stroblmeandClaude Opus 5
d4a9406c51
The gates have never gone green on the new runners. Three separate reasons: - backend/Dockerfile shipped Python 3.10 while the code imports typing.Self and datetime.UTC, so the container exited on import and the suite could not even load its conftest. The image moves to 3.13 and the packages declare >=3.12, which is the floor the tests actually pass on; ruff's target follows and rewrites timezone.utc and asyncio.TimeoutError accordingly. Relocking drops the 3.10 branch, which bumps FastAPI and so regenerates the SDK. - frontend/README.md had no trailing newline and two dashboard widgets used arbitrary text-[…] sizes. Both are em-relative on purpose, so they move to the inline style the neighbouring ramp already uses. - Every commit left its own run queued: without a concurrency group a runner that was offline for a while works through a backlog nobody reads. A stack that fails to come up now prints its logs before the teardown removes it. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Fluksio
A node-based automation engine: flows, dashboards and batch runs, in one resident process with no infrastructure behind it.
pip install fluksio
fluksio serve
That is the whole installation — no Docker, no database server, no ports to
open. It keeps a SQLite database, a git repository of your flows and an
artifact store under ~/.fluksio, and prints an admin password once.
For data science
Your functions become nodes where they already live. Install Fluksio into the environment you work in and your nodes run on it — the packages are already there:
# 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")}
# myresearch/pipeline.py
from fluksio import Flow, Port
from myresearch.train import fit
train = Flow("train", nodes=[prepare, fit, evaluate],
inputs=[Port("lr", "float", initial=0.01)], outputs=["score"])
fluksio login --url http://127.0.0.1:8000
fluksio sync myresearch
fluksio run train --lr 0.05 --wait
The decorators return your functions untouched, so everything stays callable,
testable and importable as what it was. A metric leaves through a declared
port rather than a logging call, which is why there is no log_metric(): the
run keeps the whole series, a chart can bind to it, and a downstream node can
consume it.
What else it does
- 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.
Links
- Documentation: https://docs.fluksio.com
- Getting started (data science): https://docs.fluksio.com/getting-started/data-science/
- Home: https://fluksio.com
Python 3.12 or newer, Linux or macOS.
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).