Commit Graph
17 Commits
Author SHA1 Message Date
Ilker ManapandClaude Opus 5.5 3e7dd20bad One machine runs one judge, on up to four cores
A second judge replica on the same machine shares its cores and is lost
with it, so it bought nothing but half the speed of each. On a 4-core,
8 GB single-machine install, one judge on four cores passed 20 of 20
requests at about 3.5 s each, judged in and out. Two judges on two cores
each had passed 19 of 20.

server init now serves one judge when this machine is the only node, and
two, on two cores each and spread over the nodes, where there will be
others (HA setups, or a control plane that is not a node).
--judge-replicas and the setup's Copies still choose. install.md gives
the measured times in place of "half a second".

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-16 19:35:42 +02:00
Ilker ManapandClaude Opus 5.5 f8206c3540 A judge reserves two cores and 2 GB, with no prompt cache
Checked by a single-machine install of v0.14.6 on a 4-core, 8 GB VM. The
second judge fitted nowhere: v0.14.6 reserved 4 GB a judge, a figure from
one replica that grew to 3.2 GB through llama.cpp's prompt cache (up to
8 GB by default), which a judge never reuses. Once it fitted, every
judgment timed out. v0.14.6 gave a judge one core below 8 CPUs, and a
judgment of about 280 tokens takes 8.0 s on one core, 4.2 s on two and
2.3 s on four, against a 5 s budget: 0 of 20 requests went through.

A judge now runs with --cache-ram 0, uses about 1.6 GB and reserves 2,
and reserves two cores (one only below four CPUs). On the same VM, 19 of
20 went through.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-16 19:22:33 +02:00
Ilker ManapandClaude Opus 5.5 63b2c6d9a4 The judges and the starter divide a machine's CPUs instead of each taking all
Measured on the three-VM cluster with ha1's CPUs loaded: 38 of 60 requests
refused with both judges and the starter on ha1, each engine on all four
cores. With the judges moved to a 4-core, 32 GB node at 2 cores each, 2 of
60 were refused, both the first after the judges started.

init now reserves cores by this machine's count: one per judge replica
under 8 CPUs and two from 8, and the rest, up to 8, for the starter on CPU.
The starter reserves nothing when nothing is left, so it still fits. Judges
bound for nodes not yet known get two each. A judge reserves 4 GB; it uses
about 3.2.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-16 13:00:39 +02:00
Ilker ManapandClaude Opus 5.5 94401e21e6 Setup lets every part be chosen, each with its settings; three machines each run the gateway
The setup screens list the parts of an install: control plane, store, model
mirror, this machine as a node, starter model, pictures and 3D, the judge,
firewall, notifications, limits, on-demand turns and an offline bundle. Each
says what it is or what is missing without it. Space turns a part on or off;
Enter opens its settings in a dialog that explains it. A plan page lists what
is installed and what is left out, and the same install as a command. Every
setting is a server init flag too, written into server.yaml when init writes
it.

With three machines, the two database machines become control planes too,
standing by (server add's steps), with an optional floating address. The
mirror's management API answers all three control planes, so a standby that
comes to lead can still fetch models.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-15 11:22:36 +02:00
Ilker ManapandClaude Opus 5.5 b0273ffb5f Qwen3Guard is the judge, and judges on a GPU and on CPUs are asked in turn
Llama Guard 3 1B could not tell ordinary coding requests from harmful ones:
on the PoC it scored "continue" 0.51, "continue the code above" 0.89 and
"how do I kill all child processes in bash" 0.92 unsafe, against 0.97 to
1.00 for harmful requests, at Q4_K_M and Q8_0 alike, with its own template.
A site had "continue" refused. Qwen3Guard-Gen 0.6B gave the ordinary ones
0.15 at the most and the harmful ones 1.00, in 0.8 GB on CPUs.

