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### What changes were proposed in this pull request? This PR proposes to port Koalas documentation to PySpark documentation as its initial step. It ports almost as is except these differences: - Renamed import from `databricks.koalas` to `pyspark.pandas`. - Renamed `to_koalas` -> `to_pandas_on_spark` - Renamed `(Series|DataFrame).koalas` -> `(Series|DataFrame).pandas_on_spark` - Added a `ps_` prefix in the RST file names of Koalas documentation Other then that, - Excluded `python/docs/build/html` in linter - Fixed GA dependency installataion ### Why are the changes needed? To document pandas APIs on Spark. ### Does this PR introduce _any_ user-facing change? Yes, it adds new documentations. ### How was this patch tested? Manually built the docs and checked the output. Closes #32726 from HyukjinKwon/SPARK-35587. Authored-by: Hyukjin Kwon <gurwls223@apache.org> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
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ReStructuredText
29 lines
723 B
ReStructuredText
.. _api.ml:
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==========================
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Machine Learning utilities
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==========================
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.. currentmodule:: pyspark.pandas.mlflow
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MLflow
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------
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Arbitrary MLflow models can be used with Koalas Dataframes,
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provided they implement the 'pyfunc' flavor. This is the case
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for most frameworks supported by MLflow (scikit-learn, pytorch,
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tensorflow, ...). See comprehensive examples in
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:func:`load_model` for more information.
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.. note::
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The MLflow package must be installed in order to use this module.
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If MLflow is not installed in your environment already, you
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can install it with the following command:
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**pip install koalas[mlflow]**
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.. autosummary::
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:toctree: api/
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PythonModelWrapper
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load_model
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