Conflicts: docs.go (manual, OpenAPI and suites all kept), modelserve Job
(trace and eval both kept). The eval routes are added to the OpenAPI route
table, update and promote documented with their optional body, and the
document regenerated.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A suite is a set of cases in YAML or JSON lines, each scored by exact,
contains, regex, a number within a tolerance, a JSON Schema subset, or a
judge model with a rubric; weighed into one score against a threshold.
internal/bench/eval runs one on the control plane through Router.Replay, as
the gateway's own requests, and keeps every run and its cases in the store
(migration 0028: eval_runs, eval_cases).
- llmgw ctl eval MODEL --suite FILE|NAME, --history, --run, --suites; exits
non-zero below the score. Suites by name from control.eval_dir, or the
two built in from deploy/eval (arithmetic, json-format).
- model update and promote take --eval SUITE --min-score N, or a model's
standing gate (ctl model gate, eval: in the catalog): a promote asks the
canary first and leaves it below the score; an update tries the new
weights as a canary given no share, then promotes or removes it.
- The console's model menu: ask a suite with its progress, its runs, its
gate.
- The audit middleware no longer hands a handler a body cut off at 64 KiB.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Discovery reports a card in MIG mode as its instances instead of the whole
card, on both paths: nvidia-smi (-L names them, -q -x gives their memory,
ids and processes) and NVML (GetMigMode, GetMaxMigDeviceCount,
GetMigDeviceHandleByIndex, behind an interface so it is tested with a fake).
Each instance has its MIG UUID, its own memory, and a class from the card's
and the profile: a100-80g-mig-3g.40gb. Placement numbers instances
1000 + 100 x card + instance and every view shows them as card/migN.
A replica on MIG runs on exactly one instance: a process opens one MIG
device and instances have no NVLink between them, so tp, pp, gpus or nodes
above one on a MIG class are refused in catalog validation and again in
placement, and the planner offers one instance or none. The agent gives
the engine CUDA_VISIBLE_DEVICES=MIG-<uuid> (no Vulkan device variable on
MIG). Memory and processes are per instance; heat, power and wear the
card's; utilisation is left out rather than reported as zero. A card in MIG
mode with nothing carved stays out of placement with the reason, and
llmgw doctor reports each card's mode and instances and warns of the states
in which nothing can run. ctl nodes, ctl status, the console and the plan
count devices by class. docs/install.md has a MIG section.
Written against fakes of nvidia-smi and NVML output; not yet run on MIG
hardware.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
llmgw keys cache --project P --ttl 1h [--any] | --off, and Cache answers… on
the console's project menu. The same request — project, endpoint, canonical
model and the body without stream, stream_options, user, metadata, store and
service_tier, keys sorted — is answered from the gateway's memory for the
TTL. Only reproducible answers (temperature 0 or a seed; embeddings, rerank,
score) unless --any; only a whole 200 from the model asked for, not a
fallback's, a canary's, or one a guardrail changed.
Looked up after the limits and the input guardrails; the output policy
judges a reused answer; no_store and stored conversations bypass it.
Streamed answers are kept put back together and streamed back as chunks.
x-llmgw-cache: hit|miss|bypass, Age on a hit, Cache-Control: no-cache or
x-llmgw-cache: no to bypass. A hit is recorded with no tokens and no cost,
marked cache_hit (migration 0027) and shown by ctl request. An LRU bounded
by router.cache.max_bytes and max_entry_bytes; llmgw_cache_requests_total,
llmgw_cache_bytes, llmgw_cache_entries.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
adminapi.Routes lists every endpoint with its query parameters and Go values
of the bodies it reads and answers with; internal/docgen/openapi turns that
into docs/admin-api.openapi.json by reflection, the way encoding/json encodes
the types, with the fields' comments as descriptions. A test fails when an
endpoint is registered and not in the table, another when the committed
document is stale. The binary embeds it and serves it with the token.
