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docs/source/how_to_guides/remote_files.md

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- You store the workflow results in remote storage to save and distribute them.
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pytask uses [universal-pathlib](https://github.com/fsspec/universal_pathlib) to work
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with remote files. The package provides a [`pathlib`](https://docs.python.org/3/library/pathlib.html)-like interface, making
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with remote files. The package provides a [`pathlib`][]-like interface, making
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it very easy to interact with files from an HTTP(S)-, Dropbox-, S3-, GCP-, Azure-based
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filesystem, and many more.
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docs/source/how_to_guides/writing_custom_nodes.md

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A typical task operation is to load data like a [`pandas.DataFrame`][]
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from a pickle file, transform it, and store it on disk. The usual way would be to use
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paths to point to inputs and outputs and call [`pandas.read_pickle`](https://pandas.pydata.org/docs/reference/api/pandas.read_pickle.html) and
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[`pandas.DataFrame.to_pickle`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_pickle.html).
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paths to point to inputs and outputs and call [`pandas.read_pickle`][] and
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[`pandas.DataFrame.to_pickle`][].
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```py
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--8<-- "docs_src/how_to_guides/writing_custom_nodes_example_1.py"
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[`pytask.PickleNode`](../reference_guides/api/nodes_and_tasks.md#pytask.PickleNode) that automatically
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loads and stores Python objects.
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And we pass the value to `df` via [`typing.Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) to preserve
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And we pass the value to `df` via [`typing.Annotated`][] to preserve
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the type hint.
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The result will be the following task.

docs/source/reference_guides/api/marks.md

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## Mark Classes
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::: pytask.Mark
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options:
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filters:
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\- "!^_\[^_\].*"
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\- "!^\_\_.*__$"
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show_root_heading: true
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show_signature: true
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show_root_heading: true
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show_signature: true
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::: pytask.mark
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::: pytask.MarkDecorator
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options:
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filters:
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\- "!^_\[^_\].\*"
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\- "!^__.*\_\_$"
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::: pytask.MarkGenerator
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options:
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filters:
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\- "!^_\[^_\].*"
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\- "!^__.\*__$"
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## Mark Utilities
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