# Fluksio

A node-based automation engine: flows, dashboards and batch runs, in one
resident process with no infrastructure behind it.

```sh
pip install fluksio
fluksio serve
```

That is the whole installation — no Docker, no database server, no ports to
open. It keeps a SQLite database, a git repository of your flows and an
artifact store under `~/.fluksio`, and prints an admin password once.

## For data science

Your functions become nodes where they already live. Install Fluksio into the
environment you work in and your nodes run on it — the packages are already
there:

```python
# 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")}
```

```python
# myresearch/pipeline.py
from fluksio import Flow, Port
from myresearch.train import fit

train = Flow("train", nodes=[prepare, fit, evaluate],
             inputs=[Port("lr", "float", initial=0.01)], outputs=["score"])
```

```sh
fluksio login --url http://127.0.0.1:8000
fluksio sync myresearch
fluksio run train --lr 0.05 --wait
```

The decorators return your functions untouched, so everything stays callable,
testable and importable as what it was. A metric leaves through a declared
port rather than a logging call, which is why there is no `log_metric()`: the
run keeps the whole series, a chart can bind to it, and a downstream node can
consume it.

## What else it does

- **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.

## Links

- Documentation: <https://docs.fluksio.com>
- Getting started (data science): <https://docs.fluksio.com/getting-started/data-science/>
- Home: <https://fluksio.com>

Python 3.10 or newer, Linux or macOS.