app/backend/README.md

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-worker on 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).