stroblmeandClaude Opus 5
4a38c6ed31
Three faults with one root: the stored body of a code-defined node is an import shim, and nothing that mattered was ever read from the code itself. - The run stamp could not identify what ran. The shim imports whatever is on disk when the worker starts, and an uncommitted tree stamps <commit>-dirty for every run it ever produces. Run.code_digest hashes the repository's .py files, memoized on their stat state, and it is read again when the run is actually claimed -- so a sweep queued for hours records the code each of its runs executed, not the code that was there when it was submitted. - The stage cache adopted code that was too new. The fingerprint hashed the shim, which is invariant under any edit to the imported function or anything it calls into, so a re-run was served from cache and answered without the outputs the edit added. It now carries the repo digest and the node's declared ports. Every fingerprint changes once, which invalidates the existing cache; a canvas flow has no repository and keys as before. - An interrupted sync looked like a hand-edited canvas. The engine answers a new-node template for a node with no stored body, and the template carries no marker, so the drift check read "somebody edited this" and demanded --force -- for the one state that re-running the sync is the fix for. NodeSource.missing states the fact, and sync skips those and reuses the bodies it read instead of asking for each one twice. 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).