Melvin Strobl
c604cfdf3b
- POST /devices/pair answers a 2-character `check`, listed by /devices/pending and shown beside each waiting entry; five codes a minute per address, and at 50 waiting the oldest gives way instead of a 429 - portal-minted device tokens are asked for with the device's nonce (`pnc`) and held against it, so unpair and delete revoke them; one without `pnc` stands only while the nonce is 0 - a device credential acts as the device (`device_principal`), not as the approving superuser, so deleting that account unpairs nothing - the report is validated port by port: unknown enum values read as `unknown`, unreadable ports and sections are left out and logged, `config` may name an entry of `config_schemas`; 422 only for no schema 1 or no object - devices.json is parsed once per change (cached by stat), delete drops the report and keeps the nonce, a busy recheck runs in the background - scope and io only from enabled flows (start/stop tells the devices), a port bound twice gives one io row - the Devices tab's saves cancel and hold back device refetches until settled
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).