stroblmeandClaude Opus 5
831a537980
Both boilers on the house had been unable to switch on since the Node-RED transition, and the reason was here rather than in their logic: the command reached `boiler.water_boiler` and stopped, because `dmx.switches` never ran. A wave orders nodes by a dependency count, and two things decremented that count only on success: - a node that published nothing — rate limited, unchanged, or failed — never freed its consumers. `dmx.switches` reads both boilers through `rbe` nodes, so the kitchen one being unchanged, which it is nearly always, held the main one's command back. The encoder ran about four times an hour, and only when the lights happened to change in the same wave. - a node that could not run at all never freed them either, permanently. `plugs.pump_run` waits on a watering pulse that only exists at 02:00, so every wave it appeared in took its consumers out with it. Freeing a consumer is not the same as running it: `untouched` already refuses to run anything whose inputs nothing refreshed, and that is the accurate test. The dependency count is ordering, not permission. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01C5H4uLCCpsbipL1R7WKCee
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).