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Three things the one-folder-per-study layout ran into. **Discovery walks down.** A plain directory is now walked all the way, so `fluksio sync dev` finds `dev/s1_baseline/study.py` and naming each study is no longer the price of the layout. Hidden directories, `__pycache__`, `node_modules` and virtualenvs are left alone, and a package is taken whole. Two files that would import under one module name are refused, naming both: Python keeps one module per name, so the second would silently *be* the first — and a node's generated body imports by that name, so a worker would run the wrong study's code. The message says the fix, which is an `__init__.py` per study directory. A module that raises while importing is now a sentence naming the file rather than an importlib traceback. **`run` and `sweep` sync downwards too**, so the flow is found from the repository root without the sync-then-`--no-sync` two-step. A study that will not import is a warning rather than a stopped run, since a walk meets every study and a half-finished one two directories away is not this run's problem. The upload was already a no-op for a flow nothing changed in, so what the walk costs is import time — `--sync PATH` narrows it, and skipping unchanged subtrees would need a cache keyed on file state that is deliberately not here. **`serve` moves off a busy default port** — 8001, 8002, up to twenty — says which it took, and writes that one into `client.json`. A port given with `--port` still fails when it is taken, because naming one is asking for it. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A9Hdrmf2cwNABCnE5x9UJa
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).