From e4a1cda0adf2432d6c7a9b6e9feafccc43246285 Mon Sep 17 00:00:00 2001
From: Richard Liaw <rliaw@berkeley.edu>
Date: Thu, 11 Jun 2020 11:29:16 -0700
Subject: [PATCH] [tune][hotfix] fix links (#8904)

---
 doc/source/tune.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/source/tune.rst b/doc/source/tune.rst
index c6da56c5d..3e1e450fc 100644
--- a/doc/source/tune.rst
+++ b/doc/source/tune.rst
@@ -11,7 +11,7 @@ Tune is a Python library for experiment execution and hyperparameter tuning at a
 
   * Launch a multi-node :ref:`distributed hyperparameter sweep <tune-distributed>` in less than 10 lines of code.
   * Supports any machine learning framework, :ref:`including PyTorch, XGBoost, MXNet, and Keras<tune-guides-overview>`.
-  * Automatically manages ref:`checkpoints <tune-checkpoint>`_ and logging to `TensorBoard <tune-logging>`__.
+  * Automatically manages :ref:`checkpoints <tune-checkpoint>` and logging to :ref:`TensorBoard <tune-logging>`.
   * Choose among state of the art algorithms such as :ref:`Population Based Training (PBT) <tune-scheduler-pbt>`, :ref:`BayesOptSearch <bayesopt>`, :ref:`HyperBand/ASHA <tune-scheduler-hyperband>`.
   * Move your models from training to serving on the same infrastructure with `Ray Serve`_.