Melvin Strobl
29d3ec467f
- flow/nodes/device.py: `device` node with params `device` and `config`;
provides the device's `in` ports, requires its `out` ports without
waking anything, never run (Node.passive), down "Offline" from presence
- DeviceDef.io and DevicePort go; GET /devices/{id} computes `io` and
`bindings` from the device nodes of published flows, in a fixed order,
cached by FlowStore.revision; the credential's scope comes from them
- issues `unknown_device` and `device_port` (wired twice; against a report
the wrong direction or dtype, or unlisted as advisory), rechecked when a
device is created or deleted; ValidationIssue.advisory is a field
- a device publishing on a bound port is credited to its node by the
server; the panel endpoint kind is gone from `_endpoints` and the canvas
- a rebuild that moves a device's bindings sends `dashboard_changed` with
an empty name, which the firmware refetches on
- startup moves each device's `io` rows into device nodes in the flows
declaring their messages, draft and published, one commit; rows no
flow declares are logged and left out
- frontend: Device node settings (device, Publishes, Follows, a port
picker from a report), palette shortcuts per reported port, DeviceDetail
lists bindings with "Use in flow" in place of the I/O editor
- docs: devices, flow editor, node types, issues, API
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).