stroblmeandClaude Fable 5
99f6530698
Two things a local install should not have asked for. `fluksio serve` now signs you in. Logging in to your own machine was a formality — the password was printed by the same process that would have checked it, and the database it authenticates against sits in the directory the token goes into — so `serve` mints the token itself and says where it put it. `fluksio login` is left for an engine somewhere else. And an installation is `.fluksio` beside the code, found the way `.git` is, rather than one `~/.fluksio` for the machine. A repository with its own venv was already getting its own engine; it now gets its own flows, run history and token too, instead of three repositories sharing one database and fighting over one port. `--global` asks for the shared one, `--data-dir` still names any directory, and when both exist the banner says which you are looking at and how to reach the other. The directory ignores itself from within — a `.gitignore` of `*`, the way uv writes one into `.venv` — because it holds a credential and a database, and neither belongs in anybody's history. The token is written mode 600. A login an older version wrote to ~/.config/fluksio is still read, so nothing that worked stops working. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012ue1tkFWB1bcGy3aWhCKpU
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
Then login once (the token is stored on your device) with the credentials shown after the previous command
fluksio login --url http://127.0.0.1:8000
and you're ready to go!
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. It prints an admin password once, and signs you in itself.
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.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 sync myresearch
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).