stroblmeandClaude Opus 5
1f7c6646f1
A driver script died of one slow answer: httpx.ReadTimeout out of RunHandle.refresh() with a 30 s read timeout and no retry anywhere, which cost a sweep 78 of its 84 runs. - Split the timeout (5 s connect, 120 s read): a wrong URL fails at once, and a busy engine gets longer than the slowest thing it does on purpose (a 60 s compile, a 15 s rebuild wait). - Retry idempotent calls three times on a transport error or 502/503/504. 503 is the engine's own "ask again" — it is what RebuildBusy answers. - Submit carries a key the engine stores with the run, so a retry after a timeout returns that run instead of starting a second. A sweep keys every entry, so a half-created one recreates only what is missing. - wait() and --follow tolerate five failed polls in a row; a 404 still stops at once, because that is an answer rather than a gap. - CLI says "engine not answering" and names the run still on the engine, instead of printing a traceback. - runs: clamp the params column to 80 characters; events() takes the flow/since/until the endpoint already had; RunHandle.failures answers "what killed this run" from the run's own node rows. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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).