stroblmeandClaude Fable 5
99f6530698
Two things a local install should not have asked for. `fluksio serve` now signs you in. Logging in to your own machine was a formality — the password was printed by the same process that would have checked it, and the database it authenticates against sits in the directory the token goes into — so `serve` mints the token itself and says where it put it. `fluksio login` is left for an engine somewhere else. And an installation is `.fluksio` beside the code, found the way `.git` is, rather than one `~/.fluksio` for the machine. A repository with its own venv was already getting its own engine; it now gets its own flows, run history and token too, instead of three repositories sharing one database and fighting over one port. `--global` asks for the shared one, `--data-dir` still names any directory, and when both exist the banner says which you are looking at and how to reach the other. The directory ignores itself from within — a `.gitignore` of `*`, the way uv writes one into `.venv` — because it holds a credential and a database, and neither belongs in anybody's history. The token is written mode 600. A login an older version wrote to ~/.config/fluksio is still read, so nothing that worked stops working. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012ue1tkFWB1bcGy3aWhCKpU
86 lines
2.8 KiB
Markdown
86 lines
2.8 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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Then login once (the token is stored on your device) with the credentials shown after the previous command
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```sh
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fluksio login --url http://127.0.0.1:8000
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```
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and you're ready to go!
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Fluksio keeps a SQLite database, a git repository of your flows and an artifact
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store in a `.fluksio` beside your code — one installation per project, found
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the way `.git` is. It prints an admin password once, and signs you in itself.
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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.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 sync myresearch
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fluksio run train --lr 0.05 --wait
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```
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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](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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