Handlers that wrote map literals and decoded anonymous structs now use named
types, so the document can see them. GET /api/v1/models/{name} answers with a
model's whole definition as it is in force, which a tool managing it needs.
docs/admin-api.md: reaching the API, the token, what a request must carry.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
notify.webhooks sends signed JSON or Slack-compatible messages, queued,
retried, de-duplicated hourly; ctl notify test checks each webhook.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Monitor draws CPU, memory, GPUs, requests, tokens, latency, network and disk
as Braille charts over 5m/1h/24h/7d, then per node, card and model
sparklines; node and model pages have their own. History is read from
monitoring.prometheus through POST /api/v1/metrics/range, or without it
from what the console saw. The control plane serves the cluster's machines
for Prometheus http_sd at /prometheus/targets on its metrics port, and node
add installs prometheus-node-exporter.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The console starts a serve or update job over the admin API and stays,
instead of closing to follow the download. Jobs lists them with their
progress and steps; the sidebar counts those running. ctl model serve and
update take --detach; ctl jobs lists jobs and follows one.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The control plane reads the model's shape from one GGUF header or its
config.json and lists, for every file, the best way it runs here: one card,
several of a node's (by layer for llama.cpp, tensor-parallel for vLLM),
several nodes' over one fabric (vLLM, pipeline-parallel between them), or a
CPU node; what each card holds and whether it is free now. ctl model plan
prints it; model serve takes its file and way unless told, gives a GGUF on
GPUs a GPU profile, and with --fallbacks adds the next best ways. The
console shows it per file and asks for the way and the fallback.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The mirror keeps where it fetched a file beside it, and reports that and the
checksum after it restarts. A model whose files the mirror cannot trace is
found on the registry: a repository holding a file of that name that has the
commit the mirror's directory is named after, with every file at the same
size, the most downloaded first. ctl model origin and the console set one by
hand, checked the same way. Commits compare shortened.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A model served from the registry records its origin: repository, commit and
each file's checksum; one served before is worked out from the mirror's
record of where it fetched the file. Every mirror.update_check (6h) the
control plane asks whether those files changed there. ctl model updates
lists it, ctl model update fetches beside the old files, checks, swaps and
replaces replicas one at a time, ctl model rollback goes back. The console
shows it on Models and offers the same.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
init --patroni user@host ... installs PostgreSQL, Patroni and etcd on the
given machines over ssh, with this one as the third etcd vote, makes the role
and database, and moves the store in use into the cluster. Each step checks
first, so it changes nothing on a cluster that is up. For a store of several
hosts the control plane publishes each member's role, state and lag from
Patroni, a stopped member as missing, prints it in ctl status, and alerts on
it. Making an administrator on PostgreSQL sent a bool to an integer column;
it sends an integer now.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A node that cannot run the CUDA 13 build installs the same version's
CUDA 12.9 build from vLLM's GitHub release, with PyTorch's cu129 torch;
the install computes on the GPU before it counts. The build can be
chosen per node — auto, cu13, cu129, none — on the cluster page, with
ctl engine-build, or over the admin API; the control plane keeps the
choice and tells the node at once.
Refs #2.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A restart with no room beside it waited the drain grace period before
stopping the old replica, and stopped it even when the replacement
would fit nowhere. It now stops at once when the replica alone blocks
its replacement, gives the restart up (saying why) when nothing would
fit, and ctl restart --in-place stops first on request.
Refs #2 (item 3).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
engine_overrides give nodes picked by GPU class, id or labels their own
engine versions; node add --disable-engine keeps one off a node; what
vLLM cannot do on a node is a warning rather than a refused node; the
CUDA requirement follows the version; a hand-named binary is no longer
replaced by an installed one.
