From a512b4b394d1f55adeeb8fc9ca2b9a386d3bca79 Mon Sep 17 00:00:00 2001
From: Jiaming Yuan <jm.yuan@outlook.com>
Date: Thu, 16 Dec 2021 14:17:06 +0800
Subject: [PATCH] [doc] Promote dask from experimental. [skip ci] (#7509)

---
 doc/python/python_intro.rst | 15 +++++++++++++--
 doc/tutorials/dask.rst      |  7 +++----
 2 files changed, 16 insertions(+), 6 deletions(-)

diff --git a/doc/python/python_intro.rst b/doc/python/python_intro.rst
index e31f705ea..054598873 100644
--- a/doc/python/python_intro.rst
+++ b/doc/python/python_intro.rst
@@ -1,13 +1,23 @@
 ###########################
 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**
 
 * :doc:`/python/examples/index`
 * :doc:`Python API Reference <python_api>`
 
+**Contents**
+
+.. contents::
+  :backlinks: none
+  :local:
+
 Install XGBoost
 ---------------
 To install XGBoost, follow instructions in :doc:`/install`.
@@ -22,7 +32,8 @@ To verify your installation, run the following in Python:
 
 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
 - SciPy 2D sparse array
diff --git a/doc/tutorials/dask.rst b/doc/tutorials/dask.rst
index e27f95624..efe455b1e 100644
--- a/doc/tutorials/dask.rst
+++ b/doc/tutorials/dask.rst
@@ -3,11 +3,10 @@ Distributed XGBoost with Dask
 #############################
 
 `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
-workflows.  The implementation in XGBoost originates from `dask-xgboost
+easy management of distributed workers and excels at handling large distributed data
+science workflows.  The implementation in XGBoost originates from `dask-xgboost
 <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
-warnings) in the future.  The tutorial here focuses on basic usage of dask with CPU tree
+different interface.  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,
 Official Dask API for XGBoost
 <https://medium.com/rapids-ai/a-new-official-dask-api-for-xgboost-e8b10f3d1eb7>`_.