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llmgw/deploy/examples/agent.yaml
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ilkerandClaude Opus 5 2373726efd Agent stream: connect the fleet to the control plane
One bidirectional connect-go stream per node, carrying registration, heartbeats
and shard status up, and start, stop and drain down. The agent dials out: GPU
nodes sit behind firewalls far more often than a control plane does, and an
outbound connection needs no inbound rule on every node in the fleet.

- registration carries the whole hardware inventory, so there is no separate
  discovery call and a reconnecting agent refreshes what the scheduler knows.
  The fabric labels travel with it; they cannot be inferred from the wire, so
  losing them in transit would silently disable multi-node placement.
- agentsvc is both the stream handler and the reconciler's dispatcher, because
  it holds the open streams and is therefore the only thing that can deliver a
  command to a node.
- the agent validates a profile before launching anything, since an engine that
  refuses a profile does so after minutes of loading, then supervises the
  process and reports every transition with the engine's output tail on failure.
- reconnects forever with jittered backoff. A control plane restart must not
  require touching every GPU node, and a fleet must not reconnect in lockstep
  and knock it over again. Engines keep serving throughout, because the router
  talks to them directly.

llmgw agent is real now, and llmgw server runs a control plane whenever any
model declares a profile. The static endpoint list stays for router-only runs.

Verified with real binaries as separate processes: the agent registers, a
replica is placed, an engine launches through the vLLM adapter's actual argument
construction, streaming traffic is served, and killing the agent moves the node
through unreachable to lost with placement explaining why nothing can run.

config.Model and catalog.Model were the same thing described twice, so the
router now reads catalog entries directly and the validation rules live in one
place.

Still missing: mTLS. The stream is plaintext HTTP/2 and must stay on a trusted
network until that lands.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-09-16 02:48:21 +02:00

64 lines
2.2 KiB
YAML

# llmgw agent configuration, one per GPU node.
#
# The agent dials out to the control plane and keeps one stream open. Engines
# keep serving if the agent restarts: the router talks to them directly, and
# orphan adoption picks them up again on the next start.
node_id: gpu-a-01 # defaults to the hostname
server: "http://llmgw-server.internal:7443"
# The address engines are reachable on, and the address NCCL bootstraps over.
# Never a fabric address: NCCL rendezvouses over TCP before moving to RDMA.
mgmt_addr: "10.0.0.11"
mgmt_interface: eth0
labels:
rack: r12
heartbeat_interval: 5s
reconnect_base: 1s
reconnect_max: 30s
# Fabric labelling is explicit and there is no way around it. OpenSM's default
# subnet prefix is fe80:: on every fabric, so a matching prefix proves nothing
# and a wrong guess produces a replica that starts, passes health checks and
# then hangs on its first all-reduce (plan §5.2).
#
# A node with no fabrics is its own single-node island: it can host single-node
# replicas and will never be given a multi-node one.
fabrics:
- name: ib-a
hcas: [mlx5_0, mlx5_1, mlx5_2, mlx5_3]
rate_gbps: 200
discovery:
# Worth setting where the driver lives outside the default paths, which
# containerized driver installs often do.
nvidia_smi: ""
ib_root: /sys/class/infiniband
# NVML is the only source of NVLink topology, which is what keeps a
# tensor-parallel group off the PCIe path inside a node. It needs a binary
# built with `-tags nvml`; otherwise this silently falls back to nvidia-smi.
prefer_nvml: true
skip_gpus: false
engines:
vllm:
bin: /opt/venvs/vllm/bin/vllm
# Which multi-node mechanism works depends on the vLLM version, and the
# flags have changed more than once. Check the version you deploy.
launch_mode: ray # "ray" or "mp"
ray_bin: /opt/venvs/vllm/bin/ray
sglang:
# SGLang is launched as a module, which is its documented entry point.
python: /opt/venvs/sglang/bin/python
supervisor:
work_dir: /var/lib/llmgw-agent
# Engine output is the only diagnosis when a model fails to load, so keep a
# useful tail of it.
log_lines: 512
# Engines need time to free GPU memory cleanly before being killed.
stop_timeout: 60s
health_interval: 2s