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[doc] Promote dask from experimental. [skip ci] (#7509)

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doc/python/python_intro.rst
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########################### ###########################
Python Package Introduction Python Package Introduction
########################### ###########################
This document gives a basic walkthrough of the xgboost package for Python.
This document gives a basic walkthrough of the xgboost package for Python. The Python
package is consisted of 3 different interfaces, including native interface, scikit-learn
interface and dask interface. For introduction to dask interface please see
:doc:`/tutorials/dask`.
**List of other Helpful Links** **List of other Helpful Links**
* :doc:`/python/examples/index` * :doc:`/python/examples/index`
* :doc:`Python API Reference <python_api>` * :doc:`Python API Reference <python_api>`
**Contents**
.. contents::
:backlinks: none
:local:
Install XGBoost Install XGBoost
--------------- ---------------
To install XGBoost, follow instructions in :doc:`/install`. To install XGBoost, follow instructions in :doc:`/install`.
@ -22,7 +32,8 @@ To verify your installation, run the following in Python:
Data Interface Data Interface
-------------- --------------
The XGBoost python module is able to load data from many types of different formats, including: The XGBoost python module is able to load data from many different types of data format,
including:
- NumPy 2D array - NumPy 2D array
- SciPy 2D sparse array - SciPy 2D sparse array

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doc/tutorials/dask.rst
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############################# #############################
`Dask <https://dask.org>`_ is a parallel computing library built on Python. Dask allows `Dask <https://dask.org>`_ is a parallel computing library built on Python. Dask allows
easy management of distributed workers and excels at handling large distributed data science easy management of distributed workers and excels at handling large distributed data
workflows. The implementation in XGBoost originates from `dask-xgboost science workflows. The implementation in XGBoost originates from `dask-xgboost
<https://github.com/dask/dask-xgboost>`_ with some extended functionalities and a <https://github.com/dask/dask-xgboost>`_ with some extended functionalities and a
different interface. Right now it is still under construction and may change (with proper different interface. The tutorial here focuses on basic usage of dask with CPU tree
warnings) in the future. The tutorial here focuses on basic usage of dask with CPU tree
algorithms. For an overview of GPU based training and internal workings, see `A New, algorithms. For an overview of GPU based training and internal workings, see `A New,
Official Dask API for XGBoost Official Dask API for XGBoost
<https://medium.com/rapids-ai/a-new-official-dask-api-for-xgboost-e8b10f3d1eb7>`_. <https://medium.com/rapids-ai/a-new-official-dask-api-for-xgboost-e8b10f3d1eb7>`_.