Conflicts with the LoRA work: vLLM's arguments, the engine profile, router
metrics and the scheduler's request all keep both; agent.pb.go regenerated
from the merged proto; the two test fakes of vLLM given separate names.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Plan §7.5 tier 2, opt-in per profile with kv_events. The agent starts vLLM
publishing KV cache events to a loopback port it listens on (pure-Go zmq4,
msgpack in both of vLLM's encodings), with sha256_cbor hashing and a fixed
PYTHONHASHSEED; it mirrors each engine's blocks and sends net changes up the
existing stream, with a full copy on every new one and a reset on gaps,
restarts and clears. The control plane hands them to the router, for the
sending node's own replicas only. The router tokenizes through a replica's
/tokenize, hashes the tokens as vLLM does (checked against cbor2/hashlib
golden values), and scores replicas by the share of leading blocks held,
falling back to the approximate tree. New metrics beside
llmgw_prefix_decisions_total. Untested against a running vLLM.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A vLLM model can carry PEFT adapters, registered at start, or with
max_loaded_adapters held at most N per replica: the router loads an adapter
through vLLM's runtime LoRA API when a request first wants it, sends the
adapter's requests to a replica that holds it while that one is not
saturated, loads each cold adapter once, and unloads the least recently used
idle one to make room. llama.cpp keeps loading every adapter at start.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The router read SGLang replicas with vLLM's names, and SGLang was started
without --enable-metrics, so it served no /metrics: every scrape failed as
if unreachable and the replica was routed to blind. It now gets
--enable-metrics and a mapping of its own (sglang:num_running_reqs,
num_queue_reqs, token_usage, the token counters and num_requests_total, as
0.5.21 publishes them). engine-check asks python -m sglang.launch_server
--help instead of Python's own help, and SGLang is given --tp-size rather
than the --tp argparse only takes as an abbreviation.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Engine trtllm runs trtllm-serve on one node, through a launcher of
llmgw's that demands the replica's key and turns on the Prometheus
endpoint its queue and cache gauges are read from; the router reads it
at /prometheus/metrics. Brought by the operator (engines.trtllm.python),
not installed; checked by ctl engine-check trtllm. Written against the
documented CLI, server and metrics and tested with stand-ins: not yet
run on a datacenter GPU.
mirror import-ollama copies a model's GGUF, and a vision model's
projector, from Ollama's directory into the mirror, checked against the
digest Ollama named each blob by; mirror import does the same for any
local file. The console's Mirror view imports from Ollama with I.
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>
env is an allowlist of tuning families less path, plugin, library, address
and credential names (NCCL_*_PLUGIN, CUDA_INJECTION64_PATH and the like
got through the old denylist); llama.cpp's LLAMA_ARG_* are checked as
flags. args refuse --trust-remote-code, --log-file, --slot-save-path,
--tool-parser-plugin, --middleware, --allowed-local-media-path and others
that read, write or load, and what the agent sets itself. Checked in the
catalog and again on the node before an engine starts.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
model serve takes --projector, fetched beside the weights and loaded by
llama.cpp with --mmproj, and --format safetensors with --gpu-class and --tp,
which fetches the weights with their config and tokenizer and runs them on
vLLM. The console offers both, F switching between GGUF and original
weights. The mirror view is one row per version with its files on Enter.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Each engine has a list of builds, best first, from what its project
publishes and how each was compiled: llama.cpp CUDA 13.3, CUDA 12.8
(driver 570 for the GPUs it ships as PTX), then Vulkan, never the CPU
build on an NVIDIA node; vLLM CUDA 13, then CUDA 12.9, and for GPUs
older than every build of the version asked for, the newest built for
them (V100: 0.19.1). The node takes the first its hardware meets and
that installs and computes on the GPU, moving on when one does not, and
reports the version it runs. GPU processes are read from nvidia-smi's
full report, so Vulkan engines count as on their GPU.
Refs #2.
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>
tp on a llama.cpp profile was a width nothing placed. gpus_per_node is
now the count: placement reserves that many cards on one node and the
engine splits layers across them. tp, pp and nodes are refused with
what to write instead.
