Fluksio

Turn Python functions into connected nodes, run experiments, and keep their metrics and artifacts together. Start an engine with Python 3.12 or newer on Linux or macOS:

pip install fluksio
fluksio serve

The first start prints an admin password and opens a terminal dashboard. For a browser dashboard, connect the instance to the optional portal.

For data science

Save this small experiment as study.py. No dataset or GPU needed:

import math

from fluksio import Flow, Port, node

@node(
    requires=[Port("lr", "float")],
    provides=[Port("loss", "float", stream=True), Port("final_loss", "float")],
)
def fit(lr, epochs=25):
    for epoch in range(epochs):
        loss = math.exp(-lr * (epoch + 1))
        yield {"loss": loss}
    return {"final_loss": loss}


train = Flow(
    "train",
    nodes=[fit],
    inputs=[Port("lr", "float", initial=0.01)],
    outputs=["final_loss"],
)

Matching message names connect the nodes and determine their order. After syncing the project, start a run:

fluksio sync study
fluksio run train --lr 0.05 --wait

Each yield records a point on the loss curve. Replace the toy calculation with your own training code when you're ready. For artifacts and comparisons, try the data science guide.

More to explore

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.

For sensors, devices and live automation, try the facility automation guide.

2.3 KiB Raw Blame History

Fluksio

For data science

More to explore

License

2.3 KiB

Raw Blame History