stroblmeandClaude Opus 5
5b122341d5
The site promises simulated inputs and mocked sensor values, and nothing in the app was that. A run already is: the values are the caller's, the state is the run's own namespace, and nothing it computes reaches the live flow. What was missing was a screen to do it from, and the draft flag being honoured. `/runs/new` is a flow, a field per declared input, a seed and Run; `/runs` stays the log. A comma-separated list in a number field expands into the grid `fluksio sweep --param` builds and goes to the sweep route, so launching one no longer needs a terminal. Only numbers split: a comma in a string is content, and one in JSON is syntax. `RunCreate.draft` was validated at submit and dropped before the run executed, so "try the working copy" ran the published one. `Run.draft` is a column now, the driver reads the same copy the submit checked, and a retry carries it. `FlowSummary.mode` came with it so the rail can say which flows are batch before one is picked. Also here: a Retry button on a finished run, which the route has always had and the UI never did, and parameter cells truncated to their column with the full value on hover. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_013TTfoK82awm8wvxXhHz3XF
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).