add anchors for olds links

docs/Advanced-Topics.rst

@@ -59,6 +59,8 @@ Parameters Tuning
 
 -  Refer to `Parameters Tuning <./Parameters-Tuning.rst>`__.
 
+.. _Parallel Learning:
+
 Distributed Learning
 --------------------
 

docs/Features.rst

@@ -72,6 +72,8 @@ It only needs to use some collective communication algorithms, like "All reduce"
 LightGBM implements state-of-art algorithms\ `[9] <#references>`__.
 These collective communication algorithms can provide much better performance than point-to-point communication.
 
+.. _Optimization in Parallel Learning:
+
 Optimization in Distributed Learning
 ------------------------------------
 

docs/Parallel-Learning-Guide.rst

@@ -1,6 +1,8 @@
 Distributed Learning Guide
 ==========================
 
+.. _Parallel Learning Guid:
+
 This guide describes distributed learning in LightGBM. Distributed learning allows the use of multiple machines to produce a single model.
 
 Follow the `Quick Start <./Quick-Start.rst>`__ to know how to use LightGBM first.
@@ -65,6 +67,8 @@ Kubeflow users can also use the `Kubeflow XGBoost Operator`_ for machine learnin
 
 Kubeflow integrations for LightGBM are not maintained by LightGBM's maintainers.
 
+.. _Build Parallel Version:
+
 LightGBM CLI
 ^^^^^^^^^^^^
 
@@ -99,6 +103,8 @@ Then write these IP in one file (assume ``mlist.txt``) like following:
 
 **Note**: For Windows users, need to start "smpd" to start MPI service. More details can be found `here`_.
 
+.. _Run Parallel Learning:
+
 Run Distributed Learning
 ''''''''''''''''''''''''