SetCompute on an engine adopted from an earlier agent called writeState,
which read the command line from sh.cmd - nil for a process this agent did
not start - and the agent crashed at every start on the PoC's nodes until
fixed. An adopted shard now keeps the command line its state file had.
engine-check looked for llama-server one directory too shallow; the agent
installs it under <version-variant>/llama-<build>/. It now looks at both,
preferring a CUDA build on a machine with NVIDIA GPUs. Run on cuda01: a
token computed on CUDA0, 25 of 25 layers offloaded.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Checked against the installed llama.cpp (b10991): at its default verbosity
it prints neither the devices it uses nor the layers it offloads, so the
start-up output said nothing and every replica's ON column stayed empty.
The agent now takes compute from nvidia-smi - the GPUs the shard's processes
hold memory on, and how much - or CPU, for every engine, falling back to it
when llama.cpp's output says nothing; adopted engines get it too, and each
session reports it once per shard as a compute-only status. engine-check
runs its GPU token at -lv 4, where llama.cpp does say where its layers went.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Seeing a device is not computing on it. For llama.cpp on a machine with
NVIDIA GPUs, engine-check now loads a small GGUF (--model, or the smallest in
the weights cache) with every layer on the GPU, asks for one token, and
reads from llama.cpp's own output where the layers went: a build that
computes on the CPU fails, naming the fix. With no --bin it checks the build
the agent installed under /opt/llmgw/engines.
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>
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>
The launch arguments had been read from documentation and never from an
engine. Now they have been:
vllm 0.29.0 --host --port --tensor-parallel-size --served-model-name
--distributed-executor-backend all accepted
llama.cpp b10991 --model --host --port --metrics --alias --parallel
--n-gpu-layers all accepted
Getting there found three faults in the checker rather than in llmgw.
It checked every command's flags against the engine's help, but a shard can be
several programs — vLLM under Ray is `ray start` and then `vllm serve` — and
they have different vocabularies. It reported --head and --num-gpus as
missing, which is true of vllm and irrelevant, because ray is what receives
them. Each command is now checked against its own help, and one that cannot be
run is reported as skipped rather than failed.
vLLM does not give up its whole help for --help: it prints a summary of
section names and expects --help=all. Checked against the summary, all five
flags looked missing. The warning about truncated help did not fire, because
the summary happens to mention --help= while listing how to ask for more.
And vLLM refuses to print anything at all on a machine with no accelerator
unless VLLM_TARGET_DEVICE says what to assume. Asking an engine about its
flags should not require the hardware it runs on.
Two things remain unchecked and are written down as such: the Ray sidecar,
because vLLM 0.29 does not install Ray, and SGLang, because it is not
installed anywhere. `launch_mode: mp` avoids Ray entirely and is worth
preferring until someone checks it.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
A release was a git tag. Installing llmgw on a node meant having a Go
toolchain, and the onboarding script copied a binary from wherever it was run —
which works and quietly makes whoever runs it the build server, with the fleet
ending up on whatever was in their working tree. Tags now build static
linux/amd64, linux/arm64 and darwin binaries and attach them. CGO_ENABLED=0 is
the point: it has to run on a node whose libc is older than the builder's, and
llmgw has no C dependency because modernc.org/sqlite is pure Go precisely so
this stays true. Verified: "not a dynamic executable", runs on a node, reports
its version.
`ctl engine-check` closes the other half of the phase 4 gap. llmgw's launch
arguments are read from each engine's documentation, and engines rename flags
between versions; when that happens the failure arrives minutes into a model
load, in the engine's own error message, on a node nobody was watching. It
builds the widest command llmgw would ever produce — multi-node, since a flag
that only appears there is the one nobody notices has gone — and checks each
flag against the engine's own --help. No GPU: a CLI prints its help anywhere.
Verified against llama.cpp b10991, all seven flags accepted.
Flags are matched on a word boundary, so --port is not satisfied by
--port-range. Without that, a renamed flag would sail through the check that
exists to catch renamed flags.
It also warns when the help text looks truncated, which is how the first run
went: llama.cpp's help cut to 400 of its 725 lines reported five flags as
missing that the running cluster uses.
TestSidecarRunsBeforeEngine was flaky under a full run, and worse than flaky —
its wait loop fell through on timeout, so a slow machine turned the ordering
assertion into a no-op and the failure surfaced later as something else. It now
asserts, with a deadline that suits a machine running every other package at
the same time.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
