Follows the portal: the noun is "instance" everywhere the app says it —
UI strings, CLI output, error details, docs and comments. The wire keys
(`instance_id`, `instance_token`) and the hub route this calls move with it.
An existing cloud.json is adopted rather than refused: without the key
alias the dataclass fails to parse, which the caller swallows and reads as
"never enrolled" instead of "reconnect".
`instance_key` on a node type becomes `target_key`. It means the outside
thing a node points at, which is a different sense of the word, and keeping
both would put two meanings of "instance" in one codebase.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015YrQnKV3bnQd4K342y8tKj
GPU count is not detected, so FLOW_GPUS was 0 on a fresh install and a node
asking for one was silently clamped to zero and ran concurrently with every
other. Setting the variable serialised them, but it was an environment
variable only — `serve` had --max-runs and --max-workers and no --gpus.
The clamp warning now names the flag when nothing here declares a card.
The same flags are written into the environment before the settings are
built, so a value they refused died in a pydantic import naming no flag.
They are checked where they are typed instead.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019Hra4ndWMCLU5F3KjUuVAc
Slurm is not a machine that attaches and stays; it is a queue somebody else
owns. So nothing here submits a node to it. It submits a job whose payload is an
ordinary worker dialling back in, and everything downstream — the protocol, the
artifacts, cancellation, the books — already worked and did not have to learn
what Slurm is.
The alternative, which Covalent takes, is to stage a serialized call and a
runner onto the login node, poll squeue and copy the result back: a second way
of running a node beside the one that exists. The cost of not doing that is one
assumption, that a compute node can open a connection outward. Where that is
false, _payload is the single method a staged variant would replace.
Clusters are configured in provisioners.json beside the alerts, since this is
infrastructure an operator writes rather than anything a flow says. The script
is generated with the system ssh and no new dependency, and prerun owns the
environment — deliberately no pip install, because what is on a cluster is
somebody's decision.
One outstanding request per profile, cancelled if it never attaches and on the
way out. Nothing autoscales.
The run gate needed the same hook: a run held before it starts never reaches the
placer's own wait, so it would have queued forever on a machine nothing had
asked for.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01A6HeySA27EkGANZN95QySW
The engine answered "where does this node run" twice, in two ways that could
not see each other: a device sent it to a worker carrying that label, and
resources were counted against the engine's own cores. Declaring both meant the
second answer won and nothing was counted at all — which the data-science
getting-started page and the worked example both do.
One question now, in flow/placement.py: of every machine attached, which could
grant what this node asked for, and which of those has it free. The books move
onto each machine — one accountant per worker, built from the inventory it
reported — and the waiting moves above them, where one condition variable can
be woken by a release anywhere or by a worker attaching. Locks go one way:
placer, then a machine's books, never back.
So a node asking for a card now finds the box that has one, rather than being
clamped down to none and run here. When nothing can grant the ask at all it is
still cut down and run — a flow written on a cluster has to work on a laptop —
but the ceiling is one real machine now, since taking the largest of each
dimension separately can describe a machine nobody has.
Two things fixed on the way. A device on a connector node held every batch run
of its flow forever, waiting for a worker that could never run an entry point.
And `prefer` falling back to the engine skipped the books, so the fallback held
nothing.
The bench flow's node has taken a `params` argument that with_settings has not
forwarded for some time, so the benchmark could not run at all: 62 ms median
submit-to-result with this, against the 61 ms on record.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01A6HeySA27EkGANZN95QySW
stroblmeandClaude Opus 5