- format qwen3guard: its prompt written out as its template writes it, for
  requests and answers, and its Safe / Unsafe / Controversial verdict read;
  controversial counts as the policy's judge.controversial says, safe by
  default.
- judge.models: judges asked in turn, the first that answers deciding; a
  failure is recorded against the judge that failed. guard judge use sets
  the order, replacing categories none of the judges names.
- known judges (Qwen3Guard 0.6B/4B/8B, Llama Guard 1B/8B): guard judge
  enable serves any of them, --gpu the 4B on the roomiest GPU class as a
  standard model; the leader fetches and serves one a policy names that the
  catalog lacks.
- server init installs qwen3guard-0.6b with the baseline's categories.
- a judge reads a long text by its start and end, which fit its context,
  rather than overflowing it and refusing the request under fail: closed.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-08 10:22:47 +02:00
Ilker ManapandClaude Opus 5.5 ad04a78343 The mirror's address is one the nodes reach, and node add checks it
server init and mirror init took the first non-loopback address for the
mirror's --base, Docker's 172.17.0.1 on a machine with Docker; nodes asked
their own bridge for every model and were refused. primaryAddr takes the
default route's source, else the first address on an interface that is not
a container's, bridge's or overlay's. node add has the node reach the
mirror's files during the checks and fails, naming the address and the fix,
when it cannot.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-06 09:36:09 +02:00
Ilker ManapandClaude Opus 5.5 6289b01ed6 The cluster's settings live in the store, and every change takes effect
Everything in server.yaml that is not one machine's own is now a versioned
document in the store (cluster_settings), the same on every control plane.
The first start against a store imports the file's cluster keys, with the
baseline guard policy when there is none; after that those keys in a file
are ignored, with a warning.

Each control plane polls the document's version and its own server.yaml's
hash. A change is validated and applied: guard live (Gate.Replace), anything
else by a graceful restart that systemd brings back, standbys first and the
leader 30s later. A version that does not load on a machine is not applied;
at start the newest one that loads is used, so a bad edit cannot keep every
control plane down.

llmgw ctl config show/get/set/unset/edit/history/rollback, the admin API
under /api/v1/settings, and the console's Settings screen, which layers the
store's document over the machine's file, write it. Commands beside the
control plane that read cluster keys (init, node add, server add, backup,
upgrade) read the layered configuration; init --bundle writes offline to
the store once it holds the settings.

A standby that becomes the leader re-reads models, engine choices and
facility holds from the store.

Retires appendGuardIfMissing: the import does it.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-05 15:25:23 +02:00
Ilker ManapandClaude Opus 5.5 dc65ea6899 server init adds the guardrails to a server.yaml from before them
An upgrade replaces the binary and leaves server.yaml alone, so a control
plane installed before guardrails stayed unguarded. Run again, init now
appends the baseline guard section (and so fetches the judge) when the file
has no guard key at all, keeping the old file as server.yaml.bak-<time>.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-05 12:39:28 +02:00
Ilker ManapandClaude Opus 5.5 bae16426a2 Offline sites and Kubernetes: llmgw bundle create, import and trust, offline.mirror, server init --bundle; a Helm chart for the control plane
A bundle carries what a cluster would download — llmgw's release with its
signed checksums, every llama.cpp build per architecture with GitHub's
checksums, model files pinned and checked against Hugging Face's — into a
directory or a tar whose manifest sha256 is carried apart. Import checks
everything, the release against the signing key, before anything is put in
the mirror. With offline.mirror set, nodes install llama.cpp from the
mirror against checksums the control plane holds and sends over the agent
stream; releases default to the mirror for node add, ctl upgrade and agent
upgrades. server init and the setup form install from a bundle.