Refs #2 (item 8).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
llama.cpp whose CUDA backend cannot load runs on the CPU without an
error. The CUDA build is now asked for its devices when installed, a
shard on GPUs must hold memory on each before it is ready, and
engine-check reports the devices. Shards get CUDA_DEVICE_ORDER=PCI_BUS_ID
so CUDA numbers GPUs as placement does.
Refs #2 (item 2).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
--lora with --lora-init-without-apply leaves an adapter's scale at 1,
and llama-server applies it at that scale to any request without a
"lora" list of its own: every plain request for the model was served
with its adapters on. Found live on qwen2.5-coder-7b-instruct. Each
adapter is now loaded with --lora-scaled FILE:0.
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 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>
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>
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>
The admin listener has no login. Reaching it is meant to mean holding the
control plane, so it binds to loopback and an operator reaches it over an
ssh tunnel — and so does every page open in that operator's browser. The
API decoded JSON whatever the Content-Type said, so a page could POST a
text/plain model definition, no preflight needed. A model's profile env
reached an engine the agent runs as root, unfiltered: LD_PRELOAD in a
definition was code execution on every node. A page that rebound its own
name to 127.0.0.1 could also read pprof's heap, which holds API keys.
The admin listener now answers only when Host is an address or localhost,
refuses requests a browser marks as cross-site (Origin, Sec-Fetch-Site),
and takes changes only as application/json, which no page can send across
origins without a preflight nobody grants. ctl sends that on every change,
bodiless DELETEs included; curl and ctl are otherwise unaffected.
Profile env that changes what runs rather than how — the dynamic loader's
LD_*, PATH, Python's PYTHON* start-up variables, BASH_ENV and their like —
is refused when a model is defined, and dropped again by the engine adapter
before the process starts, whatever sent the profile. None of the live
catalog's models sets any.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
vLLM was installed by hand and named in each agent's configuration. The
agent now installs it: a Python 3.12 environment built by uv, which is
itself a GitHub release checked against its checksum, from PyPI resolved as
it stood a day after that vLLM was published and installed with
--require-hashes. The resolved list is kept in the environment. The
environment is built relocatable under a scratch name and renamed into place
once it runs, like an archive.
The default is 0.30.0, whose flags were checked against the ones llmgw
builds. It is built for CUDA 13, so it needs driver 580 and a Turing GPU or
newer, and 25 GB free while it installs; a node lacking one says which, and a
node with no GPU skips it without being shown as a fault. gcc comes with it
because Triton compiles its launcher at the first model load. SGLang
installs the same way when the control plane names a version. llmgw doctor
reports whether vLLM can be installed on the node.
Checked by installing 0.30.0 this way on a test node: 7.7 GB, torch 2.13 for
CUDA 13.0, importing cleanly; the node has no GPU, so vLLM was not started.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
An empty launch_mode meant ray: a Ray head and joins on every node, a
dependency to install, and a path never checked against a real cluster.
It now means mp - every node runs vllm serve with its rank and the
leader's address, the workers headless - which is how vLLM documents a
multi-node launch today and needs nothing but vLLM. ray is still there
for a vLLM too old for mp's flags. The example agent configuration,
doctor and the reference say so.
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>
For machines nobody here can log into. It reads the GPUs and their
nvidia-smi topology, NVSwitch and the fabric manager, every InfiniBand
and RoCE port with its counters and RoCE v2 GID, the RDMA and
GPUDirect kernel modules, the agent's declared fabrics against the
adapters, the management interface NCCL will bootstrap over, the
agent unit's memlock limit and the engines installed - and says what
is wrong and how to fix it, most serious first, with the NCCL
environment a shard on each fabric would be given. --json writes every
fact it concluded from, to send back. It exits non-zero when something
fails.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
vLLM's mp launch gave workers no rank and the leader's address as their
own VLLM_HOST_IP; every node now runs serve with --nnodes, --node-rank,
--master-addr and --master-port, and the workers --headless, as vLLM
documents it.
NCCL_SOCKET_IFNAME and GLOO_SOCKET_IFNAME were the control plane's one
interface name for every node; the agent already worked out its own -
the one holding its management address - and now uses it.
