Commit Graph
5 Commits
Author SHA1 Message Date
stroblme 34243e6902 SDK: artifacts work outside a node, run --follow prints status and queue waits, fluksio cancel
save_artifact/load_artifact outside a node use the worker's own store code in
$FLUKSIO_ARTIFACT_DIR or <tmp>/fluksio-artifacts, so a node can be called by
hand. --follow prints status changes and each node's resource wait; the
placer's waiting snapshot now carries the run. fluksio cancel <run>...
2026-10-28 08:07:35 +01:00
Melvin Strobl 1dc891aa1b A node's worker label is worker, so a device is only a paired one
- NodeDef `device`/`device_policy` become `worker`/`worker_policy`, the
  issue `ignored_device` becomes `ignored_worker`, and the SDK's `@node`
  takes `worker=` and `worker_policy=`
- FlowStore._migrate renames the keys in published and draft documents
  once, bumping their version; a queued run's `needs` reads the old key
- placement passes the label as `label`, client regenerated
- docs: worker placement wording, MCP issuer without DOMAIN, device rows in
  the CLI data directory and the API table, device links in compose comments
2026-10-16 16:56:05 +02:00
stroblme d423c47ca6 Socket snapshot carries the nodes waiting for a machine, so a reconnecting browser restores their queued dots 2026-10-14 23:31:54 +02:00
stroblme ee8637c02a Declare this machine's GPUs from serve, and refuse a bad limit as a flag
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.
2026-08-29 13:54:33 +02:00
stroblme 7743aa1a2b Schedule a node across every machine, not just this one
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.
2026-08-27 08:49:36 +02:00