stroblmeandClaude Opus 5.5
48aac3855b
The backend image now builds the SPA and serves it on the API's own origin (fluksio/spa.py), so an instance is a single container with no proxy, DNS, port 80 or .env: `docker compose up -d` with the new compose.yml runs it on localhost:8080. It starts on fluksio serve (persisted secret key, first admin printed once) and brings its own Redis when REDIS_HOST is unset. Slimmer: python:3.13-slim, no dev group, uv once, fastapi without the cloud CLI. Optional features are per-feature extras (mqtt, influx, email, sentry, ml, server for all) picked with the EXTRAS build argument; numpy moved to `ml`. ffmpeg left the image for imageio-ffmpeg via the Modules screen. The operator stack runs the same image with app. and api. routed to it; the nginx frontend service, the LAN nginx proxy and DOMAIN in the backend are gone, and the MCP issuer is FRONTEND_HOST. Co-Authored-By: Claude Opus 5.5 <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).