doctor sums findings up as two readinesses - the node's, which decides
whether it joins, and multi-node, its fabrics for replicas spanning nodes -
prints fabric findings in a section of their own, and gives both in --json.
node add says the multi-node one. NodeView.multi_node says whether a node's
declared fabrics are up, naming adapters that are down, and ctl nodes shows
it beside the fabric.
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>
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>
Read from vLLM's release pipeline: every x86 build from 0.20.0 has
kernels for compute 7.5 and up, the CUDA 12.9 one included, and 0.19.1
is the last built for 7.0. PyPI's build is CUDA 13 from 0.20, not 0.26.
A V100 asked for a newer vLLM is refused, naming 0.19.1 for
engine_overrides; the CUDA 12.9 fallback is for older drivers on
Turing and newer. The cu129 wheel is found among the release's assets,
since its manylinux tag differs between versions.
Refs #2.
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 RoCE port without a v2 GID and a missing ib_uverbs failed doctor, and
node add, whatever fabric used them. They are now failures only for a
declared fabric and warnings otherwise; node add passes its --fabric
list to doctor so those are checked as declared.
Refs #2 (item 5).
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>
A control plane of this release asks every node for vLLM. An agent of the
last one does not know how to install it, and answered "unsupported", so a
cluster half way through its upgrade showed every node as failing. Agents
now say which engines they can install (Register.installable_engines), and
are asked for no others; an agent that says nothing is asked for llama.cpp
alone, as it always was.
doctor reports the CPU's vector extensions, and warns when AVX2 is missing,
with the fix for a VM: the test node for node add was one, on QEMU's default
CPU model, where llama.cpp runs several times slower and nothing else looks
wrong. node add shows it, since it runs doctor.
The changelog's unreleased section was two releases' worth of sections one
after the other, with two lists of fixes and the upgrade notes in the
middle. It is one entry now: largest changes first, the fixes together, and
every upgrade note at the end, with vLLM's default install and how to keep
your own.
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>
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>
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>