On RoCE, NCCL's default GID index is a v1 link-local entry on most hosts,
which does not route. The agent finds the RoCE v2 entry holding an IPv4
address and passes it as NCCL_IB_GID_INDEX; InfiniBand gets none.
A worker whose only process needs the leader - a Ray join - started
without waiting for it, since the wait was honoured for sidecars alone;
it found no head, exited and failed the replica. It now waits.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A node ran whatever llama-server somebody had once unpacked on it. Onboarding
checked an engine only when told its path, installed none, and a node with
nothing on it registered as ready. The one time that went wrong it went wrong
invisibly: the binary was there, executable, and could not start for want of
libgomp1, while the node reported healthy.
The control plane now says which engines every node has, at which version —
`engines:` in server.yaml, defaulting to the llama.cpp build this release was
checked against — and each agent converges on it. What only the node can
know, the node works out: the build for its processor and driver (CUDA with
its runtime when an NVIDIA driver is loaded, CPU otherwise), the OS packages
that build needs from its own package sources, and whether the result runs.
Every archive is checked against the sha256 GitHub publishes for it and
unpacked refusing any path or link that leaves the install directory. ldd
names a missing library outright; running `--version` proves the rest. Only
then is a directory marked installed, and it is checked again every ten
minutes, because an unrelated upgrade can remove a library.
An engine is reported only once installed and seen to run, or when the
agent's configuration names it on purpose — never because something happened
to be on PATH. The agent now says when it has probed, so an empty list means
"has none" rather than "too old to say": a node rebuilt from nothing is no
longer a candidate for every model at the moment it can run none of them.
Placement's refusal carries the node's own words, "installing: downloading
llama-b10991-bin-ubuntu-x64.tar.gz (16 MB)", and so do `ctl nodes` and the
cluster page, along with a warning for a CPU without AVX2 — the VM CPU type
that makes everything five times slower and nothing look wrong.
Agents also report their OS, architecture, vector extensions and NVIDIA
driver. Onboarding installs llmgw itself from the CI release for the node's
architecture, checked against the release's checksums, rather than copying
whatever binary the operator's machine had.
vLLM and SGLang are not installed yet: they are Python environments of
several gigabytes, not an archive, and still have to be named in the agent's
configuration.
Also fixes a race on the weights fingerprint, which two heartbeat loops touch
during a failover.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Every agent file on the cluster named its own node, its own address, the
path to llama-server and two weight directories — different on every node,
kept true by hand, and not true: node05 carried "ram: 32g" beside a measured
31 GB, looked for weights in an empty directory while fetching them into
another, and the other four never looked in theirs at all, so four nodes
reported no disk. Growing a node's RAM would have gone unnoticed, because
memory was read once, at registration.
The agent now finds these out. Its id is the hostname, which is also the
certificate's common name. Its address is the one on the route to the
control plane, which the kernel picks for a UDP dial without sending
anything. llama-server is looked for on PATH and then where release archives
are unpacked. A missing nvidia-smi is a node without NVIDIA cards rather
than a failed discovery — which also cut discovery short before InfiniBand
on every CPU node not told to skip GPUs. Fetched weights land in
/var/lib/llmgw-weights by default, and wherever they land is always looked
in.
Memory is read on every heartbeat, and installed engines are probed again
each minute and sent when they change, so a VM given more RAM or a node
given vLLM becomes a candidate without anyone restarting its agent. The
control plane logs both changes, since "the node now qualifies" is the
answer to why a model placed an hour after it was asked for.
What is written still wins. The agent file shrinks to the control plane's
addresses and the TLS files, the same on every node; onboarding writes only
the overrides it was given. What cannot be found stays configuration: which
fabric an adapter is on, because OpenSM's default prefix is the same on all
of them.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The stale-profile flag could not fire where it matters. The control plane
keeps no record of a running replica across its own restart — it rebuilds
them from what the agents report — and adoption left the fingerprint empty,
so every replica read as unknown afterwards. That is precisely the moment
the catalog is most likely to have been edited: change the configuration,
restart the server, and nothing would ever say the fleet disagrees with it.
