Melvin Strobl
6a98dcd23f
A device reporting a `live` port streams its camera to the browser's own
RTCPeerConnection. The flows socket carries the signalling as text, so it
crosses the portal: `live` messages on a person's socket, `signal` on a
device's (start, sdp, candidate, bye, busy). The server routes by session,
holds a device to two viewers, ends a session when either socket closes or
no answer comes within 10 s, and gives each socket an outbox of its own
that ends a session rather than drop a message. TURN credentials are
minted per session in coturn's REST scheme (STUN_URLS, TURN_URLS,
TURN_SECRET), the viewer's with the device's offer; GET /devices/{id}/ice
hands them out too. The tunnel keeps a stream's early frames instead of
dropping them past eight and forgets them when the stream or link goes.
A media tile on a device's camera, and the Devices tab's preview, play
live when tapped, pause frames meanwhile, and fall back to frames with a
short note on busy, failure or no picture within 10 s. coturn is an
optional compose service behind the `turn` profile.
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).