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Signed-off-by: stroblme <stroblme@posteo.de>
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stroblme
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# Fluksio
A node-based automation engine: flows, dashboards and batch runs, in one
resident process with no infrastructure behind it.
Fluksio is a node-based automation software that brings trust and reliability to your flow.
It just works and looks good.
Get started by running
```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.
and you're ready to go!
Fluksio keeps a SQLite database, a git repository of your flows and an artifact store under `~/.fluksio`, and prints an admin password once upon start.
## 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:
You can turn your existing data science project into a flow by decorating your functions with `@node` ...
```python
# myresearch/train.py
@@ -35,6 +33,8 @@ def fit(dataset, lr, epochs=25):
return {"weights": fluksio.save_artifact("weights.pt")}
```
... and passing them to a `Flow`:
```python
# myresearch/pipeline.py
from fluksio import Flow, Port
@@ -44,36 +44,30 @@ 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 access a dashboard as follows:
```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.
Checkout our [documentation](https://docs.fluksio.com/getting-started/data-science/) for more infos.
## What else it does
## Some other features
- **Flows** — typed messages between nodes, wired by name, edited on a canvas
- **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,
- **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
- **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
- **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 3.12 or newer, Linux or macOS.
Fluksio can also be used for facility automation.
Visit us on [Fluksio.com](fluksio.com) or go straight to our [documentation](https://docs.fluksio.com).
## License