The fingerprint now travels with StartShard, the agent keeps it beside the
pool and fabric it already echoes back, and adoption takes the replica's
version from the agent rather than from the catalog it is about to be
compared against. An agent too old to report it sends nothing, which still
reads as unknown rather than as matching.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The mirror would happily hold a four-shard model that nothing could serve. A
catalog entry names one source, the agent fetched one URL, and so a version of
several files had no way to be described — which is every safetensors model,
and every GGUF too large to ship whole.
A source ending in a slash names a version rather than a file. The agent fetches
every file in it into one directory and hands the engine the directory: vLLM
reads the shards and the config out of the folder, and llama.cpp is given the
first part and finds the rest beside it.
The trailing slash is the whole signal. A flag beside the source would be a
second place for the two to disagree, and a URL already distinguishes a
directory from a file everywhere else.
The mirror answers a version with a manifest instead of a file listing. A plain
file server gives HTML there, and something that has to know what a version
consists of would be left parsing it. It is on the byte listener, not the
management API: fetching weights is the one thing every node does, and making
it depend on the administrative port would undo the split that keeps that port
closeable.
Built from the directory rather than a file written beside it. A manifest on
disk is a second record of what is there, and the two disagree the first time
somebody copies a file in by hand — which the mirror explicitly allows.
The whole set or nothing. A directory with three of four shards loads as a
broken model rather than failing to load, so a completion marker is written
last and an interrupted fetch starts the missing files again rather than being
found and served. Files already there at the right size are not fetched twice.
Progress is reported under the version's address, not each file's: the shards
waiting on it asked for the version, and progress under a file's own URL
reaches nobody.
A filename from the manifest is refused if it contains a separator. The mirror
is a place an operator copies files into by hand, so what it reports is not
automatically safe to join onto a path.
An HTML listing where a manifest was expected says so. "invalid character '<'"
sends somebody looking at the wrong thing.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
A node's registry holds an adapter for every engine whether or not its binary
exists, the agent never said which were installed, and placement carried an
Engine field it had never once read. So a vLLM replica was as happily placed on
a node with no vLLM: the node registered healthy, the shard failed minutes
later with "executable file not found" in the engine's own words, and the
reconciler tried again.
That is how fifteen gigabytes of safetensors reached this cluster's mirror
before anybody noticed it had no card and no vLLM to run them with.
Agents now probe what is installed and report it — the protocol has carried
engine_versions since it was written and nothing ever filled it in. Placement
filters on the key set, `ctl nodes` has a column, and the mirror page's serve
warning is a fact instead of a guess about cards, which would have waved
through a fleet of eight-GPU nodes that never had vLLM.
Probing resolves binaries and runs none of them. The first version asked each
engine for --version, which is fine for an engine and not fine for the wrapper
scripts people put in front of one: a script that shifts past flags it does not
recognise and then execs the server will start an inference server, at agent
startup, on every node. The agent's own test suite installs exactly such a
script and caught it. So presence is a path lookup, and the version is left to
`ctl engine-check`, which is run by somebody who meant to run the engine.
SGLang is the exception and has to execute, because a module has no path to
look up — but the command is llmgw's own one-line import rather than the
engine's launcher. Its exit code is the answer: python3 is on almost every
machine, and treating a failed import as "installed" reported SGLang on every
node in a fleet that had none, which is the failure being fixed, inverted.
Probed once, not per reconnect, and bounded by the context that registration
waits on: an agent that cannot reach the control plane is worse than one
reporting an incomplete list.
A node that reports no engines at all is not excluded anywhere — by placement,
or by the page. That is what every agent older than this reports, and emptying
a fleet on a control plane upgrade is a worse failure than the one being fixed.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
9fc098f said passing --jinja fixes tool calling. It does not. llama.cpp
build 10991 has jinja enabled by default — "--jinja, --no-jinja ... (default:
enabled)" — so the flag changed nothing, and tool calls came back as text
before and after it in exactly the same way.
The flag is worth keeping for a different reason, and the comment now says
that one: it pins how prompts are built, so an engine upgrade cannot change
it underneath a running fleet. Older builds do default to the generic
template.
What actually happens is that llama.cpp renders the tools correctly — the
prompt from /apply-template carries the <tools> block and the instruction to
reply in <tool_call> tags — and qwen2.5-coder answers with a markdown JSON
block instead. 3B and 7B both, and tool_choice "required" does not constrain
it either. That is the model, not the plumbing, and no flag here will fix
it.