Refs #2 (items 4 and 7).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
llmgw ctl model adapters lists a model's adapters and adds or removes
them through POST /api/v1/models/{name}/adapters, which reads each new
file first and refuses one that is not an adapter for the model's
architecture, as the mirror page's card does.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A model's adapters are fetched by each node with its weights and loaded
unapplied at start; a request naming an adapter reaches the model's
replicas with llama.cpp's per-request lora field. Adapter names share the
model namespace, are listed, and follow allowlists on their own. The
router's config seed and pushed catalog now build the name map in one place.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A catalog model with external {url, model, api_key_file} and no profiles
is published as an endpoint by the control plane, the key read from its
file each time; the router rewrites the request's model to the provider's
name and sends its key. It is never scraped nor stale, and nothing is
placed for it. Named in fallbacks, it serves when the cluster has no room.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The quotas page gains a capacity section over the same API methods the CLI
uses; a model's card sets its class, owner, memory and cores, applying only
what changed. Headroom counts the replicas a model could get by preempting
what its class outranks, and the card and ctl model target use it.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Every agent samples, at most every ten seconds: nvidia-smi's temperature,
HBM temperature, power draw, enforced limit and throttle reasons per GPU;
hwmon's CPU package and board inlet temperatures; and CPU package power
from RAPL's energy counters. Sent with heartbeats, published as metrics
and shown on the node page; unmeasured sensors are absent, never zero.
Observe-only, step 0 of docs/facility-aware-placement.md, which now
describes the agent source and what these readings can and cannot say.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Stored ctl bench results reach the reconciler: requests per replica past
the measured SLO concurrency scale a model up, a replica is taken back only
if the rest stay under 70% of theirs, and among profiles of equal priority
the most measured tokens per GPU or core is tried first. ctl model target
sizes a model for a throughput against the room there is.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A model has an owner (org, team or project). Quotas guarantee a scope
GPUs, cores and memory and may limit it; what a scope holds is summed from
its models' replicas and those beneath it. A replica within its owner's
guarantee is not taken for another scope; what is held beyond a guarantee
is borrowed and taken back for a scope within its own. A limit stops a
model before it starts, and headroom stops there too. ctl quota and ctl
model owner set them; metrics and a CapacityAtLimit alert report them.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A model's class (critical, standard, opportunistic; unset follows its
scaling) decides what may be stopped to make room for it: a lower class
for a higher, and within a class only what another grew into under load,
for a model below its minimum. Victims are tried on a copy of the
inventory and stopped only if they make the room. Preempted replicas are
drained with a short grace and their model is held back until the room is
used. ctl model resources and class move under ctl model.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A profile's cpu_cores is reserved out of its node's CPUs by placement, as
memory is, and becomes the engine's thread count. Agents report their CPU
count in registration and heartbeats. New CPU models must declare memory;
model views show reserved against measured memory with warnings, and
ctl model resources sets both for a stored model.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The Held tab's model name opens a card: each replica with its node, state,
age, CPU and memory, the room for more found by dry-running the scheduler
on a copy of the inventory, and a form for the always-running and
under-load replica counts. An always-running count placement cannot meet
is refused with the reason. Headroom is also on the admin API.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
CPU replicas start on half the machine's CPUs, not all of them: two busy
llama.cpp servers each spinning on all 8 vCPUs fell to 0.8 tok/s apiece,
where at 4 threads they keep 5.5. llamacpp.threads sets it per node.
Inference requests can be traced over OTLP/HTTP (tracing.endpoint), with
admission, schedule and upstream spans and traceparent sent to the engine.
Security: onboarding no longer puts the cluster root in the system trust
store and removes it where it was; the mirror's mutual-TLS API can be
limited to named certificates (--api-client); init.log no longer keeps the
administrator's password; a warning when auth is off on a public listen;
the recorded actor is sanitised and sudo-aware; Ray's dashboard is off, and
the remaining engine gaps are documented.
reconciler.go and agent.go are split by topic, the status mapping of each
failure is tested, and the plan's checklist is brought up to date.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Adding a model took four steps and a YAML file — plan it, fetch it into the
mirror, write its catalog entry with a memory figure somebody estimated,
scale it — and whether it would load was found out at the end.
