stroblmeandClaude Opus 5
51464941ac
Docs / docs (push) Successful in 35s
Playwright Tests / test-playwright (1, 2) (push) Successful in 3m11s
Playwright Tests / test-playwright (2, 2) (push) Successful in 2m17s
pre-commit / pre-commit (push) Failing after 2m44s
Test Backend / test-backend (push) Successful in 3m0s
Compose Smoke Test / test-compose (push) Successful in 41s
Playwright Tests / merge-reports (push) Successful in 8m14s
`fluksio export metrics` is the long table — a row per run, metric and step —
and `fluksio export runs` the wide one, a row per run with the inputs that
*vary* across the selection as columns beside its final numbers, status,
duration and the commit and digest of the code it ran. Both carry the run id
on every row, which is the join back to the run page and what makes an
exported file auditable. `Client.export_metrics`/`export_runs` answer the same
rows to a notebook.
The engine streams csv or jsonl from two routes declared above `/{run_id}`;
parquet is a client-side conversion behind the new `fluksio[parquet]` extra,
so nobody pays for pyarrow who does not want dtypes kept. The long export
reads each run through `_series`, so a cached node's curve comes with it, and
`--stride` thins each series rather than the concatenation of all of them.
Two things they needed on the way: `GET /runs` takes `?since=` and `?before=`,
so a long history pages by the last row's own timestamp instead of an offset
that shifts under it; and a read that reaches no engine now says so in half a
second rather than seven, because `runs`, `flavors`, `export` and an unwatched
`status` pass `retries=0`. Everything that submits keeps them.
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).