Melvin StroblandClaude Opus 5.5
62c0b915f3
- Volatile frames are held in memory with a byte-bounded queue instead of the /dev/shm ring scanned on every put; workers keep writing to the directory, which is trimmed on a timer. A volatile upload no longer spools to disk. ARTIFACT_VOLATILE_BYTES=0 turns the ring off. - A device sends camera frames as binary messages on its socket, held and published in one step and credited to its device node; refused frames are logged and dropped. Its snapshot carries demand and the uplink (binary, or http through the portal). - One demand registry for every socket's wanted media, the tunnel's included, plus what running flows read, mapped onto on-demand ports and published as device_demand; at most 8 media names per socket. - Per-message deflate off in serve and dev; a device may fetch the last 8 digests of a message; published dashboards cached per store revision; a revoked device socket is closed with 1008. - Media tiles want bytes only while on screen; the Devices tab previews a wired camera; the device node warns when a flow reads an on-demand port. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0138N7k5PT4dcR1iEqXaaQ9k
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).