`llmgw ctl model serve OWNER/REPO` does it as one job in the control plane,
where the registry, the mirror, the catalog and the fleet already are:
- the registry: the GGUF file set matching --quant (Q4_K_M by default) or
--file, leaving out vision projectors and importance matrices, and naming
what the repository does have when nothing matches; gated repositories are
refused;
- the mirror: fetched at the repository's revision, with progress, and each
file's sha256 compared with what the registry published — the mirror is
the one place a corrupted download is caught once rather than loaded on
every node;
- the check: whether the nodes' llama.cpp loads the architecture, and the
memory it needs from its own header;
- the catalog, then a wait for a replica that stops early, with the reason,
when the model is parked or fits on no node;
- a smoke test: one short request to the replica, with its engine key, and
the answer and tokens per second.
ctl starts the job and prints each step as it changes; a download of hours
does not depend on the terminal. --on-demand adds the model without starting
a replica. Jobs are kept in memory until the control plane restarts.
The catalog name a repository suggests moved into the registry package, so
the mirror page and this agree on it.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Every upgrade so far has been a script of mine copying a binary to each
machine over ssh, restarting it and polling until it came back — which is
the job, but not something the product could do, and which needed ssh into
every node.
`sudo llmgw ctl upgrade VERSION` on the control plane does it. This machine
first: the release for its platform, checked against the release's
checksums, the old binary kept beside it, llmgw-server restarted, and a wait
until every node that was ready has reconnected. Then each node in turn: the
control plane tells its agent the version over the stream it already holds;
the agent fetches and checks the release itself, swaps it in with a rename,
answers, and exits with status 75 so systemd starts the new binary. Engines
keep running, as for any agent restart, and are adopted again.
A node is done when it is back on the new version, ready, with as many ready
replicas as before; one that is not within the timeout stops the run before
the next node, so a bad release reaches one machine. Nodes already on the
version are skipped, so running it again resumes.
The fetch-and-verify is one package now, shared with onboarding's idea of
what a release version looks like.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
"Why was this request slow?" had no answer. Nothing tied a request to the
replica that served it, the time admission held it went out in a response
header and was never kept, and the only per-request log lines were for
disconnects, early stream ends and upstream retries.
Every request now has an id: the client's X-Request-Id when it is a plain
string of reasonable length, otherwise one the gateway makes. It is set on
the response before anything else, so a request refused as malformed can be
named too, and forwarded to the engine. One access log line per request
carries it with the model asked for and served, the replica, status, time in
the queue, time to the first token, total time, tokens, and the key,
project and user.
Usage records gain the id, the replica, queue time and time to first token,
tried against a restored copy of the live store before being shipped: 591
rows kept, columns and index in place. `llmgw ctl request ID` reads one back
through the admin API.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
On the live cluster kev-0.5b — an architecture the nodes' llama.cpp did not
know — was started 155 times in eight hours, failing the same way each time,
and phi-4, which fitted on no node, logged "no placement" 250 times in two
days. Neither showed up anywhere an operator looked: the backoff never gave
up, no metric said a model was failing or unplaceable, and the one alert for
a model with nothing serving could not fire, because its series was published
only while a replica was ready.
After five failures in a row a model is parked: not placed again until its
definition changes — a new source or profile — or `llmgw ctl retry` is run.
Status, the model list and the admin API show it with its last error. The
placement warning is logged when its reason changes or every ten minutes.
A request for a parked or unplaceable model is answered at once with why,
rather than "loading; retry shortly", which a client retries forever. Each
request still clears the previous placement result and triggers a fresh
attempt, so a model that fits once room is made is not refused on a stale
answer; the router waits for that attempt, not for its full activation
window.
Metrics now cover model health (desired, ready at 0 for what should run,
consecutive start failures, parked, unplaceable), engine installs per node,
leadership, and certificate expiry read from disk at each scrape, with alerts
for each. `ctl status` counts a model against what it should have now, so an
on-demand model that was asked for and fitted nowhere no longer reads as
satisfied.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
There was no way to restart a running replica. A running engine picks up an
engine key, an edited profile or a new engine version only when it starts,
so after v0.10.3 the nine replicas already running stayed without a key —
reachable from any node — with nothing to do about it short of draining
nodes, which on a small CPU fleet has nowhere to move a model to.
