stroblmeandClaude Fable 5
47e513b658
Four things had drifted. It told you to run `fluksio login` two lines above saying `serve` signs you in — one of the two had to go, and it is the login. It ran `fluksio sync` before `fluksio run`, which `run` now does itself. The pipeline snippet passed `prepare` and `evaluate` to `Flow` without importing them, so copying it got a NameError on the one example that matters. And the site link was relative, which resolves to nothing on the page this file exists to be — pypi.org. Added one sentence, on installing into the environment you already work in, because it is the reason a reader's own imports keep working and there is nowhere else on this page they would learn it. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012ue1tkFWB1bcGy3aWhCKpU
87 lines
3.0 KiB
Markdown
87 lines
3.0 KiB
Markdown
# Fluksio
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Fluksio is a node-based automation software that brings trust and reliability to your flow.
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It just works and looks good.
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Get started by running
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```sh
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pip install fluksio
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fluksio serve
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```
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and you're ready to go — `serve` signs you in itself and says where it put the
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token, so there is no login step. (`fluksio login` is for an engine somewhere
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else.)
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Install it into the environment you already work in and your nodes run on that
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one, so everything you had imported is still importable. Fluksio keeps a SQLite
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database, a git repository of your flows and an artifact store in a `.fluksio`
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beside your code — one installation per project, found the way `.git` is.
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## For data science
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You can turn your existing data science project into a flow by decorating your functions with `@node` ...
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```python
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# myresearch/train.py
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import fluksio
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from fluksio import Port, node
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@node(
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requires=["dataset", Port("lr", "float")],
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provides=[Port("loss", "float", stream=True), Port("weights", "artifact")],
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device="gpu", device_policy="prefer",
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)
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def fit(dataset, lr, epochs=25):
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for epoch in range(epochs):
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loss = step(...)
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yield {"loss": loss} # published as it happens, kept as a series
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return {"weights": fluksio.save_artifact("weights.pt")}
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```
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... and passing them to a `Flow`:
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```python
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# myresearch/pipeline.py
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from fluksio import Flow, Port
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from myresearch.data import prepare
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from myresearch.evaluate import evaluate
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from myresearch.train import fit
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train = Flow("train", nodes=[prepare, fit, evaluate],
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inputs=[Port("lr", "float", initial=0.01)], outputs=["score"])
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```
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Fluksio will automatically infer the order of nodes based on the inputs and outputs you defined.
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When everything is set, you can launch your first run as follows:
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```sh
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fluksio run train --lr 0.05 --wait
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```
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That syncs your code and then runs it, so after an edit the command is the same
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one again.
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Checkout our [documentation](https://docs.fluksio.com/getting-started/data-science/) for more infos.
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## Some other features
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- **Flows**: typed messages between nodes, wired by name, edited on a canvas
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or declared in code. Every change is a commit in a git repository you own.
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- **Runs**: an experiment and a CI-style job are the same entity. Parameters,
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seed, result, per-node timings, artifacts and the commit it ran at.
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- **Dashboards**: charts and controls bound to the same messages the flows
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carry, with no separate metrics pipeline.
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- **Remote workers**: `pip install fluksio-worker` on the GPU box; it dials
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*out* over one websocket, so nothing there has to be reachable.
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Fluksio can also be used for facility automation.
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Visit us on [fluksio.com](https://fluksio.com) or go straight to our [documentation](https://docs.fluksio.com).
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## License
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Copyright (C) 2026 Melvin Strobl — [GNU Affero General Public License v3.0 or
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later](https://www.gnu.org/licenses/agpl-3.0.en.html). Running a modified
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version over a network obliges you to offer its users the corresponding source
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(AGPL §13).
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