44 lines
1.3 KiB
Markdown
44 lines
1.3 KiB
Markdown
# Pick your starting point
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Choose the setup that fits your work.
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Both paths lead to the same Fluksio engine, with a first flow to try along the way.
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This is really just about a decision what you're planning to do with Fluksio.
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<div class="fluksio-lanes" markdown>
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<div class="fluksio-lane fluksio-lane--science" markdown>
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### Data science
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*"I have some Python code. I want to keep track of what I ran."*
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Install Fluksio with pip in your research environment. Turn a function into a
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node, record metrics and artifacts, and compare experiments. Connect to
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[fluksio.com](https://fluksio.com) for the browser dashboard.
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[Start with Python →](data-science.md)
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</div>
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<div class="fluksio-lane fluksio-lane--facility" markdown>
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### Facility automation
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*"I have a bunch of sensors. I want them to do something."*
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Start the Docker stack for a local engine and browser interface. Build a live
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flow, connect your devices, and put their values on a dashboard.
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[Start with Docker →](facility-automation.md)
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</div>
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</div>
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## Next steps
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- [Flows, nodes and messages](../concepts/flows.md) explains the graph.
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- [The flow editor](../interface/flow-editor.md) covers editing and publishing.
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- [The command line](../code/cli.md) covers local and remote use.
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- [Configuration](../reference/configuration.md) lists settings and data paths.
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