stroblmeandClaude Opus 5
a9136c7811
The limit was applied in `apply_outputs`, which the executor reaches after the item is off the queue — so a subscriber told to publish every 15s still cost a queue entry, a `cascade_started`, a run record and a walk of everything reachable from it per inbound message. Seven relay nodes behind one inverter ran 192 times a minute to publish six. Two halves, matching the two shapes it takes: `trigger()` now keeps a value whose every port is inside its window and journals nothing at all. The window split came out of `_throttled` as a read-only `_window_split`, so the question is asked the same way in both places and the exact split is still made once, at claim time. A cascade carries the names it actually published, and the wave runs only the nodes something in that set feeds. A node whose triggering inputs were all held back is completed without running, which frees its own consumers to be judged the same way — the case where a node re-published 619 messages a minute off inputs that changed six times. Redeliveries and emissions carry no such set and still walk everything, since one has a half-finished wave to finish and the other is the value already being in state. Skipping a node can make one ready that the scheduling pass has already walked past, so `submit_ready` runs to a fixpoint. That also closes the same latent hole on the replay path, where a done-marker skip could strand a join with no future outstanding to come back for it. Measured with the new `scripts/bench_engine.py`, 500 messages through the house's shape: a limited source went from 500 cascades / 3500 node runs / 5009 events to 1 / 7 / 19, publishing the same 8 values; an unlimited source into limited relays took the node reading them from 500 runs to 1. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BpfSinyCBfjuieikyfMPbf
Fluksio
Fluksio is a node-based automation software that brings trust and reliability to your flow. It just works and looks good. Get started by running
pip install fluksio
fluksio serve
and you're ready to go.
For data science
You can turn your existing data science project into a flow by decorating your functions with @node ...
# myresearch/train.py
import fluksio
from fluksio import Port, node
@node(
requires=["dataset", Port("lr", "float")],
provides=[Port("loss", "float", stream=True), Port("weights", "artifact")],
device="gpu", device_policy="prefer",
)
def fit(dataset, lr, epochs=25):
for epoch in range(epochs):
loss = step(...)
yield {"loss": loss} # published as it happens, kept as a series
return {"weights": fluksio.save_artifact("weights.pt")}
... and passing them to a Flow:
# myresearch/pipeline.py
from fluksio import Flow, Port
from myresearch.data import prepare
from myresearch.evaluate import evaluate
from myresearch.train import fit
train = Flow("train", nodes=[prepare, fit, evaluate],
inputs=[Port("lr", "float", initial=0.01)], outputs=["score"])
Fluksio will automatically infer the order of nodes based on the inputs and outputs you defined. When everything is set, you can launch your first run as follows:
fluksio run train --lr 0.05 --wait
Checkout our documentation for more infos.
Some other features
- Flows: typed messages between nodes, wired by name, edited on a canvas or declared in code. Every change is a commit in a git repository you own.
- Runs: an experiment and a CI-style job are the same entity. Parameters, seed, result, per-node timings, artifacts and the commit it ran at.
- Dashboards: charts and controls bound to the same messages the flows carry, with no separate metrics pipeline.
- Remote workers:
pip install fluksio-workeron the GPU box; it dials out over one websocket, so nothing there has to be reachable.
Fluksio can also be used for facility automation. Visit us on fluksio.com or go straight to our documentation.
License
Copyright (C) 2026 Melvin Strobl - GNU Affero General Public License v3.0 or later. Running a modified version over a network obliges you to offer its users the corresponding source (AGPL §13).