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Two looks were somebody else's language spoken well, and neither was the product's. A dashboard nobody has dressed yet should look like the rest of the app, so there is now a third set that follows the root DESIGN-GUIDELINES.md to the letter — `--card` surfaces told from the page by a hairline and a low shadow rather than by colour, every control a pill, 16px panels, frosted floating chrome, one slate-blue accent spent on what a person can act on — and it is what `look` means when nothing says otherwise. That also turns the exemption the other way round. The dashboard is still allowed to look unlike the product; it just no longer does so by default. An existing dashboard, which has never named a look, lands on the design it had before any of this. Restraint is the style rather than an omission here: no ripple, no glow, no lift, and a press answered by the colour changing. The one deliberate departure is the selector, which holds its choice in `--primary` rather than the `--accent` the segmented rule asks for — that is a decision about the widget, not about the look, and a control must not change what it signals when the drawing changes. All three sets hold it the same way.
Fluksio
A node-based automation engine: flows, dashboards and batch runs, in one resident process with no infrastructure behind it.
pip install fluksio
fluksio serve
That is the whole installation — no Docker, no database server, no ports to
open. It keeps a SQLite database, a git repository of your flows and an
artifact store under ~/.fluksio, and prints an admin password once.
For data science
Your functions become nodes where they already live. Install Fluksio into the environment you work in and your nodes run on it — the packages are already there:
# 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")}
# myresearch/pipeline.py
from fluksio import Flow, Port
from myresearch.train import fit
train = Flow("train", nodes=[prepare, fit, evaluate],
inputs=[Port("lr", "float", initial=0.01)], outputs=["score"])
fluksio login --url http://127.0.0.1:8000
fluksio sync myresearch
fluksio run train --lr 0.05 --wait
The decorators return your functions untouched, so everything stays callable,
testable and importable as what it was. A metric leaves through a declared
port rather than a logging call, which is why there is no log_metric(): the
run keeps the whole series, a chart can bind to it, and a downstream node can
consume it.
What else it does
- 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.
Links
- Documentation: https://docs.fluksio.com
- Getting started (data science): https://docs.fluksio.com/getting-started/data-science/
- Home: https://fluksio.com
Python 3.10 or newer, Linux or macOS.
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).