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removed python native API from documentation
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Signed-off-by: yes <[email protected]>
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tanwarsh committed Nov 14, 2024
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14 changes: 1 addition & 13 deletions docs/developer_guide/advanced_topics/overriding_agg_fn.rst
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Override Aggregation Function
*****************************

With the aggregator-based workflow, you can use custom aggregation functions for each task via Python\*\ API or command line interface.


Python API (Deprecated)
==========

1. Create an implementation of :class:`openfl.interface.aggregation_functions.core.AggregationFunction`.

2. In the ``override_config`` keyword argument of the :func:`openfl.native.run_experiment` native function, pass the implementation as a ``tasks.{task_name}.aggregation_type`` parameter.

.. note::
See `Federated PyTorch MNIST Tutorial <https://github.com/intel/openfl/blob/develop/openfl-tutorials/Federated_Pytorch_MNIST_custom_aggregation_Tutorial.ipynb>`_ for an example of the custom aggregation function.

With the aggregator-based workflow, you can use custom aggregation functions for each task via command line interface.

Command Line Interface
======================
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99 changes: 0 additions & 99 deletions docs/developer_guide/advanced_topics/overriding_plan_settings.rst

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15 changes: 1 addition & 14 deletions docs/developer_guide/utilities/splitters_data.rst
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Expand Up @@ -13,20 +13,7 @@ Dataset Splitters
You may apply data splitters differently depending on the |productName| workflow that you follow.


OPTION 1: Use **Native Python API** (Aggregator-Based Workflow) Functions to Split the Data (Deprecated)
===========================================================================================

Predefined |productName| data splitters functions are as follows:

- ``openfl.utilities.data_splitters.EqualNumPyDataSplitter`` (default)
- ``openfl.utilities.data_splitters.RandomNumPyDataSplitter``
- ``openfl.interface.aggregation_functions.LogNormalNumPyDataSplitter``, which assumes the ``data`` argument as ``np.ndarray`` of integers (labels)
- ``openfl.interface.aggregation_functions.DirichletNumPyDataSplitter``, which assumes the ``data`` argument as ``np.ndarray`` of integers (labels)

Alternatively, you can create an `implementation <https://github.com/intel/openfl/blob/develop/openfl/utilities/data_splitters/numpy.py>`_ of :class:`openfl.plugins.data_splitters.NumPyDataSplitter` and pass it to the :code:`FederatedDataset` function as either ``train_splitter`` or ``valid_splitter`` keyword argument.


OPTION 2: Use Dataset Splitters in your Shard Descriptor
OPTION 1: Use Dataset Splitters in your Shard Descriptor
========================================================

Apply one of previously mentioned splitting function on your data to perform a simulation.
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14 changes: 0 additions & 14 deletions docs/get_started/examples.rst
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:ref:`running_the_task_runner`

-------------------------
Python Native API (Deprecated)
-------------------------
Intended for quick simulation purposes

See :ref:`python_native_pytorch_mnist`

.. toctree::
:hidden:
:maxdepth: 1

examples/python_native_pytorch_mnist


-------------------------
Interactive API
-------------------------
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173 changes: 0 additions & 173 deletions docs/get_started/examples/python_native_pytorch_mnist.rst

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16 changes: 0 additions & 16 deletions docs/source/api/openfl_native.rst

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