. LM Studio was deployed, published and never prompted.
The pre-pull mirrors the one that works. tasks/01_manager_ops.yml resolves the
hosting node and container id, loops the declared models through
`lms get --yes --gguf`, overlaps them with async/poll and reaps before
playwright, exactly like svc-ai-ollama. That `lms` exists is measured, not
assumed: /app/daemon-run.sh:67 in the image starts ./.bundle/lms server start,
and `lms get --help` documents -y for scripting and --gguf for the format.
Synonymity needs both namespaces in one place. Ollama knows qwen2.5:0.5b, LM
Studio knows qwen2.5-0.5b-instruct, so AI_LOCAL_MODELS carries both per entry:
`alias` is what the gateway publishes and what Ollama pulls, `name` is what
`lms get` resolves. Both roles now reference that one list, and config.yaml.j2
publishes each backend under the alias, routing Ollama to ollama/<alias> and LM
Studio to openai/<name>. The lmstudio loop skips an alias Ollama already
publishes, so two backends never produce a duplicate model_name and Ollama
keeps precedence. lmstudio/default is gone; the model name is now the same
whichever backend answers it.
Ollama's preload_models changes shape from a list of strings to the {alias,
name} entries. Every consumer moves with it: the pull command, the template
loop, and both expressions in 16_ai.yml. An inventory that overrides
`applications.svc-ai-ollama.services.ollama.preload_models` with the old string
list will break at map(attribute='alias'); this is a deliberate shape change,
in the spirit of 7b15c61dfc.
The CI model list is not a property of an installation, so AI_LOCAL_MODELS_CI
lives in inventories/development/default.yml, whose own header scopes that file
to the cross-cutting environment knobs. test_variable_definitions only knew
group_vars, so it reported the constant as undefined; the shipped inventories
are a definition source too, because the deploy CLI bakes them into host_vars,
and the guard's own docstring already claimed "defined somewhere in the
repository".
A latent crash came out of this: `| list | first` raises on an empty sequence,
so a backend deployed without preload models blew up LITELLM_CHAT_MODEL. Ollama
with only embedding models could already hit it; the existing test dodged it by
rendering only _SERVED. `| default('')` now yields the empty string that
_SERVED already reports as false.
Tests: config.yaml.j2 is rendered rather than read, over ollama-only,
lmstudio-only, both and neither, asserting the published model_name set, that
both backends name the same alias, that a shared alias is published once and
routed to Ollama, and that the render is valid YAML. test_litellm_chat_model
gains the lmstudio branch, the no-preload case and the same-model assertion.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Read platform_arch from the role config and append a node.platform.arch
constraint where it is set, so a role whose image exists for one
architecture only cannot be scheduled onto a node that could never run
it. LM Studio ships x86_64 alone and is the first consumer.
The constraint lives with the other placement rules rather than in the
consuming role, because architecture binding is a property any role with
a platform-specific image can carry.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Consumers of the model plane speak one OpenAI-compatible endpoint and
carry one virtual key; which backend answers is the gateway's business.
svc-ai-litellm renders its model_list from the round's actual topology,
so an alias only exists when its backend role is in group_names, and it
emits an explicit empty list when no backend is present at all because
LiteLLM rejects a bare model_list: key.
Keys reach the gateway through the per-consumer credential rather than
being minted there: 01_keys.yml probes /key/info and only POSTs
/key/generate on 404, so reruns stay idempotent. The consumer list is
built in 00_core.yml, not inside the included file, because 01_keys.yml
delegates to the hosting node and delegate_to is templated before when.
The gateway declares ollama and lmstudio as consumer flags so the
network renderer attaches it to its own backends; without them it cannot
reach them.
svc-ai-lmstudio runs the headless llmster daemon behind expose only, so
it is reachable from the gateway but never bound on the host, matching
svc-ai-ollama.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
kevinveenbirkenbachandClaude Opus 5