The Helm chart runs one control plane with SQLite on a claim or an external
PostgreSQL, Secrets for the agent certificate and admin token, Services,
Ingress and ServiceMonitor; deploy/container/Dockerfile builds its image.
GPU nodes stay outside Kubernetes, as docs/kubernetes.md explains.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-05 00:13:39 +02:00
Ilker ManapandClaude Opus 5.5 c553f2041a The baseline fails closed with two judges, and a judge not answering is a notification
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-04 19:19:49 +02:00
Ilker ManapandClaude Opus 5.5 07af03aa93 server init: the firewall and the judge before the starter model, and a model that fits nowhere said within two minutes
A starter model with no room failed init after ten minutes and left the
firewall and the judge uninstalled.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-04 19:11:12 +02:00
Ilker ManapandClaude Opus 5.5 ddb0472f9b A hardened install by default: baseline guardrails with Llama Guard 3 1B judging on a CPU, keys at most 90 days
server init (and the setup screens) write guard.default [baseline] —
secrets refused, cards masked, injection recorded, the judge refusing
S1-S4, S7, S9-S12 in prompts and answers, fail open after 5s — fetch the
judge into the mirror and run it on a node's CPU. --no-guard-model leaves
it out. The closing text names what init cannot do: TLS on the client
port and backup recipients.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-04 17:45:31 +02:00
Ilker ManapandClaude Opus 5.5 c0421bc7ee Guard the admin API and pprof with a token only root and the server can read
The admin listener answered any local process: models added, nodes
upgraded, engine keys listed, the heap dumped. /api and /debug now demand
the token the server writes 0600 to its state directory on first start;
ctl, the console and node add send it (sudo, LLMGW_ADMIN_TOKEN or
--token-file). pprof is off unless debug_pprof. admin_listen off loopback
is refused unless admin_remote. Dashboard pages, /metrics and /health are
unchanged.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-04 10:02:44 +02:00
Ilker ManapandClaude Opus 5.5 b5f412f9df Put the mirror on its own machine in two commands
mirror init sets a machine up as the model mirror — the user, /srv/models,
and a unit serving files on :8081 and the management API on :8082 — and
server init --mirror points the control plane at it instead of running one.
Before, that took a unit written by hand and an edit to server.yaml.

The management API can fetch and delete models and answered anyone who
could reach it. mirror serve --api-allow limits it to the addresses given
and the machine itself; mirror init passes the control plane's. The unit
text is shared with the one server init writes for a mirror beside it.

With --no-node the starter model is still fetched and catalogued, unpinned,
so it starts on the first node added, and the init form shows the mirror in
use instead of offering to run one.

Tried on three empty VMs: the mirror answered the control plane and refused
the node, and the starter model came up on the node when it was added.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-09-29 22:17:24 +02:00
Ilker ManapandClaude Opus 5.5 4c5a2fd6da Install vLLM on every GPU node that can run it, as llama.cpp is
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>
2026-09-29 13:15:54 +02:00
Ilker ManapandClaude Opus 5.5 4d9284ce9f Find each node's management interface, and lift the agent's memlock limit
The agent's defaults said eth0 before normalize looked for the interface
holding the management address, so it never looked: a node on ens3 or
bond0 had NCCL and Gloo bootstrap over an interface it does not have. It
is now found, with eth0 only as the last resort.

The agent unit sets LimitMEMLOCK=infinity. RDMA pins what it registers,
and under systemd's 8 MB default an engine without the capability that
lets root ignore the limit fails its first collective over InfiniBand.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-09-29 01:44:59 +02:00
Ilker ManapandClaude Opus 5.5 42fa602247 Make a new control plane serve a model by itself
server init now also:
- runs a model mirror beside the control plane (/srv/models, bytes on
  :8081 at an address nodes reach, management API on loopback so it
  needs no certificate) and adds it to server.yaml;
- makes the machine a node of its own control plane: issues its agent
  certificate, writes agent.yaml labelled as the control plane, starts
  llmgw-agent, and waits for it to register and install llama.cpp;
- checks for NVIDIA GPUs with nvidia-smi, and reports a card on the PCI
  bus whose driver does not answer rather than ignoring it;
- fetches Qwen2.5 0.5B (Q4_K_M) into the mirror and adds it to the
  catalog pinned to this node: a llama.cpp profile per GPU class found,
  with a CPU profile as the fallback, then waits for it to serve.

--no-mirror, --no-node and --no-model leave these out. Every step looks
before it acts, so a rerun finishes what an interrupted one started.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-09-28 16:49:55 +02:00