A python node's settings are constants of its own function, so they are passed
the way its ports are: by name. The controller binds them to the compiled
function, the `params` field is gone from the worker and remote protocols, and
a setting sharing a port's name is reported as a node error rather than
shadowing it. The panel's scaffold follows suit and keeps the header in step
with both ports and settings.
The demo's `pace` moves from a flow input to a setting of the training node,
which is what it always was.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NUb8YpL2s3gmN9WTACTt4q
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
A checkpoint is not a message. DType.ARTIFACT carries a reference — digest,
size, media type, name — so everything on the wire stays JSON and thirty
megabytes never sit in Redis, which answers the vision's open binary-payload
question by narrowing it: inline codecs would only serve payloads too small to
be worth a round trip, and nothing asks for that.
The store is content-addressed rather than per-run, for three reasons that all
pay later: a sweep whose fifty configs share one preprocessed input stores it
once, a reference stays valid however it is passed around because it names
content instead of a location, and the digest is what a stage cache will
compare — so building it in now is what keeps that from being a change to the
message contract.
Node code calls fluksio.save_artifact/load_artifact and cannot tell whether it
is writing the engine's own directory or putting bytes over HTTP, which is
what will let the same flow run on a remote worker unchanged.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
A training loop has numbers worth keeping thousands of steps before it has a
result. Node code now imports fluksio and calls log_metric/progress, which
sends a line back without ending the call; the engine writes those to
run_metric in batches from the run's own driver rather than folding them off
the event bus, which drops what it cannot keep up with.
Two things fall out. Each event resets the worker deadline, so a node's
timeout measures silence rather than duration — which is what lets a two-hour
training keep a liveness contract instead of racing it. And the worker pool's
_running is now keyed by (run, node), so cancelling one config of a sweep
kills that training and leaves the rest alone.
Fixes a latent framing bug: read_line returned whatever a read had taken,
which was fine while a worker only ever sent one line per request and
unparseable as soon as it sent several. It now keeps the remainder.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
The worker script is handed to the interpreter by path, so app/flow was
sys.path[0] for every node: `import queue` got the engine's. It now drops
its own directory before anything else imports, and runs with the
deployment's credentials scrubbed out of its environment.
Also: reload builds off the event loop, the pool wakes what is blocked on
it when it stops, a refused metrics flush is kept for the next one rather
than dropped, and the cascade events are paired through failures.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017MeiWk3Yq12n2pTvnQWYvt
User code no longer execs in the engine. A pool of persistent worker
subprocesses speaks one JSON object per line; the controller installs a
proxy as the node's function, so every execution path funnels through it
and the pipeline is untouched. A crash costs one subprocess, a per-node
timeout is a kill, and cancelling from the canvas is that same kill.
The workers run a venv of the user's own on the data volume, filled from
a pip manifest versioned beside the flows. Applying it retires the
workers and rebuilds, so a package lands without restarting the engine.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017MeiWk3Yq12n2pTvnQWYvt
stroblmeandClaude Opus 5