stroblmeandClaude Opus 5
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serve: refuse a second engine for one data directory whatever port it was asked for, using the pidfile and a token this directory signed. The check runs before the database is touched and before the credential is written, which is what left every later CLI call pointing at a dead port. The terminal dashboard is three tabs (Overview, Runs, Logs) with the toolbar following the focused pane, the engine's output goes to serve.log rather than down a pipe, and closing the screen stops both reader threads so the prompt comes back. It adopts a running engine on every start, so stop/start and restart work on one it did not start, and a stop waits for the process to be gone before the next start. Enrolment reports itself in the modal. enroll: a new claim code replaces the pairing instead of being refused. The code is redeemed before anything is written, mappings to a portal being left are cleared, and a running engine redials when the stored enrolment changes. runs: an engine re-queues the runs left `queued` by the one before it, and `fluksio retry <id>` / `retry --group <sweep>` submits an interrupted run again with the same inputs and group, recorded through Run.parent_id. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01U9BoNGq6V9MdRWAte7JBuC
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).