`llmgw ctl restart REPLICA... | --model M | --all` queues replicas to be
replaced. A replica being replaced keeps serving but stops counting toward
its model, so the reconciler places a replacement beside it; once that is
ready the old one is drained and stopped as a node drain would. Replicas of
one model go one at a time, so a model never needs the memory of every
replica twice. Where no second copy fits anywhere, the old replica is
stopped after the drain grace period and the new one starts in its place,
and the log says so, since that is the one case with an outage.
`ctl replicas` shows "(restart queued)" and "(restart replacing)" while it
happens.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Engines listen on every address their node has, with no key. Checked on the
live cluster: from a neighbouring node, a request straight to an engine's
port was answered — past the gateway's authentication, quotas and
accounting, by anyone who could reach the node.
Each replica's engine is now started with a key and refuses requests without
it. The key is derived from the replica id with an HMAC over a secret kept
in the store, rather than stored per replica: the router has to send the
right key to replicas this process never started — adopted after a restart,
or placed by the other control plane — and a function of a shared secret
gives that without a table to keep in step. A key taken from one node opens
that replica only. The secret is made on first use and copied with the rest
of the store when moving to PostgreSQL; a new one would lock the router out
of every running engine at the moment of the move.
The key reaches llama.cpp and vLLM through their environment
(LLAMA_API_KEY, VLLM_API_KEY) rather than their command line, which every
user on the node can read; SGLang only takes it as an argument. The agent's
readiness check, the router's proxy and its metrics scrape send it, the
client's own Authorization still never reaches the engine, and `ctl bench`
fetches the key of the replica it is pointed at from the admin API.
Engines started before this keep running without a key until their replica
is next started. With no store there is nowhere to keep the secret, and
engines start without one, with a warning.
Also formats two files that went in unformatted.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
doctor checks a node alone, and every check can pass while two nodes still
cannot reach each other. ctl fabric-test A B asks B's agent for an
ib_write_bw server and A's for the client, adapter by adapter as NCCL pairs
them, one rail at a time, and grades each against its link's rate. It goes
through the agents' streams, so there is no SSH between nodes; the agent
installs perftest the first time, and names the RoCE v2 GID itself.
The result is read from perftest's table by its heading, not a column
number. Agents now list the requests they answer beyond the usual
(Register.features), and the control plane refuses a test on one too old to
answer instead of waiting minutes for it. The forwarding dispatcher carries
the new capability, and its test says so: a missing forward would have made
the command quietly unavailable in production.
Not yet run over real InfiniBand or RoCE; this cluster has neither.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Adding a node was deploy/bootstrap/onboard-node.sh, which is not installed
anywhere and wanted the node's name, the control plane's address and a
dozen flags. It is now in the binary, run on the control plane:
sudo llmgw node add ubuntu@10.0.0.21
The node id is the machine's hostname, and the address its agent dials is
this machine's on the route to it, checked against the server certificate.
It installs llmgw there (the release for its architecture, fetched and
checked by the node; uname rather than dpkg, so RHEL works), runs doctor
there and stops on a problem before changing anything, issues and installs
the certificate, writes agent.yaml and the unit, starts the agent, and waits
for it to join and install llama.cpp. The authority is read first, so a
missing one is said before the node is touched.
The steps are events. In a terminal they draw a checklist (bubbletea) that
asks what a script cannot: which fabric each group of active adapters is on,
grouped by the subnet manager or RoCE network they report, and whether to go
on past a problem. Elsewhere, one line per step.
Found by running it on an empty VM: ssh's "Permanently added" warning on
stderr was read as the machine's hostname, so standard error is now kept
apart; and a node added again while cordoned never counted as joined, so
joining now means a heartbeat since the restart.
llmgw node remove forgets a lost node, which otherwise stays listed until
the control plane restarts. ctl nodes no longer lists vLLM as skipped on
every CPU node. The quick install, install and onboarding docs use it.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A GB200 NVL72 rack or an NVLink Switch system is a scale-up island: its
GPUs reach each other over NVLink whatever node they are in. Each GPU
now reports its fabric from nvidia-smi -q - state, status, ClusterUUID,
CliqueId, bandwidth - at registration and once a minute after, since a
fabric manager restarting drops every card out at once; and each node
reports nvidia-imex and its channels, without which the domain's GPUs
cannot share memory across nodes. A single machine's NVSwitch, which
reports a cluster of zeros, is no domain.
