Deprecate Native API

docs/developer_guide/advanced_topics/overriding_agg_fn.rst

@@ -7,19 +7,7 @@
 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
-==========
-
-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
 ======================

docs/developer_guide/running_the_federation.notebook.rst

@@ -4,7 +4,7 @@
 .. _running_notebook:
 
 **********************************
-Aggregator-Based Workflow Tutorial
+Aggregator-Based Workflow Tutorial (Deprecated)
 **********************************
 
 You will start a Jupyter\* \  lab server and receive a URL you can use to access the tutorials. Jupyter notebooks are provided for PyTorch\* \  and TensorFlow\* \  that simulate a federation on a local machine.

docs/developer_guide/utilities/splitters_data.rst

@@ -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
-===========================================================================================
-
-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. 

docs/get_started/examples.rst

@@ -29,20 +29,6 @@ See :ref:`running_the_task_runner`
 
     :ref:`running_the_task_runner`
 
--------------------------
-Python Native API
--------------------------
-Intended for quick simulation purposes
-
-See :ref:`python_native_pytorch_mnist`
-
-.. toctree::
-    :hidden:
-    :maxdepth: 1
-
-    examples/python_native_pytorch_mnist
-
-
 -------------------------
 Interactive API
 -------------------------