Melvin Strobl
72c1d73e96
C4: a device's `viewers` (superusers by default, or members) covers the image and video messages its ports publish. Anyone else gets none of their values on the socket, in the catalogue or a flow's state, 403 for their history, for the frames they hold (the last 8 included) and for TURN credentials, no bytes when asking for them, and `bye: forbidden` for a live view. A device credential stays bounded by its dashboards. The device page sets it in its Camera section, and a tile the viewer may not see says "No access to this camera" and asks the camera for nothing. C3: - A volatile upload is held to MAX_FRAME_BYTES (4 MiB, 413 above it, read without extra copies), and so is a frame on a device's socket. - A frame the engine holds in memory is written to the ring directory before a local worker is handed it, so load_artifact finds it. - A socket the bus dropped events for catches up with a snapshot, demand included, and a device's credential is checked again per frame, cached by the flow store revision and its nonce, so a lost event never keeps an unpaired device's frames coming. - A report is at most 64 KB once its named config_schemas are put in place. - A device away for 60 s has its true bool messages set to false. - Refused frames are logged by fixed keys, a frame is named after its message, `fluksio serve` waits 5 s for open connections, a tile with a stream URL creates no demand, the 8-name cap keeps the newest names. - Every container's log rotates (json-file, 3 x 10 MB).
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")],
worker="gpu", worker_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).