A replica spanning nodes is placed inside one domain first, on nodes
whose every card is joined and whose IMEX is up, and its shards are
told NCCL_MNNVL_ENABLE=1; InfiniBand is the fallback.
/admin/nvlink draws each domain as a rack - the NVLink Switch band on
top, light running down a backplane, trays of GPU chips that glow when
joined, pulse red when not, turn amber on degraded bandwidth - and
lists what keeps a tray out. A node's page shows its GPU-by-GPU NVLink
matrix, each card's NUMA node and nearest adapter, and its domain and
IMEX. doctor checks joining, bandwidth, IMEX and its channel; metrics
llmgw_gpu_nvlink_joined and llmgw_imex_up; /api/v1/nvlink.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The control plane asks one node on each physical fabric to run
ibnetdiscover every five minutes and parses the text itself, so a
format the parser has not met is a control plane upgrade, not one on
every node. From the map it works out the switch tiers (leaf, spine,
core, by distance from the adapters), which adapter is which node's,
which cables trained below the rate the rest of their kind run at, and
whether the rails line up: every node's Nth adapter on its own leaf.
A page per fabric (/admin/fabrics) draws it: switches in their tiers,
adapters fanned out under their leaves, cables coloured by rate and
bundled, degraded ones marching red with a marker, and the cluster's
own adapters live - down ports glowing red, and particles running each
cable as fast as its adapter is sending and receiving, from the
counters. It zooms, pans, fades what a hovered switch or adapter does
not touch, opens a panel of detail on a click, colours by rail, finds a
node, and refreshes every fifteen seconds. The data is also at
/api/v1/fabrics/{id}/topology.
Placement reads the map: a replica spanning nodes takes nodes of one
leaf together when it can, and nodes whose cable trained short last.
Static assets are named by their content, as the stylesheet already
was.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A fabric's identity was its master subnet manager, which changes on a
failover: for the minute its ports took to agree, one fabric read as
two, and a name covering it as two separate fabrics. Each agent now
also lists every subnet manager on a fabric (saquery SMIR, through one
port per master, once a minute); managers listed together are one
fabric, named by the lowest GUID among them, so the identity survives
the master changing.
The same list says what a fabric can survive. The cluster page's new
subnet manager table, /api/v1/fabrics, doctor and metrics
(llmgw_fabric_master_sm, llmgw_fabric_subnet_managers,
llmgw_fabric_problems) show each fabric's master, priority and
standbys, and say when a fabric has no standby, its ports disagree on
the master, ports are up with no subnet manager (INIT), or the master
changed in the last hour. Every change of master a port reports is
kept, and a fabric's last day of them is listed.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A fabric was what agent.yaml called it. With several independent
InfiniBand fabrics at a site, two given one name would have had a
replica spread across both, starting and then hanging on its first
collective. Each active InfiniBand port now reports the master subnet
manager it sees (sminfo, asked once a minute), which every port on one
fabric shares and ports on separate fabrics cannot; a RoCE port reports
the IP network of its RoCE v2 address. Ports also carry their node and
port GUIDs.
Placement divides a declared fabric by that physical identity and forms
a replica on one part only; a node whose adapters for one name are on
two fabrics is refused, and a node that cannot say is kept apart from
those that can. /api/v1/fabrics and the cluster page list each physical
fabric with its subnet manager, and say when one name covers two
fabrics, two names one fabric, an adapter is up but undeclared, or a
node cannot be checked. doctor reports each port's subnet manager, and
an agent on a node with adapters installs infiniband-diags when sminfo
is missing.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
