# 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-worker on 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.