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Nov 25, 2024
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Nest markdown section headings below the main heading #511

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8 changes: 4 additions & 4 deletions notebooks/quickstart.ipynb
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Original file line number Diff line number Diff line change
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"id": "40c02f81-7e0d-44f3-8311-88867083f6f3",
"metadata": {},
"source": [
"# Storage\n",
"## Storage\n",
"\n",
"You've run you workflow and want to close your notebook for the day -- but you don't want to lose your data state. No problem! Just `.save()` your workflow. We'll go back and use our e-mail workflow as an example:"
]
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"id": "53d72537-6db7-4b42-b729-fa8a16cc5815",
"metadata": {},
"source": [
"# Failure recovery\n",
"## Failure recovery\n",
"\n",
"If a graph raises an exception, the default behaviour is to save (pickle) a copy of the node into its directoy under the filename \"recovery\". These can then be manually re-loaded to investigate the failure state:"
]
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"id": "4c7f5d84-d9f6-4eb5-9cf0-56494c53110c",
"metadata": {},
"source": [
"# Parallelization\n",
"## Parallelization\n",
"\n",
"`pyiron_workflow` actually splits apart data channels and the flow of data from \"signal\" channels and the flow of execution. This is important for while-loop flows, and if you want to learn more go check out the `deepdive.ipynb`. Most of the time, workflows form a Directed Acyclic Graph (DAG), and this execution flow can be completely automated -- you only need to define the flow of data.\n",
"\n",
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"id": "5a66e5fe-7fdc-4c31-93ff-b1e21e9dd86c",
"metadata": {},
"source": [
"# For-loops\n",
"## For-loops\n",
"\n",
"You can quickly iterate over node instances to get a `pandas.DataFrame` linking looped input to output (non-looped input is accessible elsewhere, like in the node's input channels). This comes in two flavours: nested loops with `iter`:"
]
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