stroblmeandClaude Opus 5
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A code node in a batch run is now fingerprinted by its source, its raw settings and the values it reads — an artifact input counting as its digest, which is what the content addressing was always for. A run that finds the key restores what the earlier one returned and skips the node, recorded as `cached`. The run history is the cache: `run_node.outputs` beside the `cache_key` the schema already had, no second store. On for code nodes, never for the built-in and connector types that have side effects; off per node with `@node(cache=False)` and per run with `--no-cache`. Emissions are not replayed on a hit, so a cached training node returns its result without redrawing its curve. Recorded in NOTEPAD.md with the two other deliberate limits. `fluksio run --local` boots the real app in the command's own process and drives it through its ASGI interface behind the ordinary client, so a run no longer needs a `serve` terminal beside it — same data directory, same history, and the cache carries between the two. It always waits, because the engine it starts lives exactly as long as the command. Also: `fluksio sweep --param lr=0.1,0.01` for the product of the lists, `run --follow` for a run's numbers as they arrive, Ctrl-C cancelling a waited run rather than abandoning it, coloured statuses on a terminal, and `name` made optional on the metrics endpoint so a follower can ask for every series. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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).