stroblmeandClaude Opus 5
40f8ad378d
Slurm is not a machine that attaches and stays; it is a queue somebody else owns. So nothing here submits a node to it. It submits a job whose payload is an ordinary worker dialling back in, and everything downstream — the protocol, the artifacts, cancellation, the books — already worked and did not have to learn what Slurm is. The alternative, which Covalent takes, is to stage a serialized call and a runner onto the login node, poll squeue and copy the result back: a second way of running a node beside the one that exists. The cost of not doing that is one assumption, that a compute node can open a connection outward. Where that is false, _payload is the single method a staged variant would replace. Clusters are configured in provisioners.json beside the alerts, since this is infrastructure an operator writes rather than anything a flow says. The script is generated with the system ssh and no new dependency, and prerun owns the environment — deliberately no pip install, because what is on a cluster is somebody's decision. One outstanding request per profile, cancelled if it never attaches and on the way out. Nothing autoscales. The run gate needed the same hook: a run held before it starts never reaches the placer's own wait, so it would have queued forever on a machine nothing had asked for. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6HeySA27EkGANZN95QySW
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).