The first cut had node code call fluksio.log_metric, which was a second,
undeclared way for data to leave a node: invisible to validation, absent from
the canvas, and stored where the graph could not see it. That is precisely the
MLflow discrepancy this framework exists to avoid, so it is gone.
A node that produces values over time is a generator. Every yield is a dict
keyed by output port, published the instant it happens — same port, same type
check, same place on the canvas as any other value — and what it returns is
its result. A port doing this declares stream: true, and a run keeps every
number one takes, so experiment tracking is a consequence of the graph rather
than an API beside it: a chart binds to a training curve the way it binds to a
temperature. fluksio.emit writes the same ports imperatively, for where a
yield cannot reach — inside a training framework's callback.
In a live flow an emission also wakes what is downstream, as a subscriber
publishing does; in a run it does not, because a run's graph is scheduled once
and mid-node cascades would leave 'finished' with nothing to mean. The
enqueued item carries no payload: the value is already in state, and one
carrying it would re-apply an old emission after the node returned.
Verified on the stack: 30 loss values arrived live on the flow socket during a
run, attributed to the node that produced them, and the same node run on the
remote worker streamed its curve back across the socket.
Also caches remote compile results per worker, so attaching a GPU box does not
put a network round trip in every rebuild.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
stroblmeandClaude Fable 5
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