stroblmeandClaude Opus 5
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Three things the first export pass got wrong for a real study. **Dotted paths.** A node returns a record, not a scalar — the numbers arrive inside `final_metrics` — so `--metrics final_metrics.train_loss` yielded an empty column and `--metrics final_metrics` yielded the whole record in one cell. Both sides of the wide table now take dotted paths, and the defaults reach the same depth: every number a result carries is a column named by its path, and inputs are compared leaf by leaf, so two configurations differing in one field give that field as the axis rather than two blobs that are merely not equal. Lists stay whole — a curve belongs in the long table. **`--list`.** Metric names are flow-qualified, so `--name train_loss` matched nothing and said only that. `fluksio export metrics --list` prints the names the selection carries, and an empty export made with `--name` points at it. **A version to compare.** The CLI ships ahead of the engine and a stale one answered a flat 404 with nothing anywhere in the API to tell how old it was. The engine reports `version` on `/observability/summary`, `fluksio status` prints it, and a 404 from export now names both versions — or says "older" when the field itself predates the engine. Bumped to 0.1.5, which is what makes the number worth reading. Also formats `flow/metrics.py`, which had been committed unformatted and was the last `ruff format --check` failure. 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).