Recording it because a wrong explanation in a commit message is worse than
no explanation: the next person to see tool calls arriving as text would
have checked --jinja, found it already set, and had nowhere to go.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Tool calling did not work, and it looked like the model's fault. Asked to
use a tool, qwen2.5-coder produced exactly the right call —
{"name": "list_files", "arguments": {"path": "."}} — as plain reply text,
with tool_calls null and finish_reason "stop". Every OpenAI-compatible
client, opencode among them, sees a model that ignores the tools it was
given.
The cause is that llama-server falls back to a generic chat template unless
--jinja is passed, and the generic one neither formats the tool definitions
the way the model was trained to read them nor parses what comes back into
tool_calls. It affects ordinary chat too, for the same reason.
A profile can still pass --no-jinja, because a model whose bundled template
is broken has to be serviceable without editing the adapter.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
New nodes had been joined by hand, four times, by me. There was no procedure,
so there was nothing to be wrong — and nothing to be right either. Two scripts
now: provision-node.sh makes the VM on Proxmox, onboard-node.sh gives it an
identity. They are separate because they are run by different people with
different access, and the second must run where the certificate authority is,
which is the one part that cannot be delegated.
Running them turned up why phase 4 was never verified. FabricAttachment
carried no koanf tags, so the `name:` key the shipped example documents bound
to nothing and `rate_gbps` was never read at all. An agent configured exactly
as instructed refused to start with "fabrics[0]: name is required" — naming
the key the operator had just written. Every fabric in this project had been
configured in a Go test, where the tags do not matter.
The server example did not load either: its models had no source, so it failed
validation the moment anything tried to parse it.
Both examples are now parsed by a test. A published example that has never
been loaded is a guess, and these two had been guesses since they were
written.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
llama.cpp is what makes a cluster with no cards useful: it serves the same
OpenAI API on CPU, so the whole path — placement, supervision, scraping,
scheduling, admission — can be exercised end to end on ordinary machines.
Three things had to give way for it.
A profile may now omit its GPU class, and that is how it says it runs on the
CPU. Only engines that can actually serve without a card may omit it; for
vLLM an omitted class stays an error, because otherwise a typo becomes a
replica placed on a machine with no hardware and diagnosed minutes later
during model loading. Placement spreads CPU replicas instead of bin packing
them, since unlike GPUs there is no allocation to stop two of them sharing a
machine's cores.
Metric mappings are now per engine rather than per cluster. llama.cpp calls
the same three numbers requests_processing, requests_deferred and
kv_cache_usage_ratio, and a single global mapping would leave half a mixed
fleet unmeasurable. An empty mapping still defaults to vLLM's names; one that
names its own Running metric is taken as a complete statement, so the fields
it leaves blank stay blank rather than being filled with names that engine
cannot have.
The adapter refuses multi-node and tensor-parallel profiles. llama.cpp splits
across local GPUs by a different mechanism than this adapter drives, and
accepting either would produce a replica that starts and then serves from one
machine while the catalog believes it has several.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Placement and environment generation had landed early, which made multi-node
look further along than it was. A replica spanning nodes could not come up at
all, and all three reasons came down to assumptions that only hold on one node.
Not every rank serves HTTP. The agent health-checked every shard at its own
port, but only the leader answers; workers timed out, reported Failed and took
the replica with them. Engine.ServesHTTP now says which ranks serve, and a
worker is judged by staying alive. That is deliberately weaker evidence: the
replica's real readiness is the leader answering, which only happens once every
worker has joined.
A shard is not always one process. Engine.Commands returns a sequence — every
command but the last is a sidecar kept alive for the shard's lifetime and torn
down with it, and the last one's exit is the shard's. vLLM under Ray is why: the
leader runs a Ray head and then the server against it, while a worker runs only
the join and no vLLM process at all. RayCommand had been written and tested but
never called, so launch_mode: ray would have run vllm serve against a cluster
that did not exist. Sidecars are killed with the shard, because a Ray head left
behind holds the port the next replica needs.
Start order was backwards. Workers went first, on the theory that the head
should wait for them. Both engines put the rendezvous point on rank 0, so a
worker started first has nothing to connect to. vLLM's server blocks until the
Ray cluster has the GPUs it needs, so the leader can start immediately and waits
on its own. A command can now also require that rendezvous point to be listening
before the next starts, bounded by a timeout so a head that never appears fails
the shard instead of hanging it.
Stop was also killing only the engine, leaving sidecars behind.
None of this is verified against real engines — it is tested with stub
processes. The Ray sequencing, and which launch mode a given vLLM version wants,
can only be confirmed on hardware.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Not persisting replica state left one hole: restarting the control plane made it
forget every replica while the engines kept running. They held GPUs the
inventory believed were free, so the next placement failed to allocate, with an
error naming the new process rather than the old one.
The fix belongs on the agent, not in a database. A row can say "running" about a
process that died; the fleet cannot.
- the supervisor writes the full shard spec, not just a pid and command line. A
pid identifies a process; the spec is what lets it be described back to the
control plane and matched to a replica.
- Adopt takes over an engine a previous agent left running. It is not this
process's child, so it cannot be waited on: it is watched by polling and
stopped by signalling its group directly. Its earlier output is gone and the
log says so rather than presenting emptiness as silence.
- every Register reports what the agent is running, not only the first, because
a reconnect may be a control plane restart and has to recover the same way.
- the reconciler rebuilds replicas from those reports and reclaims their GPUs.
Anything whose model or profile left the catalog is stopped; nothing will
route to it and it is holding cards.
- an engine the agent cannot identify is killed. A process holding GPUs that
nothing can describe is worse than no process, because placement hands those
GPUs out again.
Stop was also wrong for adopted shards: it checked for an exec.Cmd, which they
do not have, and reported them stopped without killing anything.
TestAgentReconnects had been passing for no reason. httptest's
CloseClientConnections does nothing to an h2c stream — prior-knowledge h2c
hijacks the connection and httptest stops tracking hijacked ones — so the agent
never reconnected, and the assertion on the connection map passed on a stale
entry. Tests now sever connections through their own listener and count opened
streams.
Plan §11 also records what multi-node still needs, since placement landing early
makes it look further along than it is: workers never become Ready because the
agent health-checks every shard while only rank 0 serves HTTP; RayCommand is
written and tested but never called; and startup is fire-and-forget.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Everything here works without GPUs and is covered by tests. The control plane
connection is not built yet, so llmgw agent itself is still a stub and the phase
exit criterion is not met.
- discovery: IB from /sys/class/infiniband, parsing the formats sysfs actually
uses and telling native InfiniBand from RoCE; GPUs via nvidia-smi, the
cgo-free path, with health flags for uncorrected ECC and memory already held
by another process. A node without IB is not an error, it is a single-node
island.
- engines: vLLM and SGLang command and environment construction, kept free of
process handling so it stays testable. The NCCL environment is the part the
plan marks critical: NCCL_IB_HCA lists only the chosen fabric's adapters,
because leaving it open lets NCCL pick an adapter on a fabric where the peer
is unreachable, and the job then hangs instead of failing.
- validation before launch: no mixed GPU types in a tensor-parallel group, the
node/GPU/TP arithmetic has to add up, and fp8 is refused below compute
capability 8.9 rather than silently falling back to a slower path.
- supervisor: process groups, because engines fork workers that hold GPU memory
and outlive a signal sent only to the leader; a bounded log tail, since engine
output is the only diagnosis when loading fails; Starting separate from
Loading, because a 70B model legitimately takes minutes; and orphan adoption
through pid files fingerprinted with the command line, since pids get reused
and adopting an unrelated process is worse than leaking the original.
- readiness is two questions: /health says the process is up, /v1/models says
the weights loaded. Routing on /health alone sends traffic into a loading model.
- llmgw agent inventory reports what the scheduler would see on a node.
One plan correction: GPU class sizes come from the driver in GiB, which does not
always match the marketing name. An L40S is sold as 48GB and reports 44.99 GiB,
so its class is l40s-45g, not l40s-48g. Consistency matters more than matching
the box, because that number decides whether a model fits. Plan §2 and §5.1
updated.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
