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stroblmeandClaude Fable 5 47e513b658 Bring the README back in step with the commands it gives

Four things had drifted. It told you to run `fluksio login` two lines above
saying `serve` signs you in — one of the two had to go, and it is the login.
It ran `fluksio sync` before `fluksio run`, which `run` now does itself. The
pipeline snippet passed `prepare` and `evaluate` to `Flow` without importing
them, so copying it got a NameError on the one example that matters. And the
site link was relative, which resolves to nothing on the page this file exists
to be — pypi.org.

Added one sentence, on installing into the environment you already work in,
because it is the reason a reader's own imports keep working and there is
nowhere else on this page they would learn it.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012ue1tkFWB1bcGy3aWhCKpU

2026-08-24 18:31:51 +02:00

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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 — serve signs you in itself and says where it put the token, so there is no login step. (fluksio login is for an engine somewhere else.)

Install it into the environment you already work in and your nodes run on that one, so everything you had imported is still importable. Fluksio keeps a SQLite database, a git repository of your flows and an artifact store in a .fluksio beside your code — one installation per project, found the way .git is.

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

That syncs your code and then runs it, so after an edit the command is the same one again.

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-worker on 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.

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Fluksio

For data science

